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DTSTART:20070311T020000
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DTSTAMP:20251015T141939
DTSTART;TZID=America/Detroit:20251112T120000
DTEND;TZID=America/Detroit:20251112T160000
SUMMARY:Exhibition:For All Ages Exhibit
DESCRIPTION:In the 19th century\, new ideas about childhood and education\, along with advances in printing like chromolithography\, made it possible to mass-produce games and toys. These were not only fun to play with but also taught practical skills and moral lessons. Learn about familiar and unique toys and board games throughout American history in the William L. Clements Library’s new exhibit\, “For All Ages” on view weekdays from 12-4 pm between October 3-January 5.\n\nEven though the objects are behind glass\, the co-curators have created an interactive way to explore the display. Visit the exhibit to participate in a scavenger hunt and win a prize!
UID:138977-21884429@events.umich.edu
URL:https://events.umich.edu/event/138977
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:american history,Exhibit,Free,Fun,Games,In Person,libraries,Library
LOCATION:William Clements Library
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20251024T122844
DTSTART;TZID=America/Detroit:20251112T120000
DTEND;TZID=America/Detroit:20251112T130000
SUMMARY:Lecture / Discussion:HET Brown Bag Seminar | Broadening direct searches for light dark matter
DESCRIPTION:Direct searches for low-mass DM were originally designed using the same conceptual picture as WIMP searches. However\, over the last five years\, the crucial role of in-medium effects has come into sharp focus. A new theoretical framework in the language of condensed matter physics has emerged for understanding the relationship between the properties of detector systems and their sensitivity to DM interactions. I will report on three recent advances that leverage this formalism to substantially broaden the design considerations for the next generation of experiments\, and even extract new constraints from existing data. First\, for DM–electron interactions\, large new datasets generated by the materials science community have enabled the first data-driven search for optimal detector materials\, which promises to significantly enhance the sensitivity of near-future experiments. Second\, just as detectors designed to detect nuclear scattering have been used to study electronic scattering\, I will explain how in-medium effects make the reverse possible as well\, allowing us to set new limits on DM–nucleon scattering using the low-threshold detectors designed to detect electronic scattering. Third\, with the advent of low-threshold detectors sensitive to energy deposits as low as 50 meV\, we have finally entered the regime where the interaction rate can be significantly enhanced due to the geometry of the detector system. These three considerations promise to substantially accelerate the search for light DM in both mass and cross section over the coming years.
UID:137598-21880447@events.umich.edu
URL:https://events.umich.edu/event/137598
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:brown bag,Brown Bag Seminar,Physics,Science
LOCATION:Randall Laboratory - 3481
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20251008T143834
DTSTART;TZID=America/Detroit:20251112T120000
DTEND;TZID=America/Detroit:20251112T130000
SUMMARY:Presentation:LRCCS Occasional Lecture Series | Transgender in Late Imperial China
DESCRIPTION:Attend in person or via Zoom: https://myumi.ch/D8RV8\n\nThrough court cases\, fiction\, and late-Qing newspaper accounts\, Matthew Sommer’s new book considers a range of transgender experiences in Imperial China\, illuminating how certain forms of gender transgression were sanctioned in particular contexts and penalized in others. People moved away from the gender they were assigned at birth in different ways and for many reasons. Eunuchs\, boy actresses\, and clergy left behind normative gender roles defined by family and procreation. Anatomical males who presented as women sometimes took a conventionally female occupation such as midwife\, faith healer\, or even medium to a fox spirit — yet\, suspected of sexual predation\, they risked death for the crime of “masquerading in women’s attire\,” even when they had lived peacefully in their communities for years. Sommer scrutinizes the ways authorities and literati understood gender-nonconforming people\, contrasting official ideology with popular mentalities. An unprecedented account of China’s transgender histories\, this book sheds new light on law\, religion\, medicine\, literature\, and culture.\n   \n   Matthew H. Sommer (BA Swarthmore\, MA U. of Washington\, PHD UCLA) is the Bowman Family Professor of History at Stanford University. A social and legal historian of Qing dynasty China (1644-1912)\, his research uses original legal case records from local and central archives to explore gender\, sexuality\, and family. He is the author of Sex\, Law\, And Society in Late Imperial China (Stanford 2000) and Polyandry and Wife-Selling in Qing Dynasty China (California 2015)\, which was the inaugural winner of the American Society for Legal History’s Peter Gonville Stein Book Award. His latest book\, The Fox Spirit\, the Stone Maiden\, and Other Transgender Histories from Late Imperial China (Columbia 2024) won the Boswell Prize from the LGBTQ+ History Association. Future plans include a fourth book\, “Male Same-Sex Relations and Masculinity in Qing China\,” and a fifth\, “Criminal Procedure in Eighteenth-Century China.”
UID:140455-21887171@events.umich.edu
URL:https://events.umich.edu/event/140455
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Asian Languages And Cultures,China,Chinese Studies,Gender,gender studies
LOCATION:Weiser Hall - 10th Floor
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20251112T094900
DTSTART;TZID=America/Detroit:20251112T120000
DTEND;TZID=America/Detroit:20251112T130000
SUMMARY:Workshop / Seminar:Mathematics Undergraduate Seminar
DESCRIPTION:Typically\, Brownian Motion is constructed from the set of continuous functions from [0\, \infty) into \mathbb{R}. However\, can we modify Brownian Motion such that we can construct it from the set of all paths in \mathbb{R} rather than the continuous ones? In this talk\, we will explore this notion\, along with other concepts in stochastic analysis.
UID:141800-21889376@events.umich.edu
URL:https://events.umich.edu/event/141800
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Mathematics,Seminar,Talk,Undergraduate,Undergraduate Students
LOCATION:East Hall - EH 2851, Nesbitt Room
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20250911T132206
DTSTART;TZID=America/Detroit:20251112T120000
DTEND;TZID=America/Detroit:20251112T133000
SUMMARY:Workshop / Seminar:Medicine\, Aging\, Science & Health (MASH) Workshop
DESCRIPTION:- September 10: Abby-Lynn Smith\n- October 8: Liz Harris\n- October 15: Analidis Ochoa\n- October 22: Hsin-Keng Ling\n- October 29: Megan Kelly\n- November 6: Special Event - Society of Fellows lunch with Neil Gong (co-sponsored with ISD)\n- November 12: Sofia Hiltner\n- November 19: Renee Anspach
UID:139226-21885139@events.umich.edu
URL:https://events.umich.edu/event/139226
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Graduate Students
LOCATION:LSA Building - 4147
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20260107T161837
DTSTART;TZID=America/Detroit:20251112T120000
DTEND;TZID=America/Detroit:20251112T130000
SUMMARY:Lecture / Discussion:MPSDS / JPSM Seminar Series: Achieving Fairness in AI with Synthetic Data
DESCRIPTION:MPSDS / JPSM Seminar Series\nMPSDS M3 Series: Mastery\, Methodology\, Meetups\n\nIn person\, room 1070\, Institute for Social Research and via Zoom. \nThe Zoom call be be locked 10 minutes after the start of the presentation. \nPlease note that many of the links have changed.\n\nAchieving Fairness in AI with Synthetic Data\nArtificial intelligence and machine learning increasingly inform decisions in hiring\, lending\, healthcare\, and justice. Yet real-world datasets often encode historical bias\, and models trained on them can reproduce or amplify inequities. Pre-processing via fair synthetic data is a promising: if we can generate data that mitigates bias at the source while preserving signal\, downstream models can be both fair and useful. This talk introduces FDA (Fair synthetic data via Data Augmentation)\, a statistically principled framework that makes the fairness–faithfulness trade-off explicit and controllable. FDA jointly models a fair submodel and a faithful submodel\, coupled by a single parameter $\alpha \in [0\,1]$ that quantifies the fraction of bias removed. We prove clear operating points: $\alpha=0$ yields maximal fairness (with larger deviation from the original distribution)\, $\alpha=1$ recovers the original data in probability (hence in distribution)\, and intermediate $\alpha$ values guarantee calibrated compromises with interpretable bounds. Practically\, FDA samples directly from simple predictive distributions\, avoiding heavy black-box training. We further provide theory connecting FDA’s $\alpha$ to fairness of downstream models. Together\, these results deliver a transparent\, efficient\, and deployable path to generating fair synthetic data without sacrificing essential statistical structure.\n\nDr. Bei Jiang is an Associate Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta\, a Fellow of the Alberta Machine Intelligence Institute (Amii)\, and a Canada CIFAR AI Chair. She received her PhD in Biostatistics from the University of Michigan in 2014\, followed by a postdoctoral appointment in the Department of Biostatistics at Columbia University (2014–2015)\, before joining the University of Alberta as an Assistant Professor in 2015. Dr. Jiang has authored more than 50 journal articles—including in the Annals of Statistics\, Journal of the American Statistical Association and the Journal of Machine Learning Research and over 20 peer-reviewed conference papers at venues such as NeurIPS\, ICML\, ICLR\, and AAAI. Her research focuses on Bayesian hierarchical modeling\, statistical learning methods that advance privacy and fairness\, and federated statistical inference. Dr. Jiang has an extensive record of service to the statistical community. She is currently serving on the SSC Equity\, Diversity\, and Inclusion Committee\, the CANSSI Showcase Organizing Committee\, the Committee of the COPSS Presidents’ Award\, and the JSM 2026 Program Committee. She is an Associate Editor for the Journal of the American Statistical Association. Dr. Jiang is the 2025 recipient of the COPSS Emerging Leaders Award\, recognizing early-career statistical scientists whose leadership and scholarship are shaping the field.
UID:141461-21888827@events.umich.edu
URL:https://events.umich.edu/event/141461
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Ai,Artificial Intelligence,Basic Science,Bias,Biomedical,biomedical research,brown bag,Data,Data Analysis,Data Collection,Data Curation,Data Linkage,Data Management,Data Science,In Person,Information and Technology,Lecture,Livestream,Mathematics,Medical,Online,Public Health,Research,Science,seminar,Statistics,Survey Methodology,Survey Methods,Survey Research,Virtual
LOCATION:Off Campus Location - Room 1070, Institute for Social Research
CONTACT:
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