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DTSTART:20070311T020000
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BEGIN:VEVENT
DTSTAMP:20260518T100001
DTSTART;TZID=America/Detroit:20260605T120000
DTEND;TZID=America/Detroit:20260605T124500
SUMMARY:Exhibition:We Are Stars
DESCRIPTION:What are we made of? Where did it all come from? Explore the secrets of our cosmic chemistry and our explosive origins. Connect life on Earth to the evolution of the Universe by following the formation of hydrogen atoms to the synthesis of carbon\, and the molecules for life.
UID:124092-21903907@events.umich.edu
URL:https://events.umich.edu/event/124092
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Astronomy,Film,Museum,natural history museum,Planetarium,Science
LOCATION:Museum of Natural History - Planetarium & Dome Theater
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20260528T143201
DTSTART;TZID=America/Detroit:20260605T120000
DTEND;TZID=America/Detroit:20260605T180000
SUMMARY:Exhibition:You Next
DESCRIPTION:The University of Michigan Duderstadt Center Gallery presents “You Next”\, a duo exhibition by Thede Ambrose and Kate Donoghue\, curated by Nathan Byrne.\n\nOpening: Friday\, May 29th 6-9pm\nClosing: Sunday\, June 28th 2-5pm\n\nArtifacts\, Illusion\, and the Speculative mediate the exchange between Thede Ambrose and Kate Donoghue’s practices.\n\nReferencing both personal and found documentary and advertisement imagery\, Donoghue investigates the vacuous and hauntingly banal underbelly of commercial aspirations\, constructing paintings that collapse expectations of consumerism into innate and corrupted desires.\n\nAmbrose navigates mediated violence\, spirituality\, and the abject\, manifesting in perverse articulations of belief and reality. Imagistic sculpture and installation are generated through an expansive material interest\, and an extensive archive of found imagery.\n\n“You Next” presents the collisions of these practices. Cautionary tales\, dreams\, and fantasies of objectification come to a head in this collaboration between the two artists.\n\nGallery Hours: Tuesday - Friday\, Noon-6pm and Sunday Noon-6 pm
UID:148455-21904305@events.umich.edu
URL:https://events.umich.edu/event/148455
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Art,Art Exhibition
LOCATION:Duderstadt Center - Gallery 1019
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20260522T091837
DTSTART;TZID=America/Detroit:20260605T120000
DTEND;TZID=America/Detroit:20260605T140000
SUMMARY:Presentation:Zahra Gandhi - Dissertation Defense
DESCRIPTION:Please join Zahra Gandhi for their dissertation defense titled \"Investigating in situ Protein Molecular Behavior at Buried Interfaces using a Combined Spectroscopic and Computational Approach\".\n\n*Date:* Friday\, June 5th\n*Time:* 12:00 PM\n*Where:* CHEM 1706\n\nZoom Meeting ID: 945 9967 4084\nPassword: barnacles
UID:148389-21904176@events.umich.edu
URL:https://events.umich.edu/event/148389
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Chemistry
LOCATION:Chemistry Dow Lab - 1706
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20260513T131053
DTSTART;TZID=America/Detroit:20260605T130000
DTEND;TZID=America/Detroit:20260605T160000
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:148255-21903613@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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BEGIN:VEVENT
DTSTAMP:20260513T130729
DTSTART;TZID=America/Detroit:20260605T130000
DTEND;TZID=America/Detroit:20260605T150000
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:148265-21903545@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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DTSTAMP:20260522T102454
DTSTART;TZID=America/Detroit:20260605T130000
DTEND;TZID=America/Detroit:20260605T150000
SUMMARY:Presentation:Learning with Quantum Examples: Multiclass\, Online\, and Smoothed Settings
DESCRIPTION:Abstract:\n\nAs quantum computing progresses toward fault-tolerant architectures\, the question of which computational tasks admit provable quantum advantages and which do not has become increasingly central. Learning theory\, and in particular learning from quantum examples\, provides one of the few settings in which unconditional quantum-classical separations can be established. In distribution-free (i.e.\, worst-case) PAC learning\, existing results show that quantum examples provide no asymptotic advantage in sample complexity. In contrast\, under the uniform distribution\, unbounded quantum-classical separations are known for learning Fourier-sparse Boolean functions. Together\, these results reveal a striking dichotomy. However\, this understanding has largely been developed in the context of learning Boolean functions in the batch setting\, leaving open how these phenomena extend more broadly. This thesis develops the theory of learning with quantum examples beyond the batch Boolean setting along three directions: multiclass learning\, online learning\, and smoothed learning.\n\nIn the multiclass PAC setting\, we establish upper and lower bounds on quantum sample complexity in both the realizable and agnostic regimes\, finding that quantum examples continue to yield no distribution-independent separation from classical examples\, with learning rates governed by the Natarajan dimension up to logarithmic factors in the label-space size. We next study online learning\, where no standard framework for learning with quantum examples existed prior to this work. We provide such a model by lifting the classical online framework to one in which the adversary provides distributions over labeled examples\, and then by encoding these distributions as quantum examples. We establish expected regret guarantees for binary and multiclass classification in both the realizable and agnostic settings. The central finding is that unrestricted adversarial power permits highly concentrated distributions that dequantize the learning problem. Motivated by this dequantization phenomenon\, we develop a smoothed learning framework that constrains distributions to be smooth\, interpolating between the concentrated-distribution regime\, in which no quantum advantage exists\, and the uniform-distribution regime\, in which unbounded separations are known. For the class of Fourier-sparse Boolean functions\, we show that such separations persist throughout a nontrivial near-uniform regime in both the batch and online settings.\n\nTogether\, these results paint a coherent picture of learning with quantum examples beyond the batch Boolean setting\, showing that quantum-classical separations depend on the interplay between hypothesis class structure\, distributional assumptions\, and the degree of adversarial control permitted in the learning process.
UID:148398-21904185@events.umich.edu
URL:https://events.umich.edu/event/148398
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Dissertation,Graduate,Graduate Students,Mathematics
LOCATION:East Hall - 3088
CONTACT:
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