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DTSTART:20070311T020000
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DTSTAMP:20260521T122023
DTSTART;TZID=America/Detroit:20260521T130000
DTEND;TZID=America/Detroit:20260521T133000
SUMMARY:Workshop / Seminar:A&A Shop Orientation
DESCRIPTION:In order to access the Art & Architecture Shop\, users must complete BOTH Orientation AND Proficiency Training. The A&A Shop Orientation covers basic information about the Shop (hours\, policies\, storage\, equipment\, etc.) as well as an overview of safety rules and concepts to help you navigate the space safely and effectively. Please note that it does NOT give you access to use the machinery--all users must receive Proficiency Training on each piece of equipment before use. Access: The Shop is available to students\, staff\, and faculty from the Penny W. Stamps School of Art & Design and the Taubman College of Architecture and Urban Planning for work on class projects and research only. Potential users from other academic disciplines must be receiving credit for a class taught by a faculty member from the Art or Architecture schools. Learn more on the A&A Shop site here. 
UID:111822-21903888@events.umich.edu
URL:https://events.umich.edu/event/111822
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Sessions
LOCATION:Art & Architecture Shop, 2000 Bonisteel Room 1251
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20260504T114020
DTSTART;TZID=America/Detroit:20260521T130000
DTEND;TZID=America/Detroit:20260521T150000
SUMMARY:Lecture / Discussion:Distributional Learning via Flexible Expectile Regression: Methods for Dependent\, Multivariate and Incomplete Data
DESCRIPTION:We develop a unified framework for flexible distributional learning based on expectile regression with adaptive basis functions\, allowing one to capture heterogeneous covariate effects across different regions of the outcome distribution. Building on this foundation\, we introduce a series of methodological contributions that extend expectile regression to increasingly complex data settings.\n\nFirst\, we propose a flexible nonparametric framework for expectile regression using reproducing kernel Hilbert spaces (RKHS)\, motivated by longitudinal studies in human biology in which aspects of the distribution of offspring anthropometry covary with parental characteristics. We develop a computationally efficient algorithm based on over-relaxed alternating direction method of multipliers (ADMM) to estimate expectiles across multiple distributional levels\, and establish valid joint inference procedures for a collection of expectiles using both cross-fitting and robust analytic approaches.\n\nSecond\, we extend expectile regression to event time data subject to right censoring and left truncation\, motivated by biomedical and public health studies where outcomes are incompletely observed and covariate effects may vary across the lifespan. Our motivating application is to understand how lifespans in different demographic groups correspond to neighborhood deprivation\, allowing for different effects on early and late mortality. To capture such patterns\, we estimate conditional expectiles of patient lifespans using weighting to account for censoring and truncation.  We then derive asymptotic linear expansions of the estimators and construct robust sandwich variance estimators\, enabling valid inference for distributional contrasts\, including comparisons across demographic groups and difference-in-difference analyses across expectile levels.\n\nThird\, we develop a unified framework for multivariate generalized expectile regression to analyze multi-output longitudinal data\, motivated by applications in which multiple related outcomes are measured repeatedly over time and exhibit complex dependence. Examples include biomedical studies where multiple health indicators are tracked for each patient\, or demographic data where event counts in geographic strata evolve jointly over time. Such data may exhibit heterogeneous covariate effects that predict different features of the response distribution. We begin by extending expectile regression to have a link function for each response\, enabling the specification of models with additive and multiplicative structures. We formulate the problem as a stacked estimating equation system capturing dependence across outcomes\, across time\, and across distributional levels without requiring specification of a working correlation structure. We develop cluster-robust sandwich covariance estimators that support valid inference for joint hypotheses\, enabling simultaneous assessment of distributional effects across outcomes and expectile levels.\n\nFinally\, we introduce a new class of interpretable distributional summaries based on expectile L-moments (EL-moments)\, motivated by the need for robust and informative measures of distributional shape that can be modeled in relation to covariates. Classical measures such as skewness and kurtosis are often sensitive to extreme observations and are not readily adapted to regression settings\, while quantile-based summaries lack smoothness and can be difficult to integrate into unified modeling frameworks. By projecting the expectile function onto a shifted Legendre polynomial basis\, we obtain EL-moments that provide interpretable summaries of location\, scale\, asymmetry\, and tail behavior. We further extend these summaries to conditional settings via expectile regression\, enabling covariate-dependent characterization of distributional features. We develop an influence-function-based framework for inference\, yielding consistent covariance estimators for both the EL-moment coefficients and their derived ratios.
UID:148074-21902920@events.umich.edu
URL:https://events.umich.edu/event/148074
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Dissertation
LOCATION:West Hall - 438
CONTACT:
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DTSTAMP:20260508T144218
DTSTART;TZID=America/Detroit:20260521T130000
DTEND;TZID=America/Detroit:20260521T150000
SUMMARY:Presentation:Geometrization in Algebraic and Arithmetic Geometry
DESCRIPTION:Abstract:\n\nThis thesis uses the idea of geometrization in two contexts. The first part is devoted to the Cartier transform developed by Ogus and Vologodsky. We strengthen their main result by weakening the assumptions and by allowing certain reasonable stacks as inputs\, obtaining\, in particular\, corollaries in the logarithmic setting. The second part studies the category of F-gauges over the formal spectrum of the Witt vectors of a perfect field of positive characteristic. We identify the full subcategory of F-gauges with Hodge-Tate weights in the range from 0 to p-2 with the category of Fontaine-Laffaille modules satisfying the analogous weight constraint.
UID:148194-21903218@events.umich.edu
URL:https://events.umich.edu/event/148194
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Dissertation,Graduate,Graduate Students,Mathematics
LOCATION:East Hall - 4096
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20260513T131053
DTSTART;TZID=America/Detroit:20260521T130000
DTEND;TZID=America/Detroit:20260521T160000
SUMMARY:Class / Instruction:June 1-5 Course - Introduction to Qualitative Research Methods
DESCRIPTION:June 1-5\, 2026\, M-F\n1:00-4:00pm \nA live course via Zoom. Registration and payment are required a minimum of two weeks prior to the start of the course.\n\nFounded in 1948\, the Summer Institute in Survey Research Techniques is designed specifically to meet the needs of professionals and graduate students seeking to deepen their expertise in survey methodology and data collection. Offered through the Michigan Program in Survey and Data Science within the Institute for Social Research at the University of Michigan\, the program provides a rigorous and flexible curriculum that blends theoretical foundations with practical application — entirely online.\n\nIntroduction to Qualitative Research Methods\n\nThis introductory course provides students with a strong foundation in qualitative research\, covering principles of qualitative research\, study design including participant recruitment and sample size estimation. Students also learn how to design and conduct core data collection methods - in-depth interviews\, focus groups\, and observation – and a range of field tasks such as transcription and field training. Then writing and critiquing qualitative methods for academic work. The course is highly interactive\, emphasizing both the principles and skill development through applied activities. The course needs a minimum of 6 registrants and has an enrollment capacity of 10. \n\nDr. Monique Hennink is Professor in the Hubert Department of Global Health in the Rollins School of Public Health and Associated Faculty in Sociology at Emory University. She is also Visiting Professor at University of Michigan\, Department of Epidemiology\, and Instructor at the University of Columbia's EPISUMMER program in Epidemiology. She earned her PhD in Demography in the United Kingdom.\n\nDr Hennink was indicted into Emory’s MilliPub Club in 2023 and 2024 for two research papers. This honors faculty authors of a scientific publication with over 1\,000 citations - considered high impact scholarship. She received the 2020 Provost’s Distinguished Teaching Award for Excellence in Graduate and Professional Education at Emory University. She also received the 'Excellence in Research' Award in 2019 and the 'Excellence in Teaching' Award in 2016 at the Rollins School of Public Health.\n\nShe has particular expertise in applying qualitative research to examine public health issues. She has 30 years’ experience in the design\, conduct\, analysis\, and publication of qualitative health research. She has authored five textbooks on qualitative research\, including: Qualitative Research Methods 2nd edition (2020)\; Focus Group Discussions (2014)\, Qualitative Research Methods (2011) (also translated into Chinese) and International Focus Group Discussions (2007). She teaches graduate-level courses in qualitative research at Emory University. She developed the 'QUAL-WORKS' (https://sph.emory.edu/qual-works) training program in 2013 for public health professionals. Her courses\, workshops and books reflect the application of qualitative methods in globally diverse settings and provide guidance on how to balance methodological rigor with the practical realities of global research. She has also published on various methodological aspects of qualitative research\, such as using interpreters and translators in qualitative data collection\; the effect of using court reporters on data quality\; estimating sample size in qualitative studies\; and highlighting emerging methodological issues in focus group research. She has served as a board member for SAGE Publications on their ‘Cases in Methodology’ work and on the editorial board of the International Journal of Multiple Research Approaches. She co-chaired a three-year scientific panel for the International Union of the Scientific Study of Population (IUSSP)\, on ‘Qualitative Research in Population Studies’ which had a mandate to promote rigor in the use of qualitative methods in the discipline. She has led scientific sessions on qualitative research at key professional forums\, such as: International Congress of Qualitative Inquiry\; International Institute for Qualitative Methods\; European Association of Population Studies\; and the International Union for the Scientific Study of Population.\n\nTextbook Information: Hennink\, Hutter & Bailey (2020) 2nd Ed. Qualitative Research Methods. Sage Publications\; Ritchie et al (2014) Qualitative Research Practice: A Guide for Social Science Students & Researchers. Second Edition\; Emerson et al (2011) Writing Ethnographic Fieldnotes\; Rubin & Rubin (2012) Qualitative Interviewing. The Art of Hearing Data. Third Edition\; Hennink (2014) Focus Group Discussions. Oxford University Press
UID:148255-21903510@events.umich.edu
URL:https://events.umich.edu/event/148255
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Biomedical,Center For Political Studies,Data,Data Analysis,Data Collection,Data Curation,Data Linkage,Data Management,Data Science,Graduate,Graduate and Professional Students,Health,Health Data,Mathematics,Professional Development,Public Health,Research,Survey Methodology,Survey Methods,Survey Research
LOCATION:Off Campus Location
CONTACT:
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DTSTAMP:20260513T130729
DTSTART;TZID=America/Detroit:20260521T130000
DTEND;TZID=America/Detroit:20260521T150000
SUMMARY:Class / Instruction:June 2 - July 30\, 2026 T/TH  Course - Sampling in Practice
DESCRIPTION:June 2-July 30\, 2026\, T/TH\n1:00pm - 3:00pm\nA live course via Zoom. Registration and payment are required a minimum of two weeks prior to the start of the course.\n\nFounded in 1948\, the Summer Institute in Survey Research Techniques is designed specifically to meet the needs of professionals and graduate students seeking to deepen their expertise in survey methodology and data collection. Offered through the Michigan Program in Survey and Data Science within the Institute for Social Research at the University of Michigan\, the program provides a rigorous and flexible curriculum that blends theoretical foundations with practical application — entirely online.\n\nSampling in Practice\n\nUnlocking the art and science of sampling with an applied\, hands-on approach\, the course Sampling in Practice is designed for applied practitioners who want to master real-world sampling techniques through active learning and practical programming. Students will learn about probability sampling methods\, including simple random sampling\, stratification\, systematic selection\, cluster sampling\, probability proportional to size sampling\, and multistage sampling. We will also cover sampling cost models\, sampling error estimation techniques\, non-sampling errors\, missing data\, and nonprobability samples. The course emphasizes practical implementation\, featuring interactive coding exercises and in-class examples to reinforce each concept. A culminating project will give students the opportunity to integrate multiple techniques into a comprehensive sample design and demonstrate the profession in designing surveys\, selecting subjects\, analyzing sample data\, and solving real sampling problems using modern statistical tools.\n\nWhy take this course? \n\nThe course is crafted for students and practitioners eager: \n\nTo build proficiency in modern sampling techniques through active engagement and practical coding experience\nTo understand the basic ideas\, concepts and principles of probability sampling from an applied perspective\nTo be able to identify and appropriately apply sampling techniques to survey design problems\nTo understand and be able to assess the impact of the sample design on survey estimates\nTo be able to compute the sample size for a variety of sample designs\nTo learn how to design and select a probability sample involving complex sampling techniques in a survey project\, and receive expert feedback on a sampling report. \n\nYajuan Si is a Research Associate Professor in the Michigan Program in Survey and Data Science\, located within in the Institute for Social Research at the University of Michigan. She holds a Ph.D. in statistical science from Duke and received postdoctoral training at Columbia. Yajuan’s research focuses on methodology development\, from data analysis to study design\, in streams of Bayesian statistics\, linking design- and model-based approaches for survey inference\, data integration\, missing data analysis\, confidentiality protection\, and causal inference\, with applications in the social and health sciences. More information can be found here: https://websites.umich.edu/~yajuan/.
UID:148265-21903530@events.umich.edu
URL:https://events.umich.edu/event/148265
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Data,Data Analysis,Data Collection,Data Curation,Data Linkage,Data Management,Data Science,Graduate,Professional Development,Survey Methodology,Survey Methods,Survey Research
LOCATION:Off Campus Location
CONTACT:
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