Happening @ Michigan https://events.umich.edu/list/rss RSS Feed for Happening @ Michigan Events at the University of Michigan. 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
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
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
MICDE & MIDAS Information Session (Virtual) (September 29, 2021 12:00pm) https://events.umich.edu/event/86450 86450-21634339@events.umich.edu Event Begins: Wednesday, September 29, 2021 12:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Computational Discovery and Engineering

Join the MICDE and MIDAS teams for a 1-hour virtual information session to learn more about our graduate program offerings, including: Ph.D. in Scientific Computing, Graduate Data Science Certificate Program, Graduate Certificate in Computational Neuroscience, and Graduate Certificate in Computational Discovery & Engineering.

Each program’s faculty and/or staff manager will be present to answer questions in a small group or 1:1 setting.

Please register for this event via Zoom to receive login details. Note: You may register after the event has started.

The event recording will be distributed to all event registrants within 24 hours of the event.

Link to register via Zoom: https://umich.zoom.us/meeting/register/tJMvde2hqzkjGtUEhi4zkYBvj4m-ndmUhBy8

View this event on our website: https://myumi.ch/51qjM

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Other Tue, 07 Sep 2021 15:47:48 -0400 2021-09-29T12:00:00-04:00 2021-09-29T13:00:00-04:00 Off Campus Location Michigan Institute for Computational Discovery and Engineering Other MICDE & MIDAS Info Session - Wed. 9/29/2021 @ 12pm
MICDE & MIDAS Information Tables (In-Person, Outdoors) (September 30, 2021 3:00pm) https://events.umich.edu/event/86448 86448-21634337@events.umich.edu Event Begins: Thursday, September 30, 2021 3:00pm
Location: Ingalls Mall
Organized By: Michigan Institute for Computational Discovery and Engineering

Meet 1:1 with MICDE and MIDAS graduate program faculty and staff managers to learn more about the institutes and the graduate programs they offer, including: Ph.D. in Scientific Computing, Graduate Data Science Certificate Program, Graduate Certificate in Computational Neuroscience, and Graduate Certificate in Computational Discovery & Engineering.

This event will be held in-person under the outdoor canopy tent located on the Ingalls Mall, across the street from the Rackham Graduate School building.

All attendees are required to wear masks.

View this event on our website: https://myumi.ch/jxA2w

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Other Tue, 07 Sep 2021 15:38:47 -0400 2021-09-30T15:00:00-04:00 2021-09-30T16:00:00-04:00 Ingalls Mall Michigan Institute for Computational Discovery and Engineering Other MICDE & MIDAS Info Tables - Thurs. 9/30/2021 @ 3pm
Active Data Collection, Hypothesis Testing, and Learning (October 1, 2021 3:00pm) https://events.umich.edu/event/87360 87360-21641516@events.umich.edu Event Begins: Friday, October 1, 2021 3:00pm
Location: Lurie Robert H. Engin. Ctr
Organized By: Electrical and Computer Engineering

Dr. Tara Javidi is the 2020 ECE Distinguished Educator award winner.

This talk revisits the problem of active hypothesis testing: a classical problem in statistics in which a decision maker is responsible to actively and dynamically collect data/samples so as to enhance the information about an underlying phenomena of interest while accounting for the cost of communication, sensing, or data collection. This talk, specifically, explores an often overlooked connection between active hypothesis testing and a wide variety of problems in engineering and the next generation artificial intelligence. This connection, we argue, has significant implications for next generation of information acquisition and machine learning algorithms where data is collected actively and/or by cooperative yet local agents.

In the first part of the talk, we discuss the history of active hypothesis testing (and experiment design) in statistics and the seminal contributions by Blackwell, Chernoff, De Groot, and Stein. In the second part of the talk, we discuss the information theoretic view of feedback and actions. We will illustrate the utility of this information theoretic analysis in a number of practically relevant problems in the design of next generation of networks.

Bio:

Tara Javidi received her MS degrees in electrical engineering (systems) and in applied mathematics from the University of Michigan, Ann Arbor where she her Ph.D. in electrical engineering and computer science in 2002. She is currently a professor of electrical and computer engineering and a founding co-director of the Center for Machine-Intelligence, Computing and Security at the University of California, San Diego. She is also a co-PI at The NSF AI Institute for Learning-enabled Optimization at Scale (TILOS).

Tara Javidi’s research interests are in theory of active learning and statistical inference, information theory with feedback, stochastic control theory, and wireless communications and communication networks.

Tara Javidi is a Fellow of IEEE. She and her Phd students are recipients of the 2021 IEEE Communications Society & Information Theory Society Joint Paper Award. She was awarded University of Michigan ECE’s 2021 Distinguished Alumni Educator Award. She also received the 2018 and 2019 Qualcomm Faculty Award for her contributions to wireless technology. Tara Javidi was a recipient of the National Science Foundation early career award (CAREER) in 2004, Barbour Graduate Scholarship, University of Michigan, in 1999, and the Presidential and Ministerial Recognitions for Excellence in the National Entrance Exam, Iran, in 1992. At UCSD, she has also received awards for her exceptional University service/leadership and contributions to diversity.

This is being offered as a hybrid event. U-M authentication is required to join the webinar.
https://umich.zoom.us/j/99392452117

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Lecture / Discussion Wed, 22 Sep 2021 11:28:10 -0400 2021-10-01T15:00:00-04:00 2021-10-01T16:00:00-04:00 Lurie Robert H. Engin. Ctr Electrical and Computer Engineering Lecture / Discussion Lurie Robert H. Engin. Ctr
My Journey in Enabling 100M+ Intelligent Things (October 22, 2021 11:00am) https://events.umich.edu/event/88305 88305-21652307@events.umich.edu Event Begins: Friday, October 22, 2021 11:00am
Location: Lurie Robert H. Engin. Ctr
Organized By: Electrical and Computer Engineering

Dr. Scott Hanson is the 2020 ECE Rising Star Alumni Award Recipient.

Abstract:

Ambiq spun out of the University of Michigan in 2010 with one goal: to put intelligence everywhere. A decade later, Ambiq has added intelligence to more than 100 million wearables, hearables, smart cards, industrial sensors, and medical devices. In this talk, I’ll give some insight on the journey of building Ambiq from University-backed research project to 150+ person company building chips for the world’s biggest brands. I’ll walk through some of our latest low power circuit innovations and where we’re going next. I’ll also talk about what new innovations we need from you at the University of Michigan to enable 100 BILLION smart things.

Bio:

Scott Hanson is the Chief Technology Officer and founder of Ambiq. Scott invented SPOT, Ambiq’s core sub-threshold technology platform, to enable the world’s most energy-efficient chips during his PhD studies at the University of Michigan. He founded Ambiq in 2010 and led the development of the world record-setting Apollo, Ambiq’s first flagship processor. Under Scott’s leadership, Ambiq has shipped more than 100 million chips to the world’s top brands and has grown into the global leader in ultra-low power solutions.

In addition to his role as CTO, he has variously played roles leading product definition and development, managing production test, and, most importantly, spending a great deal of time with customers to understand their needs and their vision. As a widely recognized innovator in low power circuits, Scott today leads the development of Ambiq’s technology roadmap.

Scott’s pioneering work in sub-threshold design and picowatt processors has been widely published, with more than 30 leading publications, more than 20 patents on related technology, and a wide variety of speaking engagements. Scott’s work was honored by the University of Michigan with the 2014 Arbor Networks PhD Research Impact Award and the 2020 ECE Alumni Rising Star Award and was honored by Ernst & Young as an Entrepreneur of the Year 2020 finalist.

This event is being offered hybrid. Zoom link for access: https://umich.zoom.us/j/99121690983

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Lecture / Discussion Fri, 15 Oct 2021 09:37:29 -0400 2021-10-22T11:00:00-04:00 2021-10-22T12:00:00-04:00 Lurie Robert H. Engin. Ctr Electrical and Computer Engineering Lecture / Discussion Hanson headshot