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        "event_title":"LRCCS Occasional Lecture Series | Transgender in Late Imperial China",
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        "combined_title":"LRCCS Occasional Lecture Series | Transgender in Late Imperial China: Matthew H. Sommer, Bowman Family Professor of History, Stanford University",
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        "description":"Attend in person or via Zoom: https:\/\/myumi.ch\/D8RV8\r\n\r\nThrough court cases, fiction, and late-Qing newspaper accounts, Matthew Sommer\u2019s 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 \u2014 yet, suspected of sexual predation, they risked death for the crime of \u201cmasquerading in women\u2019s attire,\u201d 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\u2019s transgender histories, this book sheds new light on law, religion, medicine, literature, and culture.\r\n   \r\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\u2019s 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, \u201cMale Same-Sex Relations and Masculinity in Qing China,\u201d and a fifth, \u201cCriminal Procedure in Eighteenth-Century China.\u201d",
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        "tags":["Asian Languages And Cultures","China","Chinese Studies","Gender","gender studies"],
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        "event_title":"Mathematics Undergraduate Seminar",
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        "combined_title":"Mathematics Undergraduate Seminar: Zach Duah - Modifying Brownian Motion",
        "event_subtitle":"Zach Duah - Modifying Brownian Motion",
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        "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.",
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        "datetime_modified":"20260107T161837",
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        "combined_title":"MPSDS \/ JPSM Seminar Series: Achieving Fairness in AI with Synthetic Data: Bei Jiang - University of Alberta",
        "event_subtitle":"Bei Jiang - University of Alberta",
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        "description":"MPSDS \/ JPSM Seminar Series\r\nMPSDS M3 Series: Mastery, Methodology, Meetups\r\n\r\nIn person, room 1070, Institute for Social Research and via Zoom. \r\nThe Zoom call be be locked 10 minutes after the start of the presentation. \r\nPlease note that many of the links have changed.\r\n\r\nAchieving Fairness in AI with Synthetic Data\r\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\u2013faithfulness 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\u2019s $\\alpha$ 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.\r\n\r\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\u20132015), before joining the University of Alberta as an Assistant Professor in 2015. Dr. Jiang has authored more than 50 journal articles\u2014including 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\u2019 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.",
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        "event_title":"Bank of America | AmplifyME! Markets Technical Workshop",
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