Happening @ Michigan https://events.umich.edu/list/rss RSS Feed for Happening @ Michigan Events at the University of Michigan. 2020 ICPSR Virtual Data Fair (September 25, 2020 9:00am) https://events.umich.edu/event/68492 68492-17088491@events.umich.edu Event Begins: Friday, September 25, 2020 9:00am
Location: Off Campus Location
Organized By: Institute for Social Research

Join us for the 2020 ICPSR Data Fair, a series of webinars taking place September 21-25, 2020. Data is in the news at a dizzying rate, reminding us that our choices in collecting and sharing data are of great consequence.

At the Data Fair you'll learn from thought leaders who will delve into important topics like:
• data transparency
• data activism
• data in the community
• what to do with data
and more!

All for free, all virtual, and all open to the public.

Please direct any questions to Annalee Shelton, annalees@umich.edu.

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Fair / Festival Wed, 16 Oct 2019 14:58:31 -0400 2020-09-25T09:00:00-04:00 2020-09-25T17:00:00-04:00 Off Campus Location Institute for Social Research Fair / Festival ICPSR logo
ICPSR's COVID 19 Data Archive (September 25, 2020 11:00am) https://events.umich.edu/event/76472 76472-19717164@events.umich.edu Event Begins: Friday, September 25, 2020 11:00am
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

Learn about the new COVID-19 Data Repository, a repository for data examining the social, behavioral, public health, and economic impact of the novel coronavirus global pandemic (https://www.openicpsr.org/openicpsr/covid19). Dr. Amy Pienta, Research Scientist and Director of ICPSR's Business and Collection Development, will discuss ICPSR's role in writing international guidelines for sharing COVID-19 data. Senior Data Project Manager Chelsea Goforth will discuss why this archive is important, what you'll find, some ways this data might be used, and how you can contribute. Screen reader support enabled.

This webinar is part of the 2020 ICPSR Data Fair, "Data in Real Life." More information about the Data Fair can be found at http://myumi.ch/ICPSRdatafair2020. Please note that all attendees for this session must be registered for the ICPSR Data Fair.

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Presentation Tue, 01 Sep 2020 11:32:24 -0400 2020-09-25T11:00:00-04:00 2020-09-25T12:00:00-04:00 Off Campus Location Inter-university Consortium for Political and Social Research Presentation ICPSR's COVID 19 Data Archive
Challenges for Census 2020 The impact on data quality - ICPSR Data Fair 2020 (September 25, 2020 12:00pm) https://events.umich.edu/event/77144 77144-19798545@events.umich.edu Event Begins: Friday, September 25, 2020 12:00pm
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

Join us for a conversation regarding the 2020 Census. Speakers will include two Chief Statisticians of the United States (emeritae), an expert on the development of statistical data systems (particularly the census), and a demographer who has experienced the community impact of the census. Topics will include mail delays at the USPS, political appointees, COVID-19, and other factors affecting the 2020 Census.

Moderated by Katherine Wallman, former Chief Statistician at the United States

This webinar is part of the 2020 ICPSR Data Fair, "Data in Real Life." More information about the Data Fair can be found at http://myumi.ch/ICPSRdatafair2020. Please note that all attendees for this session must be registered for the ICPSR Data Fair.

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Presentation Fri, 18 Sep 2020 08:51:54 -0400 2020-09-25T12:00:00-04:00 2020-09-25T13:00:00-04:00 Off Campus Location Inter-university Consortium for Political and Social Research Presentation Challenges for Census 2020 The impact on data quality - ICPSR Data Fair 2020
Getting to Know the ISR Summer Institute in Survey Research Techniques (September 25, 2020 1:00pm) https://events.umich.edu/event/76475 76475-19719130@events.umich.edu Event Begins: Friday, September 25, 2020 1:00pm
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

Summer Institute faculty and staff will first provide a brief history of the Summer Institute, and then turn to a discussion of the program. Topics include many aspects of survey research including the fundamental principles involved in drawing samples, designing questionnaires, data collection, and design-based analysis of survey data. The SRC Summer Institute is unique in comparison to the ICPSR Summer Program in terms of its focus on the process of research design and data collection (as opposed to analyzing data that have already been collected).

This webinar is part of the 2020 ICPSR Data Fair, "Data in Real Life." More information about the Data Fair can be found at http://myumi.ch/ICPSRdatafair2020. Please note that all attendees for this session must be registered for the ICPSR Data Fair.

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Presentation Tue, 01 Sep 2020 12:02:58 -0400 2020-09-25T13:00:00-04:00 2020-09-25T14:00:00-04:00 Off Campus Location Inter-university Consortium for Political and Social Research Presentation Getting to Know the ISR Summer Institute in Survey Research Techniques
Conceptualizing and Visualizing Conflict Data with Shiny (September 25, 2020 2:00pm) https://events.umich.edu/event/76479 76479-19719134@events.umich.edu Event Begins: Friday, September 25, 2020 2:00pm
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

Join Drs. Dave Armstrong and Christian Davenport for a real-world example of data visualization using Shiny. They discuss conceptualization and measurement of conflict in quantitative data and demonstrate how to produce graphics to convey their findings.

This webinar is part of the 2020 ICPSR Data Fair, "Data in Real Life." More information about the Data Fair can be found at http://myumi.ch/ICPSRdatafair2020. Please note that all attendees for this session must be registered for the ICPSR Data Fair.

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Presentation Tue, 15 Sep 2020 11:16:57 -0400 2020-09-25T14:00:00-04:00 2020-09-25T15:00:00-04:00 Off Campus Location Inter-university Consortium for Political and Social Research Presentation Conceptualizing and Visualizing Conflict Data with Shiny
Measuring a Liberal Education and its Relationship with Labor Market Outcomes: An Exploratory Analysis (October 2, 2020 2:00pm) https://events.umich.edu/event/77737 77737-19909792@events.umich.edu Event Begins: Friday, October 2, 2020 2:00pm
Location: Off Campus Location
Organized By: Education Policy Initiative

In an exploratory project funded by The Andrew W. Mellon Foundation, Ithaka S+R developed a novel approach to measuring a liberal arts and sciences educational experience, and examining its relationship with student outcomes. They will present their framework for defining and capturing the core features of a liberal arts and sciences educational experience, their index for measuring the degree to which a subset of higher education institutions in the US have offered those features to their students, and their institution-level analyses examining the relationship between index scores and students’ short-term academic and long-term labor market outcomes.

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Workshop / Seminar Fri, 25 Sep 2020 12:45:03 -0400 2020-10-02T14:00:00-04:00 2020-10-02T15:30:00-04:00 Off Campus Location Education Policy Initiative Workshop / Seminar UM LS&A Image
Hands-on Workshop: Creating a Hybrid Simulation System Using the Simple Run Time Infrastructure Software (October 9, 2020 4:00pm) https://events.umich.edu/event/76684 76684-19735053@events.umich.edu Event Begins: Friday, October 9, 2020 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Computational Discovery and Engineering

The goal of this hands-on workshop is to introduce the Simple Run-Time Infrastructure software toolkit (SRTI) to the participants, and provide a template project consisting of multiple simulators, each with a specialized purpose, relating to a natural-disaster scenario. It will take place after the feature talks.

The SRTI is a free, open-source solution developed at the University of Michigan, and enables researchers to connect computer programs and simulators written in different languages, to share data during execution, and to design hybrid systems using disparate simulator modules, with a primary goal of being user friendly. This hands-on workshop will explain what the SRTI is, and provide an example on how to use it.

The Java Runtime Environment (JRE) is required to run the SRTI. Please install it prior to the workshop. Refer to icor.engin.umich.edu for more information on supported operating systems and languages. Participants will need to use their own computer systems at home to take part. Basic coding skills in any programming language are required.

Open to the general public. Please register if you wish to participate.

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Workshop / Seminar Thu, 03 Sep 2020 16:10:17 -0400 2020-10-09T16:00:00-04:00 2020-10-09T18:00:00-04:00 Off Campus Location Michigan Institute for Computational Discovery and Engineering Workshop / Seminar Creating a Hybrid Simulation System Using the Simple Run Time Infrastructure Software
The Evolution of Basketball with Data Science (October 12, 2020 4:00pm) https://events.umich.edu/event/78271 78271-20002854@events.umich.edu Event Begins: Monday, October 12, 2020 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

For the last couple of decades, most industries have grown to take advantage of the information gained from data collection. As that happened, professional sports teams started to catch on. Baseball took the lead thanks to the amount of data collected over the years, which dates to the 1800s, but a lot of other professional sports followed and put more attention to their data collection. With technological advancements, particularly high-speed cameras, storage capacities and image recognition, more dynamic sports started to collect richer and richer data. The insights derived from this data started shifting the way the game is played and the way players are evaluated. This talk will take you through the evolution of data science in basketball and give examples of how data is shifting the way teams make decisions on and off the court.

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Presentation Wed, 07 Oct 2020 09:55:02 -0400 2020-10-12T16:00:00-04:00 2020-10-12T17:00:00-04:00 Off Campus Location Michigan Institute for Data Science Presentation https://umich.zoom.us/j/94496488704
Literacy Among American Indians: Levels and Trends from 1900 to 1930 and Across Birth Cohorts from 1830 to 1920 (October 19, 2020 12:00pm) https://events.umich.edu/event/77313 77313-19838094@events.umich.edu Event Begins: Monday, October 19, 2020 12:00pm
Location: Off Campus Location
Organized By: Institute for Social Research

Contact PSC Office for Zoom details.

We investigate levels and trends in literacy among American Indians in the United States. Using 1900-1930 decennial census data, we document literacy for the 1900 through 1930 period and for birth cohorts from 1830 through 1920. We thus provide for American Indians a large-scale picture of the history of literacy. We document the pace and extent of Indian literacy from very low for the birth cohorts of the early 1800s to fairly universal for the cohorts of the early 1900s. We also demonstrate that the increases in Indian literacy were closely related to birth cohort, with successive new birth cohorts having higher levels of literacy. We found little evidence that increases in literacy from 1900 to 1930 happened because adults increased their literacy after the school years and as they matured across the adult life course. We also document important gender differences in Native American literacy, with the proportion literate being lower for women than for men, but with the gender gap decreasing in later birth cohorts. There were also substantial literacy inequalities across geographical regions of the country-ranging from 19 to 74 percent literate across regions in 1900. The trajectories of literacy attainment also varied across regions in interesting ways. We also document that Indian literacy was higher among those living in urban areas, those more integrated into the Euro-American community, and those with Euro-American ancestry.

https://ssai.isr.umich.edu/

Contact PSC Office for Zoom details.


BIO:
Arland Thornton is Professor of Sociology, Population Studies, and Survey Research at the University of Michigan, where he is also associated with the , Native American Studies Program and several Centers within the International Institute. He is a social demographer who has served as president of the Population Association of America and previously held a MERIT award from the National Institutes of Health. He has received four awards for his books as well as distinguished career awards from the American Sociological Association and the Population Association of America. Thornton has focused much of his career on the study of family and demographic issues, with emphasis on marriage, cohabitation, childbearing, gender roles, education, and migration. Thornton has also pioneered the study of developmental idealism, including its conceptualization, measurement, and influence in many places. He has collaborated in the collection and analysis of data from Albania, Argentina, Bulgaria, China, Egypt, Hungary, Iran, Iraq, Lebanon, Malawi, Nepal, Romania, Saudi Arabia, Taiwan, Turkey, the U.S., and Vietnam. Thornton is currently conducting research concerning American Indians, with a particular focus on levels and trends in schools, school enrollment, and literacy.


Linda Young-DeMarco is a Lead Research Area Specialist with extensive longitudinal research project management experience. Her expertise includes project conceptualization, construct and measurement development, design and preparation of open ended survey materials, survey questionnaire design, interviewer training, design, implementation, and supervision of data management activities, design and direction of archival activities, contributions to the conceptualization of data analyses, design and execution of data analyses, and collaboration in the authorship of substantive peer-reviewed research papers and book chapters. She has been project manager and collaborator with researchers at the University of Michigan's Institute for Social Research on numerous international research projects that focus on development and people's ideational beliefs concerning development around the world.

PSC Brown Bag seminars highlight recent research in population studies and serve as a focal point for building our research community.

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Workshop / Seminar Wed, 28 Apr 2021 12:53:04 -0400 2020-10-19T12:00:00-04:00 2020-10-19T13:00:00-04:00 Off Campus Location Institute for Social Research Workshop / Seminar Flyer for Brown Bag seminar
Towards an Artificial Intuition: Conversational Markers of (Anti)Social Dynamics (October 19, 2020 4:00pm) https://events.umich.edu/event/78274 78274-20002858@events.umich.edu Event Begins: Monday, October 19, 2020 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Can conversational dynamics—the nature of the back and forth between people—predict outcomes of social interactions? This talk will describe efforts on developing an artificial intuition about ongoing conversations, by modeling the subtle pragmatic and rhetorical choices of the participants.
The resulting framework distills emerging conversational patterns that can point to the nature of the social relation between interlocutors, as well as to the future trajectory of this relation. For example, I will discuss how interactional dynamics can be used to foretell whether an online conversation will stay on track or eventually derail into personal attacks, providing community moderators several hours of prior notice before an anti-social event is likely to occur.
The data and code are available through the Cornell Conversational Analysis Toolkit (ConvoKit): http://convokit.cornell.edu
This talk includes joint work with Jonathan P. Chang, Lucas Dixon, Liye Fu, Yiqing Hua, Dan Jurafsky, Lillian Lee, Jure Leskovec, Vlad Niculae, Chris Potts, Arthur Spirling, Dario Taraborelli, Nithum Thain, and Justine Zhang.

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Presentation Wed, 07 Oct 2020 10:03:41 -0400 2020-10-19T16:00:00-04:00 2020-10-19T17:00:00-04:00 Off Campus Location Michigan Institute for Data Science Presentation https://umich.zoom.us/j/95443347994
CSCS/MIDAS/MICDE Seminar | Predicting the second wave of COVID-19 in Washtenaw County, MI (October 20, 2020 11:30am) https://events.umich.edu/event/76629 76629-19733025@events.umich.edu Event Begins: Tuesday, October 20, 2020 11:30am
Location: Off Campus Location
Organized By: The Center for the Study of Complex Systems

This seminar is co-sponsored by the Michigan Institute for Computational Discovery & Engineering (MICDE) and the Michigan Institute for Data Science (MIDAS)

VIRTUAL SEMINAR LINK: myumi.ch/v2ZYv

In this work, we study and predict the spread of COVID-19 in Washtenaw County, MI through applying a discrete and stochastic network-based modeling framework. In this framework, we construct contact networks based on synthetic population datasets specific for Washtenaw County that are derived from US Census datasets. We assign individuals to households, workplaces, schools, and group quarters (such as prisons or long term care facilities). In addition, we assign casual contacts to each individual at random. Using this framework, we explicitly simulate Michigan-specific government-mandated workplace and school closures as well as social distancing measures. We perform sensitivity analyses to identify key model parameters and mechanisms contributing to the observed disease burden in the three months following the first observed cases of COVID-19 in Michigan. We then consider several scenarios for relaxing restrictions and reopening workplaces to predict what actions would be most prudent. In particular, we consider the effects of 1) different timings for reopening, and 2) different levels of workplace vs. casual contact re-engagement. Through simulations and sensitivity analyses, we explore mechanisms driving the magnitude and timing of a second wave of infections upon re-opening.

This work is based on Dr. Renardy's *paper in press* in the *Journal of Theoretical Biology* with coauthors:
Marisa Eisenberg, UM Complex Systems & Math (LSA) and Epidemiology (Public Health)
Denise Kirschner, UM Department of Microbiology & Immunology (Medical School)

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Livestream / Virtual Mon, 28 Sep 2020 14:00:42 -0400 2020-10-20T11:30:00-04:00 2020-10-20T13:00:00-04:00 Off Campus Location The Center for the Study of Complex Systems Livestream / Virtual Photo of Marissa Renardy
Data Science Coast to Coast Presents: Talitha Washington (October 21, 2020 3:00pm) https://events.umich.edu/event/78280 78280-20002864@events.umich.edu Event Begins: Wednesday, October 21, 2020 3:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

The DS C2C seminar series, hosted jointly by six academic data science institutes, provides a unique opportunity to foster a broad-reaching data science community.

Speakers include faculty members and postdoctoral fellows at the six institutes whose research spans the theory and methodology of data science, and their application in arts and humanities, engineering, biomedical, natural, physical and social sciences.

In addition, the series features some of the most important figures in data science, who will provide insight on the transformative use of data science in traditional research disciplines, future breakthroughs in data science research, data science entrepreneurship, and advocacy and national policies for a data-enabled and just society.

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Presentation Wed, 07 Oct 2020 11:23:52 -0400 2020-10-21T15:00:00-04:00 2020-10-21T17:00:00-04:00 Off Campus Location Michigan Institute for Data Science Presentation https://umich.zoom.us/j/93769972428
Fair Ranking with Biased Data (October 26, 2020 4:00pm) https://events.umich.edu/event/78276 78276-20002859@events.umich.edu Event Begins: Monday, October 26, 2020 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Search engines and recommender systems have become the dominant matchmaker for a wide range of human endeavors — from online retail to finding romantic partners. Consequently, they carry immense power in shaping markets and allocating opportunity to the participants. In this talk, I will discuss how the machine learning algorithms underlying these systems can produce unfair ranking policies for both exogenous and endogenous reasons. Exogenous reasons often manifest themselves as biases in the training data, which then get reflected in the learned ranking policy and lead to rich-get-richer dynamics. But even when trained with unbiased data, reasons endogenous to the algorithms can lead to unfair or undesirable allocation of opportunity. To overcome these challenges, I will present new machine learning algorithms that directly address both endogenous and exogenous unfairness.

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Presentation Wed, 07 Oct 2020 10:09:21 -0400 2020-10-26T16:00:00-04:00 2020-10-26T17:00:00-04:00 Off Campus Location Michigan Institute for Data Science Presentation https://umich.zoom.us/j/93790126046
Auditing for Bias in Resume Search Engines (November 2, 2020 4:00pm) https://events.umich.edu/event/78328 78328-20010766@events.umich.edu Event Begins: Monday, November 2, 2020 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

There is growing awareness and concern about the role of automation in hiring, and the potential for these tools to reinforce historic inequalities in the labor market. I will present the results of an algorithm audit of the resume search engines offered by several of the largest online hiring platforms, to understand the relationship between a candidate’s gender and their rank in search results. We audited these platform with respect to individual and group fairness, as well as indirect and direct discrimination. I conclude with a brief discussion of the social and policy implications of our study.

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Presentation Thu, 08 Oct 2020 09:17:55 -0400 2020-11-02T16:00:00-05:00 2020-11-02T17:00:00-05:00 Off Campus Location Michigan Institute for Data Science Presentation https://umich.zoom.us/j/95382333953
U-M Data Science Annual Symposium 2020 (November 10, 2020 9:00am) https://events.umich.edu/event/75640 75640-19552851@events.umich.edu Event Begins: Tuesday, November 10, 2020 9:00am
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Fully virtual. November 10th-11th

Keynote Speakers:
CATHERINE D’IGNAZIO
Assistant Professor, Urban Science & Planning
Director, Data + Feminism Lab
Department of Urban Studies & Planning, MIT

LAUREN KLEIN
Associate Professor, English, Quantitative Theory and Methods
Emory University

CALL FOR PRESENTATIONS
The Michigan Institute for Data Science (MIDAS) invites submission of 1) abstracts for presentations and 2) proposals for workshops, for the 2020 U-M Data Science Symposium.

As the focal point of data science at U-M, MIDAS facilitates the work of the broad U-M data science community, advances cross-cutting data science methodologies and applications, promotes the use of data science to benefit society, builds data science training pipelines, and develops partnerships with industry, academia and community. The annual symposium showcases the breadth and depth of U-M data science, shares research ideas that will lead to the next breakthroughs, and builds collaboration.

Presentations at the symposium should cover one or more of the following areas of data science:

Theoretical foundations
Methodology and tools
Real-world application in any domain
The ethics and societal impact of data science
Emerging areas of data science
WE INVITE SUBMISSIONS FOR THE FOLLOWING:
1. Proposals for mini-workshops. New this year, the symposium will include 3-5 mini-workshops on the afternoon of Nov. 10 as parallel sessions. Each workshop will be two hours long and for 50-100 attendees. They can be research discussion sessions, tutorials or hack sessions. Proposals should include the theme, format, organizer and potential presenters, as well as how the proposed mini-workshop brings out the strengths across multiple U-M research units and its benefit to U-M data science research and/or to the larger community. If your theme is selected, the symposium program committee will discuss with you further to help finalize the plan, and MIDAS will provide logistics support.

Some examples of possible themes: Mobilizing data science for crisis response; Data preparation for multi-party computing; Introduction of data science to attendees from non-profit organizations; Data science for wearables/mobile health.

If you would like to discuss your mini-workshop idea with the symposium committee before submission, please email Jing Liu, MIDAS Managing Director (ljing@umich.edu),

2. Abstracts for Research Talks (20 minutes including Q&A). The talks should discuss exciting research ideas, provide vision and context for challenging data science questions, stimulate discussions, and lay out collaboration opportunities. These talks should not simply be technical reports of projects.

3. Abstracts for Posters. The Posters can be used as technical reports of projects. Posters with students as first authors will be automatically entered in the poster competition.

DEADLINES:
Mini-workshop proposal submission: 11:59 pm, July 31, 2020; notification: Aug. 14, 2020
Talks and posters abstract submission: 11:59 pm, Sept. 18, 2020; notification: Oct. 9, 2020

SUBMISSION INSTRUCTIONS:
At least one author/presenter should have a U-M affiliation.
Please do not include figures, tables or bibliography in the abstract.
To submit proposals for mini-workshops:
Please include a title, list of organizers/potential presenters and their affiliations.
The main body of the submission should be no more than 300 words.
Please include the theme, format, how it features the strengths from multiple U-M research units, and its impact.
To submit abstracts for research talks and posters:
Please include a title, list of authors/presenters and their affiliations.
The main body of the submission should be no more than 300 words.
For research talks, please include a brief summary of the research idea and its context, potential methods and impact, and how it can benefit from collaboration.
For posters, please include a brief summary of the research, methods, main results and impact.
For questions, please contact midas-research@umich.edu.

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Conference / Symposium Tue, 13 Oct 2020 10:29:30 -0400 2020-11-10T09:00:00-05:00 2020-11-10T17:00:00-05:00 Off Campus Location Michigan Institute for Data Science Conference / Symposium U-M Data Science Annual Symposium 2020
The Testing Paradox for COVID-19 (November 10, 2020 10:10am) https://events.umich.edu/event/79203 79203-20231444@events.umich.edu Event Begins: Tuesday, November 10, 2020 10:10am
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Reported case-counts for coronavirus are wrinkled with data errors, namely misclassification of the tests and selection bias associated with who got tested. The number of covert or unascertained infections is large across the world. How can one determine optimal testing strategies with such imperfect data? In this talk, we propose an optimization algorithm for allocating diagnostic/surveillance tests when your objective is estimating the true population prevalence or detecting an outbreak. Infectious disease models and survey sampling techniques are used jointly to come up with these strategies

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Presentation Thu, 05 Nov 2020 09:29:08 -0500 2020-11-10T10:10:00-05:00 2020-11-10T10:30:00-05:00 Off Campus Location Michigan Institute for Data Science Presentation Professor Bhramar Muherjee
Students’ mobility patterns on campus and the implications for the recovery of campus activities post-pandemic (November 10, 2020 10:30am) https://events.umich.edu/event/79204 79204-20231445@events.umich.edu Event Begins: Tuesday, November 10, 2020 10:30am
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

This research project uses location data gathered from WiFi access points on campus to model the mobility patterns of students in order to inform the planning of educational activities that can minimize the transmission risk.
The first aim is to understand the general mobility patterns of students on campus to identify physical spaces associating with a high-risk of transmission. For example, we can extract insights from WiFi data about which locations are the busiest during which time of the day, how much time was typically spent at each location, and how do these mobility patterns change over time. The second aim is to understand how students share the same physical spaces on campus (e.g. attending a lecture, meeting in the same room, sharing the same dorm). Students are presumably in a close proximity when they are connected to the same WiFi access point. We model a student-to-student network from their co-location activities and use its network centrality measures as proxies of transmission risk (i.e. students in the center of a network would have a higher chance of getting exposed to COVID-19 than those in the periphery). We then correlate network centrality measures with academic information (e.g. class schedule, course enrollment, study major, year of study, gender, ethnicity) to determine whether certain features of the academic record are related to transmission risk. For example, we can identify which groups of students are more vulnerable to potential infections by associating with a high network centrality. Insights from this research project will inform the University of Michigan’s strategies for the recovery of educational activities post-pandemic with empirical evidence of students’ mobility pattern on campus as well as factors that associate with a high-risk of transmission.

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Presentation Thu, 05 Nov 2020 09:35:37 -0500 2020-11-10T10:30:00-05:00 2020-11-10T10:50:00-05:00 Off Campus Location Michigan Institute for Data Science Presentation Quan Nguyen
Modeling the Perceived Truthfulness of Public Statements on COVID-19: A New Model for Pairwise Comparisons of Objects with Multidimensional Latent Attributes (November 10, 2020 10:50am) https://events.umich.edu/event/79205 79205-20231446@events.umich.edu Event Begins: Tuesday, November 10, 2020 10:50am
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

What is more important for how individuals perceive the truthfulness of statements about COVID-19: a) the objective truthfulness of the statements, or b) the partisanship of the individual and the partisanship of the people making the statements? To answer this question, we develop a novel model for pairwise comparisons data that allows for a richer structure of both the latent attributes of the objects being compared and rater-specific perceptual differences than standard models. We use the model to analyze survey data that we collected in the summer of 2020. This survey asked respondents to compare the truthfulness of pairs of statements about COVID-19. These statements were taken from the fact-checked statements on https://www.politifact.com. We thus have an independent measure of the truthfulness of each statement. We find that the actual truthfulness of a statement explains very little of the variability in individuals’ perceptions of truthfulness. Instead, we find that the partisanship of the speaker and the partisanship of the rater account for the majority of the variation in perceived truthfulness, with statements made by co-partisans being viewed as more truthful.

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Presentation Thu, 05 Nov 2020 09:49:47 -0500 2020-11-10T10:50:00-05:00 2020-11-10T11:10:00-05:00 Off Campus Location Michigan Institute for Data Science Presentation Qiushi Yu and Kevin Quinn
Computational Neuroscience, Time Complexity, and Spacetime Analytics (November 10, 2020 11:10am) https://events.umich.edu/event/79206 79206-20231447@events.umich.edu Event Begins: Tuesday, November 10, 2020 11:10am
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

The proliferation of digital information in all human experiences presents difficult challenges and offers unique opportunities of managing, modeling, analyzing, interpreting, and visualizing heterogeneous data. There is a substantial need to develop, validate, productize, and support novel mathematical techniques, advanced statistical computing algorithms, transdisciplinary tools, and effective artificial intelligence apps.

Spacekime analytics is a new technique for modeling high-dimensional longitudinal data, such as functional magnetic resonance imaging (fMRI). This approach relies on extending the notions of time, events, particles, and wave functions to complex-time (kime), complex-events (kevents), data and inference-functions, respectively. This talk will illustrate how the kime-magnitude (longitudinal time order) and kime-direction (phase) affect the subsequent predictive analytics and the induced scientific inference. The mathematical foundation of spacekime calculus reveals various statistical implications including inferential uncertainty and a Bayesian formulation of spacekime analytics. Complexifying time allows the lifting of all commonly observed processes from the classical 4D Minkowski spacetime to a 5D spacetime manifold, where a number of interesting mathematical problems arise.

Spacekime analytics transforms time-varying data, such as time-series observations, into higher-dimensional manifolds representing complex-valued and kime-indexed surfaces (kime-surfaces). This process uncovers some of the intricate structure in high-dimensional data that may be intractable in the classical space-time representation of the data. In addition, the spacekime representation facilitates the development of innovative data science analytical methods for model-based and model-free scientific inference, derived computed phenotyping, and statistical forecasting. Direct neuroscience science applications of spacekime analytics will be demonstrated using simulated data and clinical observations (e.g., UK Biobank).

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Presentation Thu, 05 Nov 2020 09:57:23 -0500 2020-11-10T11:10:00-05:00 2020-11-10T11:30:00-05:00 Off Campus Location Michigan Institute for Data Science Presentation Professor Ivo Dinov
Challenges in dynamic mode decomposition (November 10, 2020 11:30am) https://events.umich.edu/event/79207 79207-20231448@events.umich.edu Event Begins: Tuesday, November 10, 2020 11:30am
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Dynamic Mode Decomposition (DMD) is a powerful tool in extracting spatio-temporal patterns from multi-dimensional time series. DMD takes in time series data and computes eigenvalues and eigenvectors of a finite-dimensional linear model that approximates the infinite-dimensional Koopman operator which encodes the dynamics. DMD is used successfully in many fields: fluid mechanics, robotics, neuroscience, and more. Two of the main challenges remaining in DMD research are noise sensitivity and issues related to Krylov space closure when modeling nonlinear systems. In our work, we encountered great difficulty in reconstructing time series from multilegged robot data. These are oscillatory systems with slow transients, which decay only slightly faster than a period.
Here we present an investigation of possible sources of difficulty by studying a class of systems with linear latent dynamics which are observed via multinomial observables. We explore the influences of dataset metrics, the spectrum of the latent dynamics, the normality of the system matrix, and the geometry of the dynamics. Our numerical models include system and measurement noise. Our results show that even for these very mildly nonlinear conditions, DMD methods often fail to recover the spectrum and can have poor predictive ability. We show that for a system with a well-posed system matrix, having a dataset with more initial conditions and shorter trajectories can significantly improve the prediction. With a slightly ill-conditioned system matrix, a moderate trajectory length improves the spectrum recovery. Our work provides a self-contained framework on analyzing noise and nonlinearity, and gives generalizable insights dataset properties for DMD analysis.
Work was funded by ARO MURI W911NF-17-1-0306 and the Kahn Foundation.

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Presentation Thu, 05 Nov 2020 10:02:20 -0500 2020-11-10T11:30:00-05:00 2020-11-10T11:50:00-05:00 Off Campus Location Michigan Institute for Data Science Presentation Ziyou Wu
Agent-Based Modeling and Systemic Racism (November 10, 2020 2:45pm) https://events.umich.edu/event/79217 79217-20231458@events.umich.edu Event Begins: Tuesday, November 10, 2020 2:45pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

In this workshop, participants will gain a better understanding of systemic bias and how algorithms may continue to promote inequity. Participants will learn about agent based methods, a tool which can be used to examine algorithmic fairness. There will be opportunities to brainstorm ideas for new research projects within the participants’ fields.

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Workshop / Seminar Thu, 05 Nov 2020 10:39:43 -0500 2020-11-10T14:45:00-05:00 2020-11-10T16:15:00-05:00 Off Campus Location Michigan Institute for Data Science Workshop / Seminar Mini-Workshop
Data Science and Natural Language Processing to Find Rare Classes of Entities From Text (November 10, 2020 2:45pm) https://events.umich.edu/event/79220 79220-20231459@events.umich.edu Event Begins: Tuesday, November 10, 2020 2:45pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Natural language processing (NLP) and Data Science methods, including recently popular deep learning-based approaches, can unlock information from narrative text and have received great attention in the medical domain. Many NLP methods have been developed and showed promising results in various information extraction tasks, especially for rare classes of named entities. These methods have also been successfully applied to facilitate clinical research. In this workshop, we will highlight some methods and technologies to identify rare concepts and entities in text in the medical domain as well as other “open” domains.

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Workshop / Seminar Thu, 05 Nov 2020 10:46:39 -0500 2020-11-10T14:45:00-05:00 2020-11-10T16:15:00-05:00 Off Campus Location Michigan Institute for Data Science Workshop / Seminar Mini-Workshop
Intro to Python for Community Members and K-12 Teachers and Students (November 10, 2020 2:45pm) https://events.umich.edu/event/79222 79222-20231462@events.umich.edu Event Begins: Tuesday, November 10, 2020 2:45pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

This hands-on workshop is tailored to audiences who do not have prior programming experience. The first half of the workshop covers Python programming basics and the second half covers performing data analysis and visualization in Python with real-world data. The audiences are encouraged to follow along with the examples on their own computer. We will use an online browser-based environment (Google Colab), and no software installations on your computer are required. Attendees will need a Google account and will sign in to their browser in order to use this cloud-based tool during the workshop.

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Workshop / Seminar Thu, 05 Nov 2020 10:51:28 -0500 2020-11-10T14:45:00-05:00 2020-11-10T16:15:00-05:00 Off Campus Location Michigan Institute for Data Science Workshop / Seminar Mini-Workshop
Mini-Workshops at the MIDAS symposium (November 10, 2020 2:45pm) https://events.umich.edu/event/78763 78763-20121154@events.umich.edu Event Begins: Tuesday, November 10, 2020 2:45pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

There will be six workshops to choose from:
- Agent-based modeling and systemic racism
- Introduction to Python for community members and K-12 teachers and students
- Natural Language Processing for free text analysis
- Scrubbing and cleaning of sensitive data
- Stitching Together the Fabric of 21st Century Social Science
- Video coding and its research applications

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Workshop / Seminar Thu, 22 Oct 2020 09:33:12 -0400 2020-11-10T14:45:00-05:00 2020-11-10T16:15:00-05:00 Off Campus Location Michigan Institute for Data Science Workshop / Seminar MIDAS Symposium 2020
Scrubbing and Cleaning of Sensitive Data (November 10, 2020 2:45pm) https://events.umich.edu/event/79223 79223-20231463@events.umich.edu Event Begins: Tuesday, November 10, 2020 2:45pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Before analysis, data must be retrieved, scrubbed of identifiable information, cleaned (e.g., addressed missing data, reshaped appropriately), and delivered. Using biomedical and transportation datasets as examples of how this generalizable process works, this workshop will walk attendees through a real-world pipeline used to process and deliver datasets. Documentation and code will be made available through GitLab to allow for coding along with the demonstration. As a result of this workshop, attendees will leave with a practical template for implementing their own a data science pipeline.

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Workshop / Seminar Thu, 05 Nov 2020 10:57:27 -0500 2020-11-10T14:45:00-05:00 2020-11-10T16:15:00-05:00 Off Campus Location Michigan Institute for Data Science Workshop / Seminar Mini-Workshop
Stitching Together the Fabric of 21st Century Social Science (November 10, 2020 2:45pm) https://events.umich.edu/event/79225 79225-20231464@events.umich.edu Event Begins: Tuesday, November 10, 2020 2:45pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Today’s pressing questions of social science and public policy demand an unprecedented degree of data scope and integration as we recognize the cross-cutting dynamics of economics, political science, sociology, demography, and psychology. This panel features four UM researchers who are pushing the frontier of data construction and linkage in coordination with partners at the U.S. Census Bureau.

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Workshop / Seminar Thu, 05 Nov 2020 11:01:06 -0500 2020-11-10T14:45:00-05:00 2020-11-10T16:15:00-05:00 Off Campus Location Michigan Institute for Data Science Workshop / Seminar Mini-Workshop
The State of the Art in Automated and Semi-Automated Video Coding (November 10, 2020 2:45pm) https://events.umich.edu/event/79226 79226-20231465@events.umich.edu Event Begins: Tuesday, November 10, 2020 2:45pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Video is being acquired at an alarming rate across domains, including social research, healthcare, entertainment, sporting and more. The ability to code this video—attribute certain properties, labels, and other annotations—in support of analytical domain-relevant questions is critical; otherwise, human coding is required. Human coding, however, is laborious, expensive, not repeatable, and, worse, often error prone. Video coding, an area within artificial intelligence and computer vision, seeks automated and semi-automated methods to support more effective and robust video coding. This workshop will review the state of the art in video coding from a capabilities, limitations and tooling perspective and present real-world use-cases.

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Workshop / Seminar Thu, 05 Nov 2020 11:04:31 -0500 2020-11-10T14:45:00-05:00 2020-11-10T16:16:00-05:00 Off Campus Location Michigan Institute for Data Science Workshop / Seminar Mini-Workshop
Novel Tools to Increase the Reliability and Reproducibility of Population Genetics Research (November 11, 2020 9:00am) https://events.umich.edu/event/79208 79208-20231449@events.umich.edu Event Begins: Wednesday, November 11, 2020 9:00am
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Advances in population genetic research have the potential to create numerous important advances in the science of population dynamics. The interplay of micro-level biology and macro-level social sciences documents gene–environment–phenotype interactions and allows us to examine how genetics relates to child health and wellbeing. However, traditional genetics research is based on nonrepresentative samples that deviate from the target population, such as convenience and volunteer samples. This lack of representativeness may distort association studies. Recent findings have provoked concern about misinterpretation, irreproducibility and lack of generalizability, exemplifying the need to leverage survey research with genetics for population-based research. This project is motivated by the research team’s collaborative work on the Fragile Family and Child Wellbeing Study and the Adolescent Brain Cognitive Development Study, which present these common problems in population genetics studies, to advance the integration of genetic science into population dynamics research. The project will evaluate sample selection effects, identify population heterogeneity in polygenic score analysis, and develop strategies to adjust for selection bias in the association studies of educational attainment, cognition status and substance use for child health and wellbeing. This interdisciplinary project will strengthen the validity and generalizability of population genetics research, deepen new understandings of human behavior and facilitate advances in population science.

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Presentation Thu, 05 Nov 2020 10:08:06 -0500 2020-11-11T09:00:00-05:00 2020-11-11T09:20:00-05:00 Off Campus Location Michigan Institute for Data Science Presentation Yajuan Si
U-M Data Science Annual Symposium 2020 (November 11, 2020 9:00am) https://events.umich.edu/event/75640 75640-19552852@events.umich.edu Event Begins: Wednesday, November 11, 2020 9:00am
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Fully virtual. November 10th-11th

Keynote Speakers:
CATHERINE D’IGNAZIO
Assistant Professor, Urban Science & Planning
Director, Data + Feminism Lab
Department of Urban Studies & Planning, MIT

LAUREN KLEIN
Associate Professor, English, Quantitative Theory and Methods
Emory University

CALL FOR PRESENTATIONS
The Michigan Institute for Data Science (MIDAS) invites submission of 1) abstracts for presentations and 2) proposals for workshops, for the 2020 U-M Data Science Symposium.

As the focal point of data science at U-M, MIDAS facilitates the work of the broad U-M data science community, advances cross-cutting data science methodologies and applications, promotes the use of data science to benefit society, builds data science training pipelines, and develops partnerships with industry, academia and community. The annual symposium showcases the breadth and depth of U-M data science, shares research ideas that will lead to the next breakthroughs, and builds collaboration.

Presentations at the symposium should cover one or more of the following areas of data science:

Theoretical foundations
Methodology and tools
Real-world application in any domain
The ethics and societal impact of data science
Emerging areas of data science
WE INVITE SUBMISSIONS FOR THE FOLLOWING:
1. Proposals for mini-workshops. New this year, the symposium will include 3-5 mini-workshops on the afternoon of Nov. 10 as parallel sessions. Each workshop will be two hours long and for 50-100 attendees. They can be research discussion sessions, tutorials or hack sessions. Proposals should include the theme, format, organizer and potential presenters, as well as how the proposed mini-workshop brings out the strengths across multiple U-M research units and its benefit to U-M data science research and/or to the larger community. If your theme is selected, the symposium program committee will discuss with you further to help finalize the plan, and MIDAS will provide logistics support.

Some examples of possible themes: Mobilizing data science for crisis response; Data preparation for multi-party computing; Introduction of data science to attendees from non-profit organizations; Data science for wearables/mobile health.

If you would like to discuss your mini-workshop idea with the symposium committee before submission, please email Jing Liu, MIDAS Managing Director (ljing@umich.edu),

2. Abstracts for Research Talks (20 minutes including Q&A). The talks should discuss exciting research ideas, provide vision and context for challenging data science questions, stimulate discussions, and lay out collaboration opportunities. These talks should not simply be technical reports of projects.

3. Abstracts for Posters. The Posters can be used as technical reports of projects. Posters with students as first authors will be automatically entered in the poster competition.

DEADLINES:
Mini-workshop proposal submission: 11:59 pm, July 31, 2020; notification: Aug. 14, 2020
Talks and posters abstract submission: 11:59 pm, Sept. 18, 2020; notification: Oct. 9, 2020

SUBMISSION INSTRUCTIONS:
At least one author/presenter should have a U-M affiliation.
Please do not include figures, tables or bibliography in the abstract.
To submit proposals for mini-workshops:
Please include a title, list of organizers/potential presenters and their affiliations.
The main body of the submission should be no more than 300 words.
Please include the theme, format, how it features the strengths from multiple U-M research units, and its impact.
To submit abstracts for research talks and posters:
Please include a title, list of authors/presenters and their affiliations.
The main body of the submission should be no more than 300 words.
For research talks, please include a brief summary of the research idea and its context, potential methods and impact, and how it can benefit from collaboration.
For posters, please include a brief summary of the research, methods, main results and impact.
For questions, please contact midas-research@umich.edu.

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Conference / Symposium Tue, 13 Oct 2020 10:29:30 -0400 2020-11-11T09:00:00-05:00 2020-11-11T13:00:00-05:00 Off Campus Location Michigan Institute for Data Science Conference / Symposium U-M Data Science Annual Symposium 2020
An end-to-end deep learning system for rapid analysis of the breath metabolome with applications in critical care illness and beyond (November 11, 2020 9:20am) https://events.umich.edu/event/79211 79211-20231452@events.umich.edu Event Begins: Wednesday, November 11, 2020 9:20am
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

The metabolome is the set of low-molecular-weight metabolites and its quantification represents a summary of the physiological state of an organism. Metabolite concentration levels in biospecimens are important for many critical care health illnesses like sepsis and acute respiratory distress syndrome (ARDS). Sepsis is responsible for 35% of patients who die in the hospital and ARDS has a mortality rate of 40%. Missing data is a common challenge in metabolomics datasets. Many metabolomics investigators impute fixed values for missing metabolite concentrations and this imputation approach leads to lower statistical power, biased parameter estimates, and reduced prediction accuracy. Certain applications of metabolomics data, like breath analysis by gas chromatography, used for the prediction or detection of ARDS, can be done without the quantification of individual metabolites. This would circumvent the quantification step of individual metabolites, eliminating the missing data problem. Our team has developed a rapid gas chromatography breath analyzer, which has been challenged by missing data, a time-consuming process of breath signature alignment, and the following quantification of metabolites across patients. Analyzing the breath signal directly could eliminate these challenges. End-to-end deep learning systems are neural networks that operate directly on a raw data source and make a prediction directly for the target application. These systems have been successful in diverse fields from speech recognition to medicine. We envision an end-to-end deep learning that leverages transfer learning, from the collection of many healthy samples, that could rapidly multiply the applications of our breath analyzer. The end-to-end deep learning system will enhance our breath analyzer so it could be used more efficiently in areas of the intensive care unit to the battlefield to identity patients or soldiers with critical illnesses like sepsis and ARDS and monitor longitudinal changes in breath metabolites.

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Performance Thu, 05 Nov 2020 10:18:18 -0500 2020-11-11T09:20:00-05:00 2020-11-11T09:40:00-05:00 Off Campus Location Michigan Institute for Data Science Performance Christopher Gillies
Machine learning-guided equations for the on-demand prediction of natural gas storage capacities of materials for vehicular applications (November 11, 2020 9:40am) https://events.umich.edu/event/79212 79212-20231453@events.umich.edu Event Begins: Wednesday, November 11, 2020 9:40am
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Transportation is responsible for nearly one-third of the world’s carbon dioxide (CO2) emission because of burning fossil fuel. While we dream for zero-carbon vehicles, future projections suggest little decline in fossil fuel consumption by the transportation sector until 2050. Therefore, ‘bending the curve’ of CO2 emission prompts the adoption of low-cost and reduced-emission alternative fuels. Natural gas (NG), the most abundant fossil fuel on earth, is such an alternative with nearly 25% lower carbon footprint and lower price compared to its gasoline counterpart. However, the widespread adoption of natural gas as a vehicular fuel is hindered by the scarcity of high-capacity, light-weight, low-cost, and safe storage systems. Recently, materials-based natural gas storage for vehicular applications have become one of the most viable options. Especially, nanoporous materials (NPMs) are in the spotlight of the U.S. Department of Energy (DOE) because of their exceptional energy storage capacities. However, the number of such NPMs is nearly infinite. It is unknown, a priori, which materials would have the expected natural gas storage capacity. Therefore, searching a high-performing material is like ‘finding a needle in a haystack’ that slows down the speed of materials discovery against growing technological demand. Here we present a novel approach of developing machine learning-guided equations for the on-demand prediction of energy storage capacities of NPMs using a few physically meaningful structural properties. These equations provide users the ability to calculate energy storage capacity of an arbitrary NPM rapidly using only paper and pencil. We show the utility of these equations by predicting NG storage of over 500,000 covalent-organic frameworks (COFs), a class of NPMs. We discovered a COF with record-setting NG storage capacity, surpassing the unmet target set by DOE. In principle, the data-driven approach presented here might be relevant to other disciplines including science, engineering, and health care.

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Presentation Thu, 05 Nov 2020 10:22:47 -0500 2020-11-11T09:40:00-05:00 2020-11-11T10:00:00-05:00 Off Campus Location Michigan Institute for Data Science Presentation Alauddin Ahmed
Fusing Computer Vision And Space Weather Modeling (November 11, 2020 10:00am) https://events.umich.edu/event/79214 79214-20231455@events.umich.edu Event Begins: Wednesday, November 11, 2020 10:00am
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Space weather has impacts on Earth ranging from rare, immensely disruptive events (e.g., electrical blackouts caused by solar flares and coronal mass ejections) to more frequent impacts (e.g., satellite GPS interference from fluctuations in the Earth’s ionosphere caused by rapid variations in the solar extreme UV emission). Earth-impacting events are driven by changes in the Sun’s magnetic field; we now have myriad instruments capturing petabytes worth of images of the Sun at a variety of wavelengths, resolutions, and vantage points. These data present opportunities for learning-based computer vision since the massive, well-calibrated image archive is often accompanied by physical models. This talk will describe some of the work that we have been doing to start integrating computer vision and space physics by learning mappings from one image or representation of the Sun to another. I will center the talk on a new system we have developed that emulates parts of the data processing pipeline of the Solar Dynamics Observatory’s Helioseismic and Magnetic Imager (SDO/HMI). This pipeline produces data products that help study and serve as boundary conditions for solar models of the energetic events alluded to above. Our deep-learning-based system emulates a key component hundreds of times faster than the current method, potentially opening doors to new applications in near-real-time space weather modeling. In keeping with the goals of the symposium, however, I will focus on some of the benefits close collaboration has enabled in terms of understanding how to frame the problem, measure success of the model, and even set up the deep network.

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Presentation Thu, 05 Nov 2020 10:27:08 -0500 2020-11-11T10:00:00-05:00 2020-11-11T10:20:00-05:00 Off Campus Location Michigan Institute for Data Science Presentation David Fouhey
Decoding the Environment of Most Energetic Sources in the Universe (November 11, 2020 10:20am) https://events.umich.edu/event/79215 79215-20231456@events.umich.edu Event Begins: Wednesday, November 11, 2020 10:20am
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Astrophysics has always been at the forefront of data analysis. It has led to advancements in image processing and numerical simulations. The coming decade is bringing qualitatively new and larger datasets than ever before. The next generation of observational facilities will produce an explosion in the quantity and quality of data for the most distant sources, such as the first galaxies and first quasars. Quasars are the most energetic objects in the universe, reaching luminosity up to 10^14 that of the Sun. Their emission is powered by giant black holes that convert matter into energy according to the famous Einstein’s equation E = mc^2. The largest progress will occur in quasar spectroscopy. Detailed measurements of spectrum of quasar light, as it is being emitted near the central black hole and partially absorbed by clouds of gas on the way to the observer on Earth, allows for a particularly powerful probe of quasar environment. Because spectra of different chemical elements are unique, spectroscopy allows to study not only the overall properties of matter such as density and temperature, but also the detailed chemical composition of the intervening matter. However, the interpretation of these spectra is made very challenging by the many sources contributing to the absorption of light. In order to take a full advantage of this new window into the nature of supermassive black holes we need detailed theoretical understanding of the origin of quasar spectral features. In a MIDAS PODS project we are applying machine learning to model and extract such features. We are training the models using data from the state-of-the-art numerical simulations of the early universe. This approach is fundamentally different from traditional astronomical data analysis. We have only started learning what information can be extracted and still looking for a new framework to interpret these data.

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Performance Thu, 05 Nov 2020 10:31:24 -0500 2020-11-11T10:20:00-05:00 2020-11-11T10:40:00-05:00 Off Campus Location Michigan Institute for Data Science Performance Oleg Gnedin
Fireside Chat with Eric Horvitz (November 11, 2020 11:00am) https://events.umich.edu/event/78764 78764-20121155@events.umich.edu Event Begins: Wednesday, November 11, 2020 11:00am
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Fireside Chat with Eric Horvitz, Microsoft, Chief Scientific Officer, November 11th, 11:00

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Lecture / Discussion Thu, 22 Oct 2020 10:23:02 -0400 2020-11-11T11:00:00-05:00 2020-11-11T12:00:00-05:00 Off Campus Location Michigan Institute for Data Science Lecture / Discussion Eric Horvitz
Webinar: Drawing a Portrait of Arts and Culture in the U.S. with the latest data from NADAC (November 12, 2020 11:00am) https://events.umich.edu/event/78592 78592-20068101@events.umich.edu Event Begins: Thursday, November 12, 2020 11:00am
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

Register: http://myumi.ch/erdzq

Please join us for the upcoming tour around the latest data on arts and culture, freely available to researchers and the general public at the National Archive of Data on Arts & Culture. Your guides will introduce you to NADAC’s recently released studies covering a wealth of topics including public participation in the arts in the United States; the impact of arts and cultural production on the United States economy; data on employment and income for those employed in the arts; information on the amount of time that people spend doing various arts activities; unique and amazing dance history data, and more!

The webinar takes place on November 12 at 11 am EST and is hosted by ICPSR, with presenters including NADAC Project Manager Anya Ovchinnikova; Data Project Manager, David Thomas; and featuring special guest Sunil Iyengar, the Director of the Office of Research & Analysis of the National Endowment for the Arts.

Thanks to the support of the National Endowment for the Arts, this webinar is free and open to the public.

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Presentation Thu, 05 Nov 2020 11:47:05 -0500 2020-11-12T11:00:00-05:00 2020-11-12T12:00:00-05:00 Off Campus Location Inter-university Consortium for Political and Social Research Presentation Announcement of webinar arts and culture data November 4 2020 from ICPSR
From Sky Surveys to Cancer: Spatial Data Everywhere (November 18, 2020 3:00pm) https://events.umich.edu/event/78283 78283-20002866@events.umich.edu Event Begins: Wednesday, November 18, 2020 3:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

The talk describes a 25 year journey leading from the Sloan Digital Sky Survey to a wide range of projects in data science. There are many common threads: the need for extreme interactivity, the need for flexible data aggregation and the commonality of spatial data. The size of data sets have grown almost a million fold, but user expectations for almost instant results has not changed. The talk will describe the gradual evolution of the SciServer, and how new interactive metaphors to interact with hundreds of terabytes of turbulence simulations emerged. We will discuss how machine learning and AI tools are transforming science, from simulations to how large experiments are designed and executed. We will also emphasize that much of these new developments still rely on having unique high value data sets at our fingertips, and how the long term survival of these is entering a critical, endangered phase.

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Presentation Fri, 13 Nov 2020 12:16:36 -0500 2020-11-18T15:00:00-05:00 2020-11-18T17:00:00-05:00 Off Campus Location Michigan Institute for Data Science Presentation https://umich.zoom.us/j/96874360760
Women + Data Science (November 19, 2020 3:30pm) https://events.umich.edu/event/78621 78621-20075975@events.umich.edu Event Begins: Thursday, November 19, 2020 3:30pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Michigan State University & University of Michigan invite you to their joint monthly webinar & meetup series for Fall 2020! Please register for access to the Zoom link.

Keynote speaker - Maria Chikina
Lightning talk speakers - Anna Yannakopoulos, MSU | Kayla Johnson, MSU | Stephanie Hickey, MSU

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Presentation Fri, 16 Oct 2020 15:47:06 -0400 2020-11-19T15:30:00-05:00 2020-11-19T17:00:00-05:00 Off Campus Location Michigan Institute for Data Science Presentation Women + Data Science
TRACKING THE ‘MOOD’ OF U.S. MEDIA COVERAGE, 1990-2020 (November 23, 2020 4:00pm) https://events.umich.edu/event/79451 79451-20327786@events.umich.edu Event Begins: Monday, November 23, 2020 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Abstract: Survey research has for many years tracking the ‘mood’ of the country. That measure has been useful for understanding trends in economic and political behavior. But where does ‘mood’ come from? And are there other ways to capture the mood of the country? This presentation explores the potential for a media-based measure of mood, explored both as a driver and reflection of pubic attitudes. Media mood is estimating using automated content analytic techniques on a very large corpus of full-text news content. Time series analysis is used to explore differences across news outlets, and the relationship between media content and public opinion from 1990 to the present.

Bio: Stuart Soroka is the Michael W. Traugott Collegiate Professor of Communication and Media & Political Science, and Research Professor in the Center for Political Studies at the Institute for Social Research, University of Michigan. His research focuses on political communication, the sources and/or structure of public preferences for policy, and the relationships between public policy, public opinion, and mass media.

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Presentation Tue, 17 Nov 2020 16:54:47 -0500 2020-11-23T16:00:00-05:00 2020-11-23T17:00:00-05:00 Off Campus Location Michigan Institute for Data Science Presentation Stuart Soroka
Research Universities and the Public Good in the Time of COVID-19 (December 2, 2020 2:00pm) https://events.umich.edu/event/79506 79506-20345431@events.umich.edu Event Begins: Wednesday, December 2, 2020 2:00pm
Location: Off Campus Location
Organized By: Institute for Social Research

ISR Insights Speaker Series is a series focusing on the research happening at ISR.

Jason Owen-Smith (Executive Director, Institute for Research on Innovation & Science (IRIS); Executive Director, Research Analytics; Professor of Sociology, University of Michigan; Research Professor, Institute for Social Research)

Wednesday, December 2 at 2pm EST: https://umich.zoom.us/j/91211224326

America's most research intensive universities represent about 3% of higher education institutions, but they conduct 90% of the nation's academic research. Drawing on his recent book, Research Universities and the Public Good: Discovery for an Uncertain Future and the work of ISR's Institute for Research on Innovation & Science (IRIS), which he directs, Jason Owen-Smith will explain how these unique and essential organizations serve as an important form of "social insurance" in the face of an uncertain future. Universities like U of M are uniquely able to address "unknown unknowns," problems and opportunities we do not know we have yet. No other sector or type of organization accomplishes is equipped to serve this purpose in our society. COVID-19 puts special pressures on the academic research mission that come after more than a decade of declining public support. The pandemic and its effects jeopardize the US Academic Research Enterprise (US-ARE) and with it the future health, wealth, and well-being of our nation and the world. Drawing on unique data science resources developed at IRIS, and 20 years of work on the economic and social value of research and innovation, Owen-Smith highlights the challenges and explains how they might be addressed by federal and state policy-makers, the leaders and faculty of institutions like ours.

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Livestream / Virtual Thu, 19 Nov 2020 18:17:21 -0500 2020-12-02T14:00:00-05:00 2020-12-02T15:00:00-05:00 Off Campus Location Institute for Social Research Livestream / Virtual flyer
GENOMIC DATA SHARING: THE PRIVACY RISK AND TECHNICAL MITIGATIONS (December 7, 2020 4:00pm) https://events.umich.edu/event/79452 79452-20327787@events.umich.edu Event Begins: Monday, December 7, 2020 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Personalized medicine makes use of rich and multi-modality individual health data for the promise of better diagnosis, improved health, and a high quality and longer life. The human genome is a key piece in the puzzle. The collection and sharing of personal genomics for research alsobring an increasing concern about privacy, and the risk of discrimination and stigmatization. There are important ethical, legal, and social implications, for example, individual genome information is known to be uniquely identifiable, which is also highly associated with relatives. Trust, accountability, and equity are critical pillars to enable responsible data sharing.

In this talk, I will overview the genomic privacy risks to show various kinds of vulnerability, covering linkage attack, membership attack, and other inference attacks. Then, I will introduce some technical mitigation strategies including secure outsourcing, multiparty computing, and privacy-preserving output perturbation. I hope that this talk will contribute to the awareness of our community with respect to the magnitude of the challenge and the necessity to develop effective and practical solutions.

Bio:

Dr. Jiang is a Christopher Sarofim family professor and center director of Secure Artificial intelligence For hEalthcare (SAFE) in the School of Biomedical Informatics (SBMI) at The University of Texas Health Science Center at Houston (UTHealth). Before joining UTHealth in 2018, he was an associate professor with tenure in the Department of Biomedical Informatics (DBMI) at UCSD. He is an associate editor of BMC Medical Informatics and Decision Making and serves as an editorial board member of the Journal of American Medical Informatics Association. His expertise is primarily in health data privacy and predictive models in biomedicine based on his Computer Science Ph.D. training from Carnegie Mellon University. He received NIH R00, R13, R21, R01, U01 grants as PI, obtained career awards like CPRIT Rising Stars and UT Stars, and won several best and distinguished paper awards from American Medical Informatics Association (AMIA) Joint Summits on Translational Science (2012, 2013, 2016). He is one of the organizers of the iDASH Genome Privacy competition (2014 – present), which was reported by Nature News and GenomeWeb.

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Presentation Tue, 17 Nov 2020 17:09:49 -0500 2020-12-07T16:00:00-05:00 2020-12-07T17:00:00-05:00 Off Campus Location Michigan Institute for Data Science Presentation Xiaoqian Jiang
MIDAS Seminar Series Presents: Eric Xing – Carnegie Mellon University (December 14, 2020 4:00pm) https://events.umich.edu/event/79453 79453-20327788@events.umich.edu Event Begins: Monday, December 14, 2020 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Professor, Computer Science, Carnegie Mellon University

Founder, CEO, and Chief Scientist, Petuum Inc.

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Presentation Tue, 17 Nov 2020 17:17:42 -0500 2020-12-14T16:00:00-05:00 2020-12-14T17:00:00-05:00 Off Campus Location Michigan Institute for Data Science Presentation Eric Xing
Data Science Coast to Coast Presents: Dr. Jeanne Holm (December 15, 2020 3:00pm) https://events.umich.edu/event/78800 78800-20125167@events.umich.edu Event Begins: Tuesday, December 15, 2020 3:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

The DS C2C seminar series, hosted jointly by six academic data science institutes, provides a unique opportunity to foster a broad-reaching data science community.

This fall, the series features important figures in data science, who will provide insight on the transformative use of data science in traditional research disciplines, future breakthroughs in data science research, data science entrepreneurship, and advocacy and national policies for a data-enabled and just society.

Speakers throughout the winter and spring will include faculty members and postdoctoral fellows at the six universities whose research spans the theory and methodology of data science, and their application in arts and humanities, engineering, biomedical, natural, physical and social sciences.

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Presentation Thu, 22 Oct 2020 23:08:05 -0400 2020-12-15T15:00:00-05:00 2020-12-15T16:00:00-05:00 Off Campus Location Michigan Institute for Data Science Presentation Data Science: Coast 2 Coast
THE CHANGING LANDSCAPE OF BIOMEDICAL DATA COLLECTIONS (December 21, 2020 4:00pm) https://events.umich.edu/event/79454 79454-20327789@events.umich.edu Event Begins: Monday, December 21, 2020 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Abstract:

The landscape of biomedical data is incredibly complex, rich, and rapidly changing, especially as we navigate the influx of data from the COVID-19 pandemic. More and more data is moving to the cloud, both existing and newly generated, with multiple cloud providers adding to the complexity. The data includes Electronic Health Records (EHRs), genomic data, and imaging/sensed data (e.g., pictures of tumors, lungs, cells, gas chromatographs), and all this data is enabling us to delve much deeper into complex biological concepts, for example, the relationship between phenotypes and genotypes. The NHLBI BioData Catalyst project is one example of a coordinated effort to move vast amounts of data into the cloud, navigating the complexities of data ingestion, diverse and widespread teams, and multiple cloud providers/environments.

On top of the massive shift to being able to apply huge amounts of data to better understand individuals, populations and, ultimately, life itself, we need a way to organize all this information. The activities in the NCATS Biomedical Data Translator project can be viewed as a constantly evolving analysis of the relationships of disparate data sets. In a sense, Translator is like Google for searching biomedical data.

My talk will introduce both projects and their respective impacts on biomedical research.

Bio:

Dr. Stan Ahalt is the Director of the Renaissance Computing Institute (RENCI) at UNC-Chapel Hill. As Director, he leads a team of research scientists, software and network engineers, data science specialists, and visualization experts who work closely with faculty research teams at UNC, Duke, NCSU, and partners across the country. Dr. Ahalt is also a Professor in UNC’s Department of Computer Science and the Associate Director of Informatics and Data Science (IDSci) in the North Carolina Translational and Clinical Sciences Institute (NC TraCS), UNC’s CTSA award; in this role, Dr. Ahalt leverages the expertise and resources of RENCI to foster clinical and translational research across UNC’s campus. Dr. Ahalt earned his Ph.D. in Electrical and Computer Engineering from Clemson University and has over 30 years of experience in data science, signal and image processing, and pattern recognition/ML.

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Presentation Mon, 23 Nov 2020 12:01:11 -0500 2020-12-21T16:00:00-05:00 2020-12-21T17:00:00-05:00 Off Campus Location Michigan Institute for Data Science Presentation Stan Ahalt
MIDAS & Owkin Federated Learning in Biomedical Research Workshop (January 14, 2021 10:00am) https://events.umich.edu/event/80139 80139-20566722@events.umich.edu Event Begins: Thursday, January 14, 2021 10:00am
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Objective: Cultivating research collaboration, joint grants and connecting the UM researchers to the right organisations. Supports Owkin expansion of our presence in North America and facilitates collaborations with PIs at UM. A great introduction to what Owkin does to UM.

Introduction Owkin & Scientific Overview of the Sessions — Patrick Sin-Chan, Partnerships Manager – Owkin
Session 1: Methodology and Data Science
Learning From Others Without Sacrificing Privacy: Application of Federated Machine Learning to Mobile Health Data
Presenter: Ambuj Tewari, Associate Professor, Statistics
Privacy Preserving Federated Learning Platform: from Design to Deployment in Real World Use Cases
Presenter: Camille Marini
Accelerating Machine Learning with Multi-Armed Bandit
Barzan Mozafari, Associate Professor, Computer Science and Engineering
Siloed Federated Learning for Multi-Centric Histopathology Datasets
Presenter: Mathieu Andreux
20 mins Panel Discussion (MIDAS Moderator- Kayvan Najarian, Professor, Computational Medicine and Bioinformatics)
Session 2: Biotech/medical
Covid-19 Severity Analysis with CT Scans and Machine Learning
Presenter: Simon Jégou
Linking Single-cell Molecular States with Phenotypes Using Machine Learning
Presenter: Josh Welch, Assistant Professor, Computational Medicine and Bioinformatics
HE2RNA: a Deep Learning Model to Predict RNA-Seq Expression of Tumors from Whole Slide Images
Presenter: Alberto Romagnoni
Using Large-scale Pharmacogenomic Databases to Predict Drug Effectiveness
Presenter: Johann Gagnon-Bartsch, Assistant Professor, Statistics
20 mins Panel discussion (Owkin Moderator: Patrick Sin-Chan)

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Workshop / Seminar Thu, 17 Dec 2020 19:36:31 -0500 2021-01-14T10:00:00-05:00 2021-01-14T14:30:00-05:00 Off Campus Location Michigan Institute for Data Science Workshop / Seminar Okwin
KNOWLEDGE EXTRACTION TO ACCELERATE SCIENTIFIC DISCOVERY (January 18, 2021 4:00pm) https://events.umich.edu/event/79534 79534-20373071@events.umich.edu Event Begins: Monday, January 18, 2021 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

To combat COVID-19, clinicians and scientists all need to digest the vast amount of relevant biomedical knowledge in literature to understand the disease mechanism and the related biological functions. The first challenge is quantity. For example, nearly 2.7K new papers are published at PubMed per day. This knowledge bottleneck causes significant delay in the development of vaccines and drugs for COVID-19. The second challenge is quality due to the rise and rapid, extensive publications of preprint manuscripts without pre-publication peer review. Many research results about coronavirus from different research labs and sources are redundant, complementary or event conflicting with each other.

Let’s consider drug repurposing as a case study. Besides the long process of clinical trial and biomedical experiments, another major cause for the long process is the complexity of the problem involved and the difficulty in drug discovery in general. The current clinical trials for drug re-purposing mainly rely on symptoms by considering drugs that can treat diseases with similar symptoms. However, there are too many drug candidates and too much misinformation published from multiple sources. In addition to a ranked list of drugs, clinicians and scientists also aim to gain new insights into the underlying molecular cellular mechanisms on Covid-19, and which pre-existing conditions may affect the mortality and severity of this disease.

To tackle these two challenges, we have developed a novel and comprehensive knowledge discovery framework, COVID-KG, to accelerate scientific discovery and build a bridge between clinicians and biology scientists. COVID-KG starts by reading existing papers to build multimedia knowledge graphs (KGs), in which nodes are entities/concepts and edges represent relations involving these entities, extracted from both text and images. Given the KGs enriched with path ranking and evidence mining, COVID-KG answers natural language questions effectively. Using drug repurposing as a case study, for 11 typical questions that human experts aim to explore, we integrate our techniques to generate a comprehensive report for each candidate drug. Preliminary assessment by expert clinicians and medical school students show our generated reports are informative and sound. I will also talk about our ongoing work to extend this framework to other domains including molecular synthesis and agriculture.

Bio:

Heng Ji is a professor at Computer Science Department, and an affiliated faculty member at Electrical and Computer Engineering Department of University of Illinois at Urbana-Champaign. She is also an Amazon Scholar. She received her B.A. and M. A. in Computational Linguistics from Tsinghua University, and her M.S. and Ph.D. in Computer Science from New York University. Her research interests focus on Natural Language Processing, especially on Multimedia Multilingual Information Extraction, Knowledge Base Population and Knowledge-driven Generation. She was selected as “Young Scientist” and a member of the Global Future Council on the Future of Computing by the World Economic Forum in 2016 and 2017. The awards she received include “AI’s 10 to Watch” Award by IEEE Intelligent Systems in 2013, NSF CAREER award in 2009, Google Research Award in 2009 and 2014, IBM Watson Faculty Award in 2012 and 2014 and Bosch Research Award in 2014-2018, and ACL2020 Best Demo Paper Award. She was invited by the Secretary of the U.S. Air Force and AFRL to join Air Force Data Analytics Expert Panel to inform the Air Force Strategy 2030. She is the lead of many multi-institution projects and tasks, including the U.S. ARL projects on information fusion and knowledge networks construction, DARPA DEFT Tinker Bell team and DARPA KAIROS RESIN team. She has coordinated the NIST TAC Knowledge Base Population task since 2010. She has served as the Program Committee Co-Chair of many conferences including NAACL-HLT2018. She is elected as the North American Chapter of the Association for Computational Linguistics (NAACL) secretary 2020-2021. Her research has been widely supported by the U.S. government agencies (DARPA, ARL, IARPA, NSF, AFRL, DHS) and industry (Amazon, Google, Bosch, IBM, Disney).

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Performance Mon, 23 Nov 2020 09:48:55 -0500 2021-01-18T16:00:00-05:00 2021-01-18T17:00:00-05:00 Off Campus Location Michigan Institute for Data Science Performance Heng Li
MIDAS seminar series presents: Arya Farahi, MIDAS fellow (January 21, 2021 4:00pm) https://events.umich.edu/event/81037 81037-20838679@events.umich.edu Event Begins: Thursday, January 21, 2021 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Arya Farahi is a Data Science Fellow at the University of Michigan

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Presentation Thu, 21 Jan 2021 09:33:38 -0500 2021-01-21T16:00:00-05:00 2021-01-21T17:00:00-05:00 Off Campus Location Michigan Institute for Data Science Presentation Arya Farahi
COMPUTER VISION: WHO IS HELPED AND WHO IS HARMED? (January 25, 2021 4:00pm) https://events.umich.edu/event/79537 79537-20373074@events.umich.edu Event Begins: Monday, January 25, 2021 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Computer vision has ceased to be a purely academic endeavor. From law enforcement, to border control, to employment, healthcare diagnostics, and assigning trust scores, computer vision systems are being rapidly integrated into all aspects of society. In research, there are works that purport to determine a person’s sexuality from their social network profile images, others that claim to classify “violent individuals” from drone footage. These works were published in high impact journals, and some were presented at workshops in top tier computer vision conferences such as CVPR.

A critical public discourse surrounding the use of computer-vision based technologies has also been mounting. For example, the use of facial recognition technologies by policing agencies has been heavily critiqued and, in response, companies such as Microsoft, Amazon, and IBM have pulled or paused their facial recognition software services. Gender Shades showed that commercial gender classification systems have high disparities in error rates by skin-type and gender, and other works discuss the harms caused by the mere existence of automatic gender recognition systems. Recent papers have also exposed shockingly racist and sexist labels in popular computer vision datasets–resulting in the removal of some. In this talk, I will highlight some of these issues and proposed solutions to mitigate bias, as well as how some of the proposed fixes could exacerbate the problem rather than mitigate it.

Bio:

Timnit Gebru is a senior research scientist at Google co-leading the Ethical Artificial Intelligence research team. Her work focuses on mitigating the potential negative impacts of machine learning based systems. Timnit is also the co-founder of Black in AI, a non profit supporting Black researchers and practitioners in artificial intelligence. Prior to this, she did a postdoc at Microsoft Research, New York City in the FATE (Fairness Transparency Accountability and Ethics in AI) group, where she studied algorithmic bias and the ethical implications underlying any data mining project. She received her Ph.D. from the Stanford Artificial Intelligence Laboratory, studying computer vision under Fei-Fei Li. Prior to joining Fei-Fei’s lab, she worked at Apple designing circuits and signal processing algorithms for various Apple products including the first iPad.

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Presentation Mon, 23 Nov 2020 10:00:32 -0500 2021-01-25T16:00:00-05:00 2021-01-25T17:00:00-05:00 Off Campus Location Michigan Institute for Data Science Presentation Timnit Gebru
Ivo Dinov: Data Science, Time Complexity, and Spacekime Analytics (February 4, 2021 11:00am) https://events.umich.edu/event/81198 81198-20872023@events.umich.edu Event Begins: Thursday, February 4, 2021 11:00am
Location: Off Campus Location
Organized By: Michigan Institute for Computational Discovery and Engineering

Abstract: Many observable processes demand managing, harmonizing, modeling, analyzing, interpreting, and visualizing of large and complex information. There is a substantial need to develop, validate, productize, and support novel mathematical techniques, advanced statistical computing algorithms, transdisciplinary tools, and effective artificial intelligence applications. Spacekime analytics is a new technique for modeling high-dimensional longitudinal data. This approach relies on extending the notions of time, events, particles, and wavefunctions to complex-time (kime), complex-events (kevents), data, and inference-functions. We will illustrate how the kime-magnitude (longitudinal time order) and kime-direction (phase) affect the subsequent predictive analytics and the induced scientific inference.

The mathematical foundation of spacekime calculus reveal various statistical implications including inferential uncertainty and a Bayesian formulation of spacekime analytics. Complexifying time allows the lifting of all commonly observed processes from the classical 4D Minkowski spacetime to a 5D spacekime manifold, where a number of interesting mathematical problems arise. Direct data science applications of spacekime analytics will be demonstrated using simulated data and clinical observations (e.g., structural and functional MRI).

Bio: Dr. Ivo D. Dinov directs the Statistics Online Computational Resource (SOCR), co-directs the multi-institutional Probability Distributome Project, and is an associate director for education of the Michigan Institute for Data Science (MIDAS).

Dr. Dinov is an expert in mathematical modeling, statistical analysis, computational processing and visualization of Big Data. He is involved in longitudinal morphometric studies of human development (e.g., Autism, Schizophrenia), maturation (e.g., depression, pain) and aging (e.g., Alzheimer’s and Parkinson’s diseases). Dr. Dinov is developing, validating and disseminating novel technology-enhanced pedagogical approaches for scientific education and active learning.

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Workshop / Seminar Mon, 25 Jan 2021 14:39:29 -0500 2021-02-04T11:00:00-05:00 2021-02-04T12:00:00-05:00 Off Campus Location Michigan Institute for Computational Discovery and Engineering Workshop / Seminar Prof. Ivo Dinov
MIDAS Seminar Series and ICPSR Co-present: Misty Heggeness, Research Economist, US Census Bureau (February 8, 2021 4:00pm) https://events.umich.edu/event/81038 81038-20838680@events.umich.edu Event Begins: Monday, February 8, 2021 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

I examine the impact of the COVID-19 shock on parents’ labor supply during the initial stages of the pandemic. Using difference-in-difference estimation and monthly panel data from the Current Population Survey (CPS), I compare labor market attachment, non-work activity, hours worked, and earnings and wages of those in areas with early school closures and stay-in-place orders with those in areas with delayed or no pandemic closures. While there was no immediate impact on detachment or unemployment, mothers with jobs in early closure states were 68.8 percent more likely than mothers in late closure states to have a job but not be working as a result of early shutdowns. There was no effect on working fathers or working women without school age children. Mothers who continued working increased their work hours relative to comparable fathers; this effect, however, appears entirely driven by a reduction in fathers’ hours worked. Overall, the pandemic appears to have induced a unique immediate juggling act for working parents of school age children. Mothers took a week of leave from formal work; fathers working fulltime, for example, reduced their hours worked by 0.53 hours over the week. While experiences were different for mothers and fathers, each are vulnerable to scarring and stunted opportunities for career growth and advancement due to the pandemic.

Misty Heggeness is Principal Economist and Senior Advisor for Evaluations and Experiments at the U.S. Census Bureau. Dr. Heggeness has a PhD from the University of Minnesota. She has worked as a research economist in the U.S. federal government since 2010 and also held positions at the National Institutes of Health and the US Department of Labor. She teaches a course on policy analysis and evaluation at the University of Maryland. Her research focuses on survey response quality, poverty & inequality, gender, and the high skilled workforce and has appeared in outlets like The New York Times, Wall Street Journal, The Economist, Nature, and Science. At the Census Bureau, she leads a high-profile initiative to integrate the Census Bureau’s major frames and co-leads a 2020 administrative records census project.

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Presentation Thu, 21 Jan 2021 09:38:52 -0500 2021-02-08T16:00:00-05:00 2021-02-08T17:00:00-05:00 Off Campus Location Michigan Institute for Data Science Presentation Misty Heggeness
Racial Justice in the Age of Data and AI - A Community Forum (February 11, 2021 4:00pm) https://events.umich.edu/event/81549 81549-20925393@events.umich.edu Event Begins: Thursday, February 11, 2021 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

This community forum is a follow-up event of Timnit Gebru’s recent seminar, “Computer vision: who is helped and who is harmed?” (watch video). The record-breaking attendance of the seminar and the active participation of the audience reflected the significance of this topic to our data science community. Three faculty members will moderate the forum, where attendees can share their observations and insights, and thoughts on how data scientists can be part of the solution for racial justice in the age of data and AI.

Moderators:
Dr. H. V. Jagadish, Director of the Michigan Institute for Data Science, and Professor of Computer Science and Engineering
Dr. Shobita Parthasarathy, Professor of Public Policy and Women’s Studies, Director of Science, Technology, and Public Policy Program
Dr. Sarita Schoenebeck, Associate Professor, School of Information
Dr. Apryl Williams, Assistant Professor, Communication & Media

For more information and a reading/watch list for a deeping understanding, please visit the calendar listing on the MIDAS website. (link to the right)

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Lecture / Discussion Mon, 01 Feb 2021 20:33:03 -0500 2021-02-11T16:00:00-05:00 2021-02-11T17:30:00-05:00 Off Campus Location Michigan Institute for Data Science Lecture / Discussion is AI racist?
Matthew Duschenes (Applied Physics) and Yi Zhu (Civil and Environmental Engineering) (February 18, 2021 4:00pm) https://events.umich.edu/event/81278 81278-20879917@events.umich.edu Event Begins: Thursday, February 18, 2021 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Computational Discovery and Engineering

MATTHEW DUSCHENES: I am in my third year of the Applied Physics & Scientific Computing Ph.D. programs, after completing a master’s in theoretical physics in my home country of Canada. As a member of Dr. Krishna Garikipati’s Computational Physics group, I am currently working on data driven modelling and am collaborating with several groups on applying these graph theoretic approaches to various systems of interest.

"GRAPH THEORETIC APPROACHES FOR PHYSICAL SYSTEMS": Numerical analyses of physical systems are conventionally performed using direct numerical simulations, that have proven highly successful, yielding high fidelity solutions to very high dimensional problems, such as boundary value problems with upwards of tens of millions of degrees of freedom. However, there is always a balance to be met between the desire for higher accuracy and additional physics to be modeled, and the complexity, interpret-ability and ease of representation of such solutions. To aid in this dilemma, I will be introducing a novel graph theoretic approach, allowing for lower dimensional, reduced order models to be produced, given small amounts of high fidelity data. In this talk I will explain how such an approach allows for an intuitive representation of the states of a systems, and how it is possible to use a non-local calculus, allowing for rigorous operators and equations to be defined on the graph. I will then be discussing some implementation details, and convey the generality, validity, and future applications of this framework through some example results from collaborations.

YI ZHU: Yi is a 3rd year PhD candidate in Civil and Environmental Engineering & Scientific Computation. His research focuses on simulation, design, and fabrication of active origami systems for engineering devices, and is particularly focused on micro-scale shape morphing systems inspired by origami.

"SIMULATION AND DESIGN OF MICRO-ORIGAMI SYSTEMS": In this talk, we will introduce some recent advancement in the simulation and the design of micro-origami systems. We will discuss the micro-origami structures we fabricated and the rapid simulation framework we developed to capture the behaviors of these active origami. We will focus on the simulation framework and demonstrate how we can capture the thermo-mechanically coupled folding behavior and contacts between origami panels effectively and rapidly. Finally, we will introduce some ongoing work on extracting origami design principle with interpretable machine learning, which demonstrates how we can use the simulation framework to create better origami design.

Register: https://umich.zoom.us/meeting/register/tJwrdeurpz4vEtUPKlRzUo1tQy3mskgineol

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Workshop / Seminar Thu, 28 Jan 2021 17:10:00 -0500 2021-02-18T16:00:00-05:00 2021-02-18T17:00:00-05:00 Off Campus Location Michigan Institute for Computational Discovery and Engineering Workshop / Seminar Matthew Duschenes and Yi Zhu
MIDAS Seminar Series, MiCHAMP, and Precision Health Co-Present: Casey Greene, School of Medicine, University of Pennsylvania (February 22, 2021 4:00pm) https://events.umich.edu/event/81040 81040-20838682@events.umich.edu Event Begins: Monday, February 22, 2021 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Abstract:

Biomedical research disciplines are awash in data. These data, generated by new technologies as well as old approaches, provide the opportunity to systematically extract biological patterns that were previously difficult to observe. I’ll share vignettes focusing on three areas: 1) how we can use large-scale public data to better understand data for which few observations are available; 2) some work to understand why large-scale integrative analyses are beneficial; and 3) how machine learning can help to produce more datasets suitable for integration while maintaining participant privacy.

Dr. Casey Greene is an Associate Professor of Systems Pharmacology and Translational Therapeutics in the Perelman School of Medicine at the University of Pennsylvania and the Director of the Childhood Cancer Data Lab, powered by Alex’s Lemonade Stand Foundation. His lab develops machine learning methods that integrate distinct large-scale datasets to extract the rich and intrinsic information embedded in such integrated data. This approach reveals underlying principles of genetics, cellular environments, and cellular responses to that environment. Casey’s devotion to the analysis of publicly available data doesn’t stop in the lab. In 2016, Casey established the “Research Parasite Awards” after an editorial in the New England Journal of Medicine deemed scientists who analyze other scientists’ data “research parasites.” These honors, accompanied by a cash prize, are awarded to scientists who rigorously reanalyze other people’s data to learn something new.

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Presentation Thu, 28 Jan 2021 09:51:43 -0500 2021-02-22T16:00:00-05:00 2021-02-22T17:00:00-05:00 Off Campus Location Michigan Institute for Data Science Presentation Casey Greene
Toxic Equilibrium: Structural Racism and Population Health Inequities (February 24, 2021 10:00am) https://events.umich.edu/event/81748 81748-20949404@events.umich.edu Event Begins: Wednesday, February 24, 2021 10:00am
Location: Off Campus Location
Organized By: Institute for Social Research

February 24, 2021
10:00am – 6:30pm
Eastern Time

The American social structure is composed of a resilient, symbiotic network of the formal and informal institutions that operate to maintain an equilibrium toward White privilege. Across time and place, changes in one institution can reverberate through other institutions, and importantly, when we attempt to intervene toward equity in one institution, other institutions can move to restore this toxic equilibrium. Cultural racism, which encompasses the socially accepted ideologies, values, and behavioral norms determined by the dominant power group, sets this equilibrium. Particularly insidious as it operates on the level of our shared social subconscious, the processes that comprise cultural racism are invisible to many because they are our “givens”, our assumptions, our defaults – but the result shapes our answers to the question: Whose life counts?

For our 6th annual University of Michigan RacismLab Symposium on the Study of Racism, we pay tribute to the legacy of Dr. James Jackson, whose mentorship guided our 1st annual symposium in 2015 and resulted in our guest edited Social Science and Medicine special issue on cultural and structural racism. In the introduction to this special issue, we called for all scholarship on race and health to be grounded in interdisciplinary frameworks of cultural and structural racism and critical race theory.

Our annual symposium continues to be sponsored by the University of Michigan Survey Research Center at the Institute for Social Research. For our virtual meeting in 2021, we partner with the Interdisciplinary Association for Population Health Science (IAPHS) to move our discussions to a national stage. As we move to a national, interdisciplinary discussion, we are honored that a pioneer in the study of structural racism, Dr. Eduardo Bonilla Silva will serve as the keynote speaker this year.

Please register for this event: https://iaphs.org/tools-for-success/online-events/racismlab/racismlab-registration/

Event link will be provided upon registration.

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Conference / Symposium Wed, 17 Feb 2021 15:24:54 -0500 2021-02-24T10:00:00-05:00 2021-02-24T18:30:00-05:00 Off Campus Location Institute for Social Research Conference / Symposium poster
Data for Public Good Symposium (February 25, 2021 9:45am) https://events.umich.edu/event/81262 81262-20879894@events.umich.edu Event Begins: Thursday, February 25, 2021 9:45am
Location: Off Campus Location
Organized By: Ginsberg Center

As both consumers and purveyors of information, how we interact with data is ever evolving. Now, more than ever, data for good represents a diverse and interdisciplinary effort to engage, educate, and empower the world around us. Statistics in the Community (STATCOM), the Center for Education Design, Evaluation, and Research (CEDER), and the Community Technical Assistance Collaborative (CTAC) invite you to attend the 4th annual Data for Public Good Symposium hosted by the Michigan Institute for Data Science. The symposium will launch virtually on Thursday, February 25, 2021 and will showcase the many research efforts and university/community partnerships that focus on improving humanity by using data for the public good.

Data for Good in Changing Times

This year’s symposium will focus on how data can help us best predict, catalyze, or respond to large-scale societal and environmental change. As we currently navigate these changes in several key areas, this symposium offers an opportunity to learn about the data-driven work that is being done to address the following challenges and develop skills to implement your own data-driven work in these spaces:

Public health and medicine
Social justice and equity
Politics and civic engagement
K-12 and higher education
Climate change and sustainability

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Conference / Symposium Tue, 26 Jan 2021 13:07:14 -0500 2021-02-25T09:45:00-05:00 2021-02-25T16:00:00-05:00 Off Campus Location Ginsberg Center Conference / Symposium 4th Annual Data for Public Good Symposium
Anil Yildirim (Aerospace Engineering) and Jiale Tan (Epidemiology) (February 25, 2021 4:00pm) https://events.umich.edu/event/81478 81478-20895806@events.umich.edu Event Begins: Thursday, February 25, 2021 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Computational Discovery and Engineering

ANIL YILDIRIM: Anil Yildirim is a PhD candidate in Aerospace Engineering and Scientific Computing. His research focuses on the development and application of robust computational tools in the context of multidisciplinary design optimization for aircraft configurations.

"ROBUST AND HIGH-PERFORMANCE TOOLS FOR MULTIDISCIPLINARY DESIGN OPTIMIZATION": The development of future sustainable aircraft heavily relies on the design and integration of advanced propulsion systems. However, the design of these systems are challenging due to the tightly coupled interactions between the aerodynamic and the propulsion disciplines. My research focuses on enabling these advanced technologies using aeropropulsive design optimization, in which the aerodynamic and propulsion system designs are optimized in a coupled manner. In this process, I use multiple robust and high-performance computational tools including the computational fluid dynamics (CFD) solver we have been developing in the MDO Lab at the University of Michigan. In this talk, I will cover some recent advancements in the field of CFD-based aeropropulsive design optimization and the computational methodologies we have been using for this work.

JIALE TAN: Jiale is a second year Phd student working with Prof. Rafael Meza in Epidemiology. His interest is to apply computational skills to public health challenges so that he can develop and apply modeling techniques for infectious and noninfectious diseases, including for viral infections like HIV and HCV, and eventually use them for modeling non-communicable diseases that disproportionately affect global health like cancer.

"MARKOV MULTISTATE TRANSITION MODEL ON ELECTRONIC NICOTINE DELIVERY SYSTEMS AND TRADITIONAL CIGARETTES": Electronic nicotine delivery systems (ENDS) have dramatically changed the landscape of tobacco products patterns in the USA since 2011. The impact of ENDS use on traditional cigarettes smoking remains a topic of considerable debate. A Markov multistate transition model was used to estimate transition rates (Hazard rate) between ENDS and cigarette use states (25 use states); never user, non-current experimental user, non-current regular user, current experimental user, and current regular user for each product. A 25×25 transition matrix was generated from this model. Parallel computations using 150 processors was used to estimate the transition rates. The Population Assessment of Tobacco and Health study, which includes longitudinal data from 11,475 youth of ages 12 to 24 years from 2013-2018 was used to calibrate the model. The hazard estimates show the patterns of ENDS and cigarette use experimentation and transition to regular use. Next steps will assess the impact of different sociodemographic covariates (age, sex, race, education, household income) on the estimated transition rates.

Register to receive Zoom information: https://umich.zoom.us/meeting/register/tJUtce2gqDkuE9chnr5NMrBGjYgeXsl-fyJX

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Workshop / Seminar Thu, 28 Jan 2021 17:13:06 -0500 2021-02-25T16:00:00-05:00 2021-02-25T17:00:00-05:00 Off Campus Location Michigan Institute for Computational Discovery and Engineering Workshop / Seminar A. Yildirim and J. Tan
Data for Public Good Symposium (February 26, 2021 9:45am) https://events.umich.edu/event/81262 81262-20879895@events.umich.edu Event Begins: Friday, February 26, 2021 9:45am
Location: Off Campus Location
Organized By: Ginsberg Center

As both consumers and purveyors of information, how we interact with data is ever evolving. Now, more than ever, data for good represents a diverse and interdisciplinary effort to engage, educate, and empower the world around us. Statistics in the Community (STATCOM), the Center for Education Design, Evaluation, and Research (CEDER), and the Community Technical Assistance Collaborative (CTAC) invite you to attend the 4th annual Data for Public Good Symposium hosted by the Michigan Institute for Data Science. The symposium will launch virtually on Thursday, February 25, 2021 and will showcase the many research efforts and university/community partnerships that focus on improving humanity by using data for the public good.

Data for Good in Changing Times

This year’s symposium will focus on how data can help us best predict, catalyze, or respond to large-scale societal and environmental change. As we currently navigate these changes in several key areas, this symposium offers an opportunity to learn about the data-driven work that is being done to address the following challenges and develop skills to implement your own data-driven work in these spaces:

Public health and medicine
Social justice and equity
Politics and civic engagement
K-12 and higher education
Climate change and sustainability

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Conference / Symposium Tue, 26 Jan 2021 13:07:14 -0500 2021-02-26T09:45:00-05:00 2021-02-26T16:00:00-05:00 Off Campus Location Ginsberg Center Conference / Symposium 4th Annual Data for Public Good Symposium
MIDAS Seminar Series Presents: Simine Vazire, Psychology, University of Melbourne (March 1, 2021 4:00pm) https://events.umich.edu/event/81079 81079-20846537@events.umich.edu Event Begins: Monday, March 1, 2021 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

How can we tell which scientific findings are credible? Peer-reviewed journals, even prestigious ones, do not provide much assurance regarding the credibility of any individual report. Ideally, we would read each report carefully when deciding what to trust, but this is often impossible (e.g., when we lack the expertise to evaluate the methods) or impractical (e.g., when we need to evaluate research at scale). Moreover, rather than each of us making private judgments, we would all benefit from collecting and sharing evaluations from a range of experts with different areas of expertise and different blind spots and biases. The ideal would be to validate a rubric for eliciting structured quantitative ratings of quality along a wide range of dimensions, and collect and make publicly available ratings from many different and diverse experts. These scores could be combined into a variety of metrics, or “Quality Factors” (QFs), that vary in the weight placed on different qualities. These QFs would provide easily digestible and flexible quality ratings of individual scientific papers that could be useful to other scientists, to journalists and policymakers, and to the public. QFs would also help incentivize authors to “get it right” rather than just get published in prestigious journals, because rewards and recognition could be tied to these more transparent, accountable, and valid metrics rather than to journal prestige. In this talk, I discuss what this could look like for my home discipline of psychology, and describe some progress towards producing Quality Factors for psychology papers.


Simine Vazire is an associate professor in the department of psychology at the University of Melbourne. She is the director of the Personality and Self-Knowledge laboratory. She is the co-founder and current president of the Society for the Improvement of Psychological Science, a senior editor at Collabra: Psychology, and editor in chief of Social Psychological and Personality Science. Her research is funded by the National Science Foundation, and examines accuracy and bias in people’s perceptions of their own behavior and personality. She also conducts meta-science examining how people interpret scientific findings, and tracking trends in the methods and results of published studies in psychology over time. She teaches and blogs about research methods and reproducibility.

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Presentation Fri, 22 Jan 2021 10:17:46 -0500 2021-03-01T16:00:00-05:00 2021-03-01T17:00:00-05:00 Off Campus Location Michigan Institute for Data Science Presentation Simine Vazire
ISR Insights Speaker Series – Chatter: The Voice in Our Head, Why It Matters, and How to Harness It (March 3, 2021 11:00am) https://events.umich.edu/event/81973 81973-20998841@events.umich.edu Event Begins: Wednesday, March 3, 2021 11:00am
Location: Off Campus Location
Organized By: Institute for Social Research

ISR Insights Speaker Series is a series focusing on the research happening at ISR.

Ethan Kross (Faculty Associate, Research Center for Group Dynamics; Professor, Management & Organizations Area, Ross School of Business; Professor, Department of Psychology, LSA)

Wednesday, March 3, 11am EST: https://umich.zoom.us/j/95763691351

Tell a stranger that you talk to yourself, and you’re likely to get written off as eccentric. But the truth is that we all have a voice in our head. In this ISR Insights talk, University of Michigan professor Ethan Kross joins Dave Mayer (Ross School of Business) to discuss Kross’ new book, Chatter. Interweaving behavioral and brain research from Kross’ lab with colorful real-world case studies, Kross explains how these conversations shape our lives, work, and relationships.

This talk is co-sponsored by Literati Bookstore, where you can purchase Kross’ new book: https://www.literatibookstore.com/book/9780525575238

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Lecture / Discussion Wed, 10 Feb 2021 16:23:00 -0500 2021-03-03T11:00:00-05:00 2021-03-03T12:00:00-05:00 Off Campus Location Institute for Social Research Lecture / Discussion flyer
An Introduction to CJARS: A new data platform for integrated criminal justice research (March 5, 2021 2:00pm) https://events.umich.edu/event/81913 81913-20990884@events.umich.edu Event Begins: Friday, March 5, 2021 2:00pm
Location: Off Campus Location
Organized By: Institute for Social Research

Webinar and Live Q&A

CJARS is a next generation data platform built on over 2+ billion lines of raw data, looking to transform criminal justice research and statistical reporting as we know it. The system, which grows each and everyday, currently contains over 133 million criminal justice events from arrest to parole, occurring in 18 states, covering over 33 million individuals. All of this data can be integrated at the individual level with extensive, longitudinal socio-economic data in partnership with the U.S. Census Bureau.

Topics to include:
- Contents and coverage of CJARS data infrastructure
- Comparison to existing BJS statistical series
- Opportunities for data linkage in the Federal Statistical Research
Data Center network
- Application process to work with CJARS data
- Resources available to support early-stage researchers

Interested researchers should register: https://forms.gle/xgmobvXtbLKKRFSPA
(Event link will be provided after registering)

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Livestream / Virtual Tue, 09 Feb 2021 12:30:48 -0500 2021-03-05T14:00:00-05:00 2021-03-05T15:00:00-05:00 Off Campus Location Institute for Social Research Livestream / Virtual CJARS - Criminal Justice Administrative Records System
MIDAS Seminar Series and Michigan AI Initiative Co-Present: Heng Ji, University of Illinois Urbana Champaign (March 8, 2021 4:00pm) https://events.umich.edu/event/81082 81082-20846538@events.umich.edu Event Begins: Monday, March 8, 2021 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

To combat COVID-19, clinicians and scientists all need to digest the vast amount of relevant biomedical knowledge in literature to understand the disease mechanism and the related biological functions. The first challenge is quantity. For example, nearly 2.7K new papers are published at PubMed per day. This knowledge bottleneck causes significant delay in the development of vaccines and drugs for COVID-19. The second challenge is quality due to the rise and rapid, extensive publications of preprint manuscripts without pre-publication peer review. Many research results about coronavirus from different research labs and sources are redundant, complementary or event conflicting with each other.

Let’s consider drug repurposing as a case study. Besides the long process of clinical trial and biomedical experiments, another major cause for the long process is the complexity of the problem involved and the difficulty in drug discovery in general. The current clinical trials for drug re-purposing mainly rely on symptoms by considering drugs that can treat diseases with similar symptoms. However, there are too many drug candidates and too much misinformation published from multiple sources. In addition to a ranked list of drugs, clinicians and scientists also aim to gain new insights into the underlying molecular cellular mechanisms on Covid-19, and which pre-existing conditions may affect the mortality and severity of this disease.

To tackle these two challenges, we have developed a novel and comprehensive knowledge discovery framework, COVID-KG, to accelerate scientific discovery and build a bridge between clinicians and biology scientists. COVID-KG starts by reading existing papers to build multimedia knowledge graphs (KGs), in which nodes are entities/concepts and edges represent relations involving these entities, extracted from both text and images. Given the KGs enriched with path ranking and evidence mining, COVID-KG answers natural language questions effectively. Using drug repurposing as a case study, for 11 typical questions that human experts aim to explore, we integrate our techniques to generate a comprehensive report for each candidate drug. Preliminary assessment by expert clinicians and medical school students show our generated reports are informative and sound. I will also talk about our ongoing work to extend this framework to other domains including molecular synthesis and agriculture.

Bio:

Heng Ji is a professor at Computer Science Department, and an affiliated faculty member at Electrical and Computer Engineering Department of University of Illinois at Urbana-Champaign. She is also an Amazon Scholar. She received her B.A. and M. A. in Computational Linguistics from Tsinghua University, and her M.S. and Ph.D. in Computer Science from New York University. Her research interests focus on Natural Language Processing, especially on Multimedia Multilingual Information Extraction, Knowledge Base Population and Knowledge-driven Generation. She was selected as “Young Scientist” and a member of the Global Future Council on the Future of Computing by the World Economic Forum in 2016 and 2017. The awards she received include “AI’s 10 to Watch” Award by IEEE Intelligent Systems in 2013, NSF CAREER award in 2009, Google Research Award in 2009 and 2014, IBM Watson Faculty Award in 2012 and 2014 and Bosch Research Award in 2014-2018, and ACL2020 Best Demo Paper Award. She was invited by the Secretary of the U.S. Air Force and AFRL to join Air Force Data Analytics Expert Panel to inform the Air Force Strategy 2030. She is the lead of many multi-institution projects and tasks, including the U.S. ARL projects on information fusion and knowledge networks construction, DARPA DEFT Tinker Bell team and DARPA KAIROS RESIN team. She has coordinated the NIST TAC Knowledge Base Population task since 2010. She has served as the Program Committee Co-Chair of many conferences including NAACL-HLT2018. She is elected as the North American Chapter of the Association for Computational Linguistics (NAACL) secretary 2020-2021. Her research has been widely supported by the U.S. government agencies (DARPA, ARL, IARPA, NSF, AFRL, DHS) and industry (Amazon, Google, Bosch, IBM, Disney).

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Presentation Mon, 25 Jan 2021 17:32:08 -0500 2021-03-08T16:00:00-05:00 2021-03-08T17:00:00-05:00 Off Campus Location Michigan Institute for Data Science Presentation Heng Li
An Introduction to CJARS: A new data platform for integrated criminal justice research (March 9, 2021 10:30am) https://events.umich.edu/event/81913 81913-20990885@events.umich.edu Event Begins: Tuesday, March 9, 2021 10:30am
Location: Off Campus Location
Organized By: Institute for Social Research

Webinar and Live Q&A

CJARS is a next generation data platform built on over 2+ billion lines of raw data, looking to transform criminal justice research and statistical reporting as we know it. The system, which grows each and everyday, currently contains over 133 million criminal justice events from arrest to parole, occurring in 18 states, covering over 33 million individuals. All of this data can be integrated at the individual level with extensive, longitudinal socio-economic data in partnership with the U.S. Census Bureau.

Topics to include:
- Contents and coverage of CJARS data infrastructure
- Comparison to existing BJS statistical series
- Opportunities for data linkage in the Federal Statistical Research
Data Center network
- Application process to work with CJARS data
- Resources available to support early-stage researchers

Interested researchers should register: https://forms.gle/xgmobvXtbLKKRFSPA
(Event link will be provided after registering)

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Livestream / Virtual Tue, 09 Feb 2021 12:30:48 -0500 2021-03-09T10:30:00-05:00 2021-03-09T11:30:00-05:00 Off Campus Location Institute for Social Research Livestream / Virtual CJARS - Criminal Justice Administrative Records System
MIDAS Seminar Series Presents: Patricia Murrieta-Flores, Lancaster University (March 15, 2021 4:00pm) https://events.umich.edu/event/82623 82623-21147749@events.umich.edu Event Begins: Monday, March 15, 2021 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

The field of Digital Humanities, and particularly the increasing accessibility of digital resources, has opened a significant number of opportunities for the study of sources that can be highly relevant to history and archaeology. These opportunities include the use of methodologies from the fields of Artificial Intelligence and Computational Linguistics and the application of a diversity of techniques and methods for the large-scale analysis and exploration of collections of historical documents.

In the case of the early colonial history of Mexico, there is an enormous variety of historical documents related to the economic, social and political life at that time. An example of this is the sixteenth-century Relaciones Geográficas de Nueva España (the Geographic Reports of New Spain). Created from the responses to a questionnaire ordered by Philip II’s and obtained between 1577 and 1585, the Geographic Reports sought to compile all the information available on the American territories under Spanish rule. Due to its essential content, these reports have been the object of study by a large number of researchers, and are frequently used in the analysis of the political, social, territorial and economic situation at the time. Although numerous studies seek to understand the shifting territorial situation in New Spain, two enormous challenges have remained. The first one is the considerable size or volume of information to be analysed and compared. The second has been the precise identification of the places mentioned in these reports, especially on a large scale.

In this presentation, I will introduce the project sponsored by the Transatlantic Platform for the Humanities and Social Sciences (T-AP) called “Digging into Early Colonial Mexico: a large-scale computational analysis of historical documents”, and some of its results. Taking as a basis the historical corpus of the Geographic Reports of New Spain, the project main objectives have been: 1) to adapt and develop techniques from Artificial Intelligence, including aspects of Natural Language Processing, Text Mining and Geographic Information Systems for the extraction and analysis of historical information from this source, and 2) to design computational methodologies for the identification of possible large-scale historical patterns. This research is allowing us to clarify some of the essential geographic questions related to the period and the colonial situation in this territory. I will also present a methodology termed Geographical Text Analysis and some of the most critical outputs from this project. These include software developed to carry out this type of analysis, the first sixteenth-century digital gazetteer of Mexico and Guatemala, and the first experiments using Natural Language Processing to automatically annotate the Relaciones corpus.

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Presentation Mon, 01 Mar 2021 13:19:07 -0500 2021-03-15T16:00:00-04:00 2021-03-15T17:00:00-04:00 Off Campus Location Michigan Institute for Data Science Presentation Patricia Murrieta-Flores
MIDAS Seminar Series and Michigan AI Initiative Co-Present: Mona Diab, Computer Science, George Washington University (March 22, 2021 4:00pm) https://events.umich.edu/event/81039 81039-20838681@events.umich.edu Event Begins: Monday, March 22, 2021 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Advances in machine learning have led to quite fluent natural language generation technologies. Most of our current optimizations and evaluations focus on accuracy in output. Faithful generation is considered a nice to have, a luxury. In this talk I make the argument that faithful generation is crucial to our generation technologies especially given the scale and impact NLP technologies have on people’s lives.

Mona Diab is a Full Professor of Computer Science at the George Washington University where she directs the Care4Lang NLP lab. She is also Research Scientist with Facebook AI. She conducts research in Statistical Natural Language Processing (NLP) is a rapidly growing, exciting field of research in artificial intelligence and computer science. Interdisciplinarity is inherent to NLP, drawing on the fields of computer algorithms, software engineering, statistics, machine learning, linguistics, pragmatics, information technology, etc. In NLP, researchers model language and its use, and build both analytical models and predictive ones. In Professor Diab’s NLP lab, they address problems in social media processing, building robust enabling technologies such as syntactic and semantic processing tools for written texts in different languages, information extraction tools for large data, multilingual processing, machine translation, and computational sociolinguistic processing. Professor Diab has a special interest in Arabic NLP, where the emphasis has been on investigating Arabic dialect processing where there are very few available automated resources.

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Presentation Tue, 09 Feb 2021 11:13:10 -0500 2021-03-22T16:00:00-04:00 2021-03-22T17:00:00-04:00 Off Campus Location Michigan Institute for Data Science Presentation Mona Diab
James S. Jackson’s Continuing Legacy and Contributions to Social and Behavioral Research on Black Americans (March 24, 2021 1:00pm) https://events.umich.edu/event/82484 82484-21108104@events.umich.edu Event Begins: Wednesday, March 24, 2021 1:00pm
Location: Off Campus Location
Organized By: Institute for Social Research

ISR Insights Speaker Series – James S. Jackson’s Continuing Legacy and Contributions to Social and Behavioral Research on Black Americans

Wednesday, March 24, 1pm EST. https://umich.zoom.us/j/99879554198

Panelists: Robert Taylor (Harold R Johnson Endowed Professor of Social Work, Sheila Feld Collegiate Professor of Social Work, School of Social Work, and Faculty Associate, RCGD); Belinda Tucker (Professor Emerita of Psychiatry & Biobehavioral Sciences, and the Special Liaison for Faculty Development, UCLA); and Phillip Bowman (Professor, Higher and Postsecondary Education at the U-M International Institute)

Join Robert Taylor, Belinda Tucker, and Phillip Bowman for a panel discussion on the continuing legacy and contributions of James S. Jackson.

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Lecture / Discussion Wed, 24 Feb 2021 16:45:04 -0500 2021-03-24T13:00:00-04:00 2021-03-24T14:00:00-04:00 Off Campus Location Institute for Social Research Lecture / Discussion event flyer
Decennial Census Digitization and Linkage Project (March 29, 2021 12:00pm) https://events.umich.edu/event/80205 80205-20596107@events.umich.edu Event Begins: Monday, March 29, 2021 12:00pm
Location: Off Campus Location
Organized By: Institute for Social Research

Contact PSC Office for Zoom details.

The Decennial Census Digitization and Linkage project (DCDL) will digitize and link individual records across the 1960-1990 censuses and create tools to improve the dissemination of these data. When combined with already-available linkages between the censuses of 1940, 2000, 2010, and soon-to-be 2020, DCDL will complete a massive longitudinal data infrastructure covering almost the entire U.S. population since 1940. The resulting data resource will provide transformational opportunities for research, education, and evidence-building across the social, behavioral, and economic sciences. I'll describe the project's innovative methods of data rescue, record linkage, and restricted data access.

BIO:
J. Trent Alexander is the Associate Director and a Research Professor at ICPSR in the Institute for Social Research at the University of Michigan. Alexander's research focuses on historical demography and large-scale data infrastructures. Prior to coming to ICPSR in 2017, Alexander initiated the Census Longitudinal Infrastructure Project at the Census Bureau and managed the Integrated Public Use Microdata Series (IPUMS) at the University of Minnesota.

Population Studies Center (PSC) Brown Bag seminars highlight recent research in population studies and serve as a focal point for building our research community.

Contact PSC Office for Zoom details.

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Workshop / Seminar Tue, 16 Mar 2021 12:57:06 -0400 2021-03-29T12:00:00-04:00 2021-03-29T13:00:00-04:00 Off Campus Location Institute for Social Research Workshop / Seminar Flyer for Brown Bag seminar
MIDAS Seminar Series Presents: Anne Plant, NIST Fellow, National Institute of Standards and Technology (March 29, 2021 4:00pm) https://events.umich.edu/event/82212 82212-21054518@events.umich.edu Event Begins: Monday, March 29, 2021 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

While reproducibility can be an important hallmark of good science, it is not often the most important indicator. The discipline of metrology, or measurement science, describes a measurement result as a value and the uncertainty around that value. We propose a systematic process for considering the sources of uncertainty in a scientific study that can be applied to virtually all

disciplines of scientific research. We suggest that a research study can be characterized by how sources of uncertainty in the study are reported and mitigated. This approach provides a path for sharing experimental data on complex systems such as biological network processes. A serious challenge for such studies involves collecting experimental metadata and protocol details.

Bio:

Dr. Plant is currently a NIST Fellow, focusing on cell imaging and theoretical frameworks for understanding complex biological response in cells. She is an ex officio member of the NIBIB National Advisory Council, a Fellow of the AIMBE, and an AAAS Fellow. She previously served as Chief of the Biosystems and Biomaterials Division at NIST, and in the White House Office of Science and Technology Policy.

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Workshop / Seminar Wed, 24 Feb 2021 11:02:58 -0500 2021-03-29T16:00:00-04:00 2021-03-29T17:00:00-04:00 Off Campus Location Michigan Institute for Data Science Workshop / Seminar Anne Plant
MIDAS Webinar Series Presents: Vipin Kumar, University of Minnesota (April 5, 2021 4:00pm) https://events.umich.edu/event/81083 81083-20846543@events.umich.edu Event Begins: Monday, April 5, 2021 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Bio:

Vipin Kumar is a Regents Professor at the University of Minnesota, where he holds the William Norris Endowed Chair in the Department of Computer Science and Engineering. Kumar received the B.E. degree in Electronics & Communication Engineering from Indian Institute of Technology Roorkee (formerly, University of Roorkee), India, in 1977, the M.E. degree in Electronics Engineering from Philips International Institute, Eindhoven, Netherlands, in 1979, and the Ph.D. degree in Computer Science from University of Maryland, College Park, in 1982. He also served as the Head of the Computer Science and Engineering Department from 2005 to 2015 and the Director of Army High Performance Computing Research Center (AHPCRC) from 1998 to 2005.

Kumar’s current research interests span data mining, high-performance computing, and their applications in Climate/Ecosystems and health care. His research has resulted in the development of the concept of isoefficiency metric for evaluating the scalability of parallel algorithms, as well as highly efficient parallel algorithms and software for sparse matrix factorization (PSPASES) and graph partitioning (METIS, ParMetis, hMetis). He has authored over 300 research articles, and has coedited or coauthored 10 books including two text books “Introduction to Parallel Computing” and “Introduction to Data Mining”, that are used world-wide and have been translated into many languages. Kumar’s current major research focus is on bringing the power of big data and machine learning to understand the impact of human induced changes on the Earth and its environment. Kumar served as the Lead PI of a 5-year, $10 Million project,”Understanding Climate Change – A Data Driven Approach”, funded by the NSF’s Expeditions in Computing program that is aimed at pushing the boundaries of computer science research.

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Presentation Fri, 22 Jan 2021 10:38:20 -0500 2021-04-05T16:00:00-04:00 2021-04-05T17:00:00-04:00 Off Campus Location Michigan Institute for Data Science Presentation Vipin Kumar
CSAAW Seminar Presents: "Improving causal inference controls using network theory in discrete choice data" (April 8, 2021 12:00pm) https://events.umich.edu/event/83617 83617-21438455@events.umich.edu Event Begins: Thursday, April 8, 2021 12:00pm
Location: Off Campus Location
Organized By: The Center for the Study of Complex Systems

Meeting Link: https://umich.zoom.us/j/93338242486
Passcode: csaaw2021
Phone ID: 933 3824 2486
Phone Passcode: 400052931

Abstract: Many datasets in social sciences are a result of agents making repeated choices over time, with some observable outcome resulting from each choice. Researchers often want to model the causal impact of covariates on the outcome variable using different estimation strategies (e.g. fixed effects regression, difference-in-differences, instrumental variables, etc). I propose a way to increase control in these estimation procedures by using network theory models motivated by a discrete choice framework. I suggest a bi-partite network representation of these datasets, with agents being nodes on one side of the network and choices being nodes on the other side of it. Edges in this network represent a choice made by an agent at a certain time, resulting from a discrete choice problem. I argue that the structure of connections in this choice-network allows the researcher to further improve controls when modelling the outcome variable. For instance, I use the choice-network to project agents in a multidimensional latent space that captures each agent's choice-profile and distances between agents in this latent space represent a metric of similarity between them. I propose exploring the high-dimensional choice-profile of agents to improve causal inference exercises in a series of ways.

Bernardo Modenesi is a Ph.D. candidate in Economics, also pursuing a masters degree in Statistics, at the University of Michigan. Bernardo's interests lie in interdisciplinary statistical methods, such as network theory and machine learning, for the improvement of causal inference exercises and economic modelling.

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Workshop / Seminar Wed, 07 Apr 2021 14:44:48 -0400 2021-04-08T12:00:00-04:00 2021-04-08T13:00:00-04:00 Off Campus Location The Center for the Study of Complex Systems Workshop / Seminar Bernardo Modenesi
MIDAS Seminar Series Presents: Vicki Bogan, Economics and Management, Cornell University (April 12, 2021 4:00pm) https://events.umich.edu/event/82466 82466-21106115@events.umich.edu Event Begins: Monday, April 12, 2021 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

We provide empirical evidence that visceral factors affect financial risk taking by showing that exposure to mass shootings alters mutual fund managers’ risk taking decisions. Funds that are exposed to mass shootings subsequently decrease risk relative to their peers. The effect that we document is temporary, lasting approximately one quarter before reverting to normal levels and is strongest among managers with demographics shown to express greater fear from mass shootings. Together with the literature on laboratory studies that show that market downturns induce fear, our findings suggest that fear could exacerbate variation in risk taking, generating the highly volatile countercyclical risk premiums shown to exist in markets.

Bio:

Vicki Bogan’s research interests are in the areas of financial economics, behavioral finance, and applied microeconomics centering on issues involving investment decision making behavior and financial markets. She explores questions relating to investment decision making (corporate and individual) and household portfolio allocation with the goal of shedding light on how to better model observed behavior.

Bogan has published numerous journal articles and book chapters including a book chapter on “Household Investment Decisions,” in Investor Behavior: The Psychology of Financial Planning and Investing. Bogan’s research has received considerable media attention including radio interviews and coverage in Forbes.com, the Wall Street Journal website, NPR’s Marketplace Tech, PsychologyToday.com, and the Harvard Business Review Blog. She also has been featured on the PBS News Hour – Paul Solman’s Making Sense, the Lou Hutt Show on Sirius XM radio, and Knowledge@Wharton on Sirius XM radio.

Bogan currently serves as Co-Editor for Financial Planning Review. She is the founder and director for the Institute for Behavioral and Household Finance. She also worked as a consultant for Hartford Funds Management Group, Inc.

Prof. Bogan teaches finance courses for master’s and undergraduate students in the Dyson School of Cornell University’s SC Johnson College of Business. She has received two outstanding educator awards and the SUNY Chancellor’s Award for Excellence in Teaching.

Bogan holds a Sc.B. degree in Applied Mathematics and Economics from Brown University, an M.B.A. in Finance and Strategic Management from the Wharton School of the University of Pennsylvania, an M.A. in Economics from Brown University, and a Ph.D. in Economics from Brown University. She also has held a visiting fellow appointment at Princeton University.

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Presentation Wed, 24 Feb 2021 11:25:34 -0500 2021-04-12T16:00:00-04:00 2021-04-12T17:00:00-04:00 Off Campus Location Michigan Institute for Data Science Presentation Vicki Bogan
MIDAS Seminar Series Presents: Ben Wellington, Quantitative Analyst, Two Sigma (April 19, 2021 4:00pm) https://events.umich.edu/event/81088 81088-20846549@events.umich.edu Event Begins: Monday, April 19, 2021 4:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Ben Wellington is a Quantitative Analyst at Two Sigma and the creator of I Quant NY, a data science and policy blog that focuses on insights drawn from New York City’s public data, and advocates for the expansion and improvement of that data. His data science has influenced local government policy including changes in NYC street infrastructure, the way New Yorkers pay for cabs and the design of NYC subway vending machines. Ben is a contributor to the New Yorker, and a visiting assistant professor in the City & Regional Planning program at the Pratt Institute in Brooklyn where he teaches statistics using urban open data and holds a Ph.D. in Computer Science from New York University.

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Presentation Fri, 22 Jan 2021 11:05:47 -0500 2021-04-19T16:00:00-04:00 2021-04-19T17:00:00-04:00 Off Campus Location Michigan Institute for Data Science Presentation Ben Wellington
Data Science Coast to Coast: Data Equity and Open Science (April 21, 2021 3:00pm) https://events.umich.edu/event/83774 83774-21503041@events.umich.edu Event Begins: Wednesday, April 21, 2021 3:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Data Science

Please register: https://academicdatascience.org/resources/coast2coastseminar

H. V. Jagadish, Director, Michigan Institute for Data Science; Bernard A Galler Collegiate Professor of Electrical Engineering and Computer Science, University of Michigan

Data Equity: A Core Requirement for Responsible Data Science

It was only recently that we regularly used to hear statements like “Let the data speak for themselves”. Today, we instead hear worries about fairness of data-driven systems and AI. Nevertheless, a focus on a specific formulation of fairness in one data science step is far too narrow to be the whole story. We need to address inequitable representation in the data record, inequities due to the data scientist’s world view being reflected in the model, inequities in the resulting outcomes, and inequities in access to fruits of the analysis. In this talk, I will lay out a research agenda in this direction, and invite you to join me.

Ciera Martinez, Biodiversity and Environmental Sciences Lead, Berkeley Institute for Data Science, University of California – Berkeley

Open science in the wild: principles to build reproducible and collaborative data analysis workflows

The academic research system is not built to incentivize open science practices, but transparency and reproducible methodology allows researchers to critically assess and build upon results to fuel scientific discovery and supports a more collaborative and equitable research community. Open science and data practices are often presented as ideals, but rarely do we train for how to handle the intricacies that emerge from every unique research project life cycle. In this talk I will present the ERP (Explore, Refine, and Produce) workflow – a three-phase data analysis workflow that guides researchers to create reproducible and responsible data analysis workflows. Each phase is centered on how to make decisions based on the audience the research is communicated, the research products created, and the career aspirations of the researchers involved. We hope this work helps create a community of practice for how we design and train for reproducible data intensive research and helps demystify data analysis for both students new to research and current researchers who are new to data-intensive work.

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Presentation Thu, 15 Apr 2021 19:53:35 -0400 2021-04-21T15:00:00-04:00 2021-04-21T16:00:00-04:00 Off Campus Location Michigan Institute for Data Science Presentation Data Equity and Open Science
Examining the impact of COVID-19 on adults with physical disabilities from marginalized communities (April 22, 2021 2:00pm) https://events.umich.edu/event/83637 83637-21446269@events.umich.edu Event Begins: Thursday, April 22, 2021 2:00pm
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

This webinar will present the findings from a recent study on the initial impact of the COVID-19 pandemic on adults with physical disabilities from marginalized communities in Southeast Michigan, one of the early pandemic epicenters in the United States. Interviews with 16 adults revealed how participants either had to engage in risky behavior to have their needs met or avoid risk and not have those needs met. They contribute to understandings of risk, its impact on physical and psychological health, and the importance of accommodations. The study expands insight into early responses to the pandemic among individuals with long-term physical disabilities from marginalized communities. It helps elucidate how socioeconomic status and race/ethnicity can differentially impact the lives of adults with physical disabilities and further marginalize a population that is “always already” vulnerable. This knowledge can expand awareness and appreciation of how social, economic, and political systems are structured and integrated into future clinical guidelines and emergency response policies and more adequately addressed.

This webinar is free and open to the public. Communication Access Realtime Translation services will be available to provide live closed captions for the event.

The content of this webinar has been developed under a grant from the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR #90RTHF0001). NIDILRR is a Center within the Administration for Community Living (ACL), Department of Health and Human Services (HHS). The contents of this webinar do not necessarily represent the policy of NIDILRR, ACL, or HHS and you should not assume endorsement by the Federal Government.

Register at https://umich.zoom.us/webinar/register/WN_8RIqY8GES1q8EeoSya0JCQ.

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Presentation Thu, 08 Apr 2021 15:28:26 -0400 2021-04-22T14:00:00-04:00 2021-04-22T15:00:00-04:00 Off Campus Location Inter-university Consortium for Political and Social Research Presentation Blue and white promotional flyer for UofM IDEAL RRTC Webinar - Examining Impact of COVID19 on Adults with Physical Disability from Marginalized Communities
Stress and Health in Context: The Role of Negative Relationships (April 28, 2021 1:00pm) https://events.umich.edu/event/83765 83765-21501085@events.umich.edu Event Begins: Wednesday, April 28, 2021 1:00pm
Location: Off Campus Location
Organized By: Institute for Social Research

Stress and Health in Context: The Role of Negative Relationships
Wednesday, April 28, 1pm EDT: https://umich.zoom.us/j/98477632981

A burgeoning literature shows social ties are integral for health and survival. Kira Birditt‘s program of research focuses on negative aspects of relationships including the extent to which they are irritating, critical, or demanding. In this talk she will discuss the findings from her program of research showing that: 1) There is a great deal of variability in negative aspects of relationships within and between individuals, 2) Negative aspects of relationships have important implications for health that often vary by the context of stress, and 3) The implications of relationships and stress vary race/ethnicity. She will also discuss the Aging and Biopsychosocial Innovations program that she leads and directions for future research.

Kira Birditt earned a PhD in Human Development and Family Studies from Pennsylvania State University and B.A. and M.S. degrees in Psychology from Western Washington University. She serves as a Research Associate Professor at the Survey Research Center and the Director of the Aging and Biopsychosocial Innovations Program. She is currently PI on three R01 projects funded by NIA examining: 1) racial health disparities in stress, social ties and health, 2) racial inequities in health among Alzheimer’s caregivers, and 3) alcohol use and cardiovascular health among aging couples.

This webinar is part of a continuing series focusing on the research happening at ISR. If there is a topic you would like to see featured or have an idea for a future presentation, please email abeattie@umich.edu. This talk is being recorded and will be shared widely.

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Lecture / Discussion Thu, 15 Apr 2021 15:59:00 -0400 2021-04-28T13:00:00-04:00 2021-04-28T14:00:00-04:00 Off Campus Location Institute for Social Research Lecture / Discussion event flyer
Beyond the Pandemic: Challenges and Opportunities for Changes in Education (May 12, 2021 1:00pm) https://events.umich.edu/event/83937 83937-21619168@events.umich.edu Event Begins: Wednesday, May 12, 2021 1:00pm
Location: Off Campus Location
Organized By: Institute for Social Research

Beyond the Pandemic: Challenges and Opportunities for Changes in Education
Wednesday, May 12 at 1pm EDT
https://umich.zoom.us/j/96351558688

At the end of the school year in 2020 parents, educators, and researchers, wondered how to deal with the “COVID slide” related to achievement and gains in learning due to schools shifting to virtual learning across the country. What we did not know at the time is that many schools would struggle to open at all in the Fall of 2020 and online and remote learning would continue to be one of the primary ways that children were educated for the rest of the 2020-21 school year. Today, the question remains: What will parents, educators, and researchers need to consider regarding achievement and learning gains as children are likely to return to in-person schooling in Fall 2021? Dr. Pamela Davis-Kean will discuss her research on how homeschooling was discussed on social media, the issues related to “holding back” or repeating a grade in primary school, and how new proposed policies for free community college may be important for helping those in secondary education get extra time to develop skills for entry into a four-year college.

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Lecture / Discussion Fri, 30 Apr 2021 16:44:15 -0400 2021-05-12T13:00:00-04:00 2021-05-12T14:00:00-04:00 Off Campus Location Institute for Social Research Lecture / Discussion event flyer
The Arts and Cultural Production Satellite Account: New Statistics for 2018 & 2019 (May 19, 2021 1:00pm) https://events.umich.edu/event/83936 83936-21619170@events.umich.edu Event Begins: Wednesday, May 19, 2021 1:00pm
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

Join us on May 19 at 1:00 pm ET for a free webinar, “The Arts and Cultural Production Satellite Account: New Statistics for 2018 and 2019,” featuring Section Chief Paul Kern with the United States Bureau of Economic Analysis (BEA). The webinar is hosted by the National Archive of Data on Arts & Culture (NADAC), a data repository funded by the National Endowment for the Arts (NEA). Participants will get an overview of the ACPSA, learn about key findings from the latest 2019 data, and discover ways ACPSA can be used in arts research. Finally, participants will learn about the ACPSA resources available on the NEA and NADAC websites and have the opportunity to ask questions.

Link to register: https://myumi.ch/gjPWr

#nadacArtsData

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Presentation Fri, 30 Apr 2021 15:45:28 -0400 2021-05-19T13:00:00-04:00 2021-05-19T14:00:00-04:00 Off Campus Location Inter-university Consortium for Political and Social Research Presentation NADAC Webinar May 19, 2021
How to Use TransPop Data: The first national probability sample of transgender individuals in the United States (July 22, 2021 2:00pm) https://events.umich.edu/event/84448 84448-21623998@events.umich.edu Event Begins: Thursday, July 22, 2021 2:00pm
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

Join us for a virtual presentation of TransPop, the first national probability sample of transgender individuals in the United States. During this presentation you'll learn how to use these data to reach your own groundbreaking results.

The TransPop study not only describes the demographic make-up of the transgender population and their experiences related to identity and transition, but also aims to provide information on areas like health outcomes, health care access, quality of life, and experiences with discrimination.

In addition to being the first national probability sample of transgender individuals in the United States, it also includes a comparative cisgender sample. A primary goal of this study was to provide researchers with a representative sample of transgender people in the United States. The study examines a variety of health-relevant domains including health outcomes and health behaviors, experiences with interpersonal and institutional discrimination, identity, transition-related experiences, and basic demographic characteristics (age, race/ethnicity, religion, political party affiliation, marital status, employment, income, location, sex, gender, and education).

The TransPop study is available at https://doi.org/10.3886/ICPSR37938.v1.

This webinar is free and open to the public. A live transcript will be available. The webinar will be recorded, and the recording and slides will be sent to all registrants after the webinar.

Zoom FAQs for Attendees: http://myumi.ch/kx2oo

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Presentation Mon, 12 Jul 2021 17:31:28 -0400 2021-07-22T14:00:00-04:00 2021-07-22T15:00:00-04:00 Off Campus Location Inter-university Consortium for Political and Social Research Presentation Promotional image for TransPop data webinar from ICPSR featuring transgender flag on white background
Dimensions of Public Attitudes Toward the Affordable Care Act, 2010 through 2017 (July 27, 2021 7:30pm) https://events.umich.edu/event/84699 84699-21624454@events.umich.edu Event Begins: Tuesday, July 27, 2021 7:30pm
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

Join us for this Blalock lecture at 7:30 pm EDT: https://myumi.ch/ICPSR2021Blalock

The 2021 ICPSR Summer Program Blalock Lectures are virtual, free and open to the public.

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Presentation Thu, 22 Jul 2021 12:31:13 -0400 2021-07-27T19:30:00-04:00 2021-07-27T21:00:00-04:00 Off Campus Location Inter-university Consortium for Political and Social Research Presentation ICPSR Summer Program Blalock Lectures 2021
Dimensions of Public Attitudes Toward the Affordable Care Act, 2010 through 2017 (July 27, 2021 7:30pm) https://events.umich.edu/event/84752 84752-21624872@events.umich.edu Event Begins: Tuesday, July 27, 2021 7:30pm
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

Join us for this Blalock lecture at 7:30pm EDT: https://myumi.ch/ICPSR2021Blalock

The 2021 ICPSR Summer Program Blalock Lectures are virtual, free and open to the public.

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Presentation Mon, 26 Jul 2021 15:12:05 -0400 2021-07-27T19:30:00-04:00 2021-07-27T21:00:00-04:00 Off Campus Location Inter-university Consortium for Political and Social Research Presentation ICPSR Summer Program Blalock Lecture series
Analyzing Open-Ended Responses for Understanding Opinions About Presidential Candidates (July 28, 2021 7:30pm) https://events.umich.edu/event/84698 84698-21624453@events.umich.edu Event Begins: Wednesday, July 28, 2021 7:30pm
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

Join us for this Blalock lecture at 7:30pm EDT: https://myumi.ch/ICPSR2021Blalock

The 2021 ICPSR Summer Program Blalock Lectures are virtual, free and open to the public.

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Presentation Thu, 22 Jul 2021 12:28:58 -0400 2021-07-28T19:30:00-04:00 2021-07-28T21:00:00-04:00 Off Campus Location Inter-university Consortium for Political and Social Research Presentation ICPSR Blalock Lecture series 2021
Harnessing Big Data for Health and Social Science Research (August 3, 2021 12:00pm) https://events.umich.edu/event/84713 84713-21624468@events.umich.edu Event Begins: Tuesday, August 3, 2021 12:00pm
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

This lecture will be pre-recorded and available two weeks before this session on the ICPSR Summer Program YouTube channel: https://www.youtube.com/channel/UCgQWgr9Np3SKx54T_0hbo-Q

Please join us for this live Q&A session with the presenter on 8/3/21 at 12pm EDT at https://myumi.ch/ICPSR2021Blalock.

The 2021 ICPSR Summer Program Blalock Lectures are virtual, free and open to the public.

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Presentation Thu, 22 Jul 2021 13:03:54 -0400 2021-08-03T12:00:00-04:00 2021-08-03T13:00:00-04:00 Off Campus Location Inter-university Consortium for Political and Social Research Presentation ICPSR Summer Program Blalock Lecture Series 2021
Data Collection Methods in the Age of Data Science: Where Are We Headed? (August 5, 2021 7:30pm) https://events.umich.edu/event/84715 84715-21624470@events.umich.edu Event Begins: Thursday, August 5, 2021 7:30pm
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

Join us for this Blalock lecture at 7:30pm EDT: https://myumi.ch/ICPSR2021Blalock

The 2021 ICPSR Summer Program Blalock Lectures are virtual, free and open to the public.

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Presentation Thu, 22 Jul 2021 12:39:39 -0400 2021-08-05T19:30:00-04:00 2021-08-05T21:00:00-04:00 Off Campus Location Inter-university Consortium for Political and Social Research Presentation ICPSR Summer Program Blalock Lecture Series 2021
Measuring Exposure to News and Political Information in the Digital Age (August 10, 2021 7:30pm) https://events.umich.edu/event/84716 84716-21624471@events.umich.edu Event Begins: Tuesday, August 10, 2021 7:30pm
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

Join us for this Blalock lecture at 7:30pm EDT: https://myumi.ch/ICPSR2021Blalock

The 2021 ICPSR Summer Program Blalock Lectures are virtual, free and open to the public.

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Presentation Thu, 22 Jul 2021 12:44:49 -0400 2021-08-10T19:30:00-04:00 2021-08-10T21:00:00-04:00 Off Campus Location Inter-university Consortium for Political and Social Research Presentation ICPSR Summer Program Blalock Lecture Series 2021
The Post-API Age Reconsidered: Social Media Research in the '20s and Beyond (August 11, 2021 12:30pm) https://events.umich.edu/event/84717 84717-21624472@events.umich.edu Event Begins: Wednesday, August 11, 2021 12:30pm
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

This lecture will be pre-recorded and available two weeks before this session on the ICPSR Summer Program YouTube channel: https://www.youtube.com/channel/UCgQWgr9Np3SKx54T_0hbo-Q

Please join us for this live Q&A session with the presenter on 8/11/21 at 12pm EDT at https://myumi.ch/ICPSR2021Blalock.

The 2021 ICPSR Summer Program Blalock Lectures are virtual, free and open to the public.

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Presentation Thu, 22 Jul 2021 13:30:58 -0400 2021-08-11T12:30:00-04:00 2021-08-11T13:00:00-04:00 Off Campus Location Inter-university Consortium for Political and Social Research Presentation 2021 ICPSR Summer Program Blalock Lecture series
Measuring White Racial Solidarity and Examining its Relationship to Political Attitudes and Preferences (August 12, 2021 7:30pm) https://events.umich.edu/event/84718 84718-21624473@events.umich.edu Event Begins: Thursday, August 12, 2021 7:30pm
Location: Off Campus Location
Organized By: Inter-university Consortium for Political and Social Research

Join us for this Blalock lecture at 7:30pm EDT: https://myumi.ch/ICPSR2021Blalock

The 2021 ICPSR Summer Program Blalock Lectures are virtual, free and open to the public.

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Presentation Thu, 22 Jul 2021 13:29:27 -0400 2021-08-12T19:30:00-04:00 2021-08-12T21:00:00-04:00 Off Campus Location Inter-university Consortium for Political and Social Research Presentation 2021 ICPSR Summer Program Blalock Lecture series
Introduction to Multilevel Models (August 19, 2021 9:00am) https://events.umich.edu/event/84809 84809-21625038@events.umich.edu Event Begins: Thursday, August 19, 2021 9:00am
Location: Off Campus Location
Organized By: Institute for Social Research

PDHP resumes our 2021 workshop series on Thursday, August 19th, with a workshop entitled Introduction to Multilevel Models, presented by Dr. Kris Preacher of Vanderbilt University's Quantitative Methods program (within the Department of Psychology and Human Development). This half-day workshop is geared toward data analysts and researchers of all levels, particularly those performing analysis on hierarchically clustered (nested) data using Mplus, R, or SPSS. Attendees will receive an introduction to the key concepts of multilevel models (appropriate settings for their use over standard statistical models, equation conventions, and interpretation), as well as hands-on practice implementing state-of-the-art features of MLM using popular statistical software packages.

Topics include:

• Key concepts and motivation for MLM vs. standard statistical models
• Estimating and plotting interaction effects
• Implications of nested vs. cross-classified mutlilevel data
• Power analysis for MLM using a general Monte Carlo technique

Registration Required

https://pdhp.isr.umich.edu/workshops/

Dr. Preacher is a Professor in the Quantitative Methods program and Department of Psychology and Human Development at Vanderbilt University. His research concerns the use (and combination) of structural equation modeling and multilevel modeling to model correlational and longitudinal data. Other interests include developing techniques to test mediation and moderation hypotheses, bridging the gap between substantive theory and statistical practice, and studying model evaluation and model selection in the application of multivariate methods to social science questions. He serves on the editorial boards of Multivariate Behavioral Research, Behavior Research Methods, Communication Methods and Measures, and Journal of Educational & Behavioral Statistics, and is an associate editor of Psychological Methods.

The Population Dynamics and Health Program (PDHP) provides resources and services that support innovative approaches to data collection and analysis and the development of early-career population scientists, as well as research on significant and emergent issues in population dynamics and health.

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Workshop / Seminar Thu, 29 Jul 2021 15:55:30 -0400 2021-08-19T09:00:00-04:00 2021-08-19T13:00:00-04:00 Off Campus Location Institute for Social Research Workshop / Seminar Introduction to Multilevel Models poster
Intro to the Chitwan Valley Family Study (CVFS) (September 15, 2021 2:00pm) https://events.umich.edu/event/85327 85327-21626235@events.umich.edu Event Begins: Wednesday, September 15, 2021 2:00pm
Location: Off Campus Location
Organized By: Institute for Social Research

This webinar series on the Chitwan Valley Family Study (CVFS) is about global and comparative population research. Sessions include measuring mental health, Covid-19, linking data, genetics, & migrant data.

Webinar 1: Intro to CVFS
Wednesday, September 15, 2-3pm EDT
Presenters: William Axinn and Dirgha Ghimire

This webinar will explain the purpose of the CVFS and give an overview of data collection from study launch to present day. There will be a Q&A session after the presentation.

The webinar will be hosted using Zoom. Registration is required to attend the webinar. Support provided by NICHD (R25 HD101358).

Registration is required for this event: https://umich.zoom.us/meeting/register/tJwpf-qtpjojGteGYl9ntT4cBx7X9TPZtB6H

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Presentation Tue, 17 Aug 2021 11:59:26 -0400 2021-09-15T14:00:00-04:00 2021-09-15T15:00:00-04:00 Off Campus Location Institute for Social Research Presentation Nepal mountains