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    "128529-21861118":
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        "time_zone":"America\/Detroit",
        "event_title":"Colloquium Seminar: Van Eanam Lecture #1 Mean Field Games and Mean Field Controls: Where Do We Stand Twenty Years Later?",
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        "combined_title":"Colloquium Seminar: Van Eanam Lecture #1 Mean Field Games and Mean Field Controls: Where Do We Stand Twenty Years Later?: Rene Carmona (Princeton University)",
        "event_subtitle":"Rene Carmona (Princeton University)",
        "event_type":"Workshop \/ Seminar",
        "event_type_id":"21",
        "description":"The first part of this talk will review the early developments in the mathematical theory that began with the introduction of Mean Field Games twenty years ago. We will briefly discuss several applications to highlight the need for an asymptotic analysis in the search for Nash equilibria and social optima in competitive and cooperative large populations. Additionally, we will explain why and how a probabilistic approach was introduced to complement and enhance the original partial differential equation approach to these problems. Next, we will introduce the concept of the master equation and its role in renewing interest in optimal transportation and the analysis of partial differential equations in the Wasserstein space of probability measures. Finally we will discuss some of the new challenges currently investigated.\r\n\r\nReception to follow in the Mathematics Common Room.",
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        "tags":["Mathematics"],
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        "datetime_modified":"20250203T155556",
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        "has_end_time":1,
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        "event_title":"Donia Human Rights Lecture | Labor and Human Rights",
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        "combined_title":"Donia Human Rights Lecture | Labor and Human Rights: Jennifer (JJ) Rosenbaum, Executive Director, Global Labor Justice",
        "event_subtitle":"Jennifer (JJ) Rosenbaum, Executive Director, Global Labor Justice",
        "event_type":"Lecture \/ Discussion",
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        "description":"Attend in person or via Zoom. Zoom registration at https:\/\/myumi.ch\/zXkQR\r\n\r\nMigration is a critical battleground in the struggle to shape the economic future. In the contemporary economy, the exploitation of labor operates through global networks of migration, mobilization, and forced labor. Capital leverages regional legal restrictions on mobility, the threat of deportation and the violence of immigration enforcement, gendered violence, and supply-chain specific strategies of control in order to keep workers contingent, vulnerable, and compliant. At the same time, the workers of the world have developed new strategies and raised new demands to fight for dignity and collective power against this exploitation. Global Labor Justice (GLJ) is a strategy partner with transnational worker groups and works to support campaigns for labor rights, collective bargaining, and worker dignity across the world. Some of GLJ\u2019s specific current campaigns include work on gender-based violence and harassment in the garment supply chain, wage theft and work conditions in the seafood industry, and the financing of hospitality industry development. In her lecture, GLJ Executive Director JJ Rosenbaum will discuss campaigns, strategies, and tactics for holding employers and governments accountable. Specific strategies include transnational advocacy approaches, enforceable supply chain agreements (EBAs), and legal strategies that GLJ is currently deploying.\r\n\r\nJJ Rosenbaum is an attorney, organizer, and human rights strategist advocating for human rights, decent work for all, and fair migration. For over two decades, JJ has used legal, policy, and advocacy strategies to win access to rights and collective power for low-wage workers and advised workers\u2019 centers on transnational grassroots collaborations. Global Labor Justice follows a more than ten-year record in the post-Katrina Gulf Coast where JJ created a new model of movement lawyering as the founding legal and policy director for the National Guestworker Alliance and the New Orleans Workers\u2019 Center for Racial Justice. JJ has extensive experience with human rights investigations, legal strategies that build collective power, and advising worker, immigrant, and community organizations. She has litigated cases before trial and appellate courts and led the human, labor, and migrants rights strategy for campaigns including the Signal workers, who exposed labor trafficking from India to the Gulf Coast after Hurricane Katrina, and the Justice @ Hershey\u2019s campaign, where hundreds of foreign students won new regulations for the cultural exchange visa program. She is the co-chair of the American Bar Association\u2019s International Labor and Employment Committee and lectures on labor migration and comparative social justice lawyering approaches at Harvard Law School.\r\n\r\nMODERATORS\r\n\r\nRavi Anupindi, PhD\r\nColonel William G. and Ann C. Svetlich Professor of Operations Research and Management, Stephen M. Ross School of Business, University of Michigan\r\nRavi Anupindi\u2019s main research areas include technology and business innovation, global supply chain management, health care delivery in low and middle-income countries, economic development, and environmental & social sustainability. He was founding Faculty Director of the Center for Value Chain Innovation (2017-2020) and co-Director of the Technology and Business Innovation Forum (2015-18) at Ross. He is a member of the Governing Council of the Supply Chain Risk Leadership Council (SCRLC) and of the Board of the Fair Labor Association, among other professional leadership roles. He has served as Chair of (UM) President\u2019s Advisory Committee on Labor Standards and Human Rights since 2013.\r\n\r\nLeila Kawar, PhD\r\nAssociate Professor of American Culture  and in the Residential College, University of Michigan\r\nLeila Kawar is a socio-legal scholar whose comparative research examines the intersection of legal knowledge with the politics of migration, citizenship, and labor. She is the recipient, with co-PI John Valadez, of a 2024 Vital Impact Project Award through the LSA Meet the Moment Research Initiative for their project The Jungle, a short documentary film, intended for national broadcast on PBS, about the horrific experiences of children in American agricultural work. She serves as a Faculty Member of (UM) President\u2019s Advisory Committee on Labor Standards and Human Rights.\r\n\r\nIf there is anything we can do to make this event accessible to you, please contact us at wesleywr@umich.edu. Please be aware that advance notice is necessary as some accommodations may require more time for the university to arrange.",
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        "event_title":"Fostering Diversity at MEDITECH: Careers in Health IT for ALL Majors",
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        "event_title":"German Convo on the Go",
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        "description":"Members of the U-M community can walk and talk in German with Mary Gell (magell@umich.edu), German language instructor. Meet at Burton Tower,  'rain or shine', for a 1-hour walk. If the temperature is dangerously low, this event will meet in room 3110 Modern Languages Building. Please contact Mary if you have questions. Note that the group leaves at 4pm sharp.",
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        "event_title":"How to Take Care of Yourself So You Can Take Care of Others",
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        "datetime_modified":"20250203T095616",
        "datetime_start":"20250211T160000",
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        "event_title":"Statistics Department Seminar Series: Ye Tian, PhD Candidate, Department of Statistics, Columbia University",
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        "combined_title":"Statistics Department Seminar Series: Ye Tian, PhD Candidate, Department of Statistics, Columbia University: \"Transfer and Multi-task Learning: Statistical Insights for Modern Data Challenges\"",
        "event_subtitle":"\"Transfer and Multi-task Learning: Statistical Insights for Modern Data Challenges\"",
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        "event_type_id":"21",
        "description":"Abstract: Knowledge transfer, a core human ability, has inspired numerous data integration methods in machine learning and statistics. However, data integration faces significant challenges: (1) unknown similarity between data sources; (2) data contamination; (3) high-dimensionality; and (4) privacy constraints. This talk addresses these challenges in three parts across different contexts, presenting both innovative statistical methodologies and theoretical insights.\r\n\r\nIn Part I, I will introduce a transfer learning framework for high-dimensional generalized linear models that combines a pre-trained Lasso with a fine-tuning step. We provide theoretical guarantees for both estimation and inference.\r\n\r\nIn Part II, I will explore an unsupervised learning setting where task-specific data is generated from a mixture model with heterogeneous mixture proportions. This complements the supervised learning setting discussed in Part I, addressing scenarios where labeled data is unavailable. We propose a federated gradient EM algorithm that is communication-efficient and privacy-preserving, providing estimation error bounds for the mixture model parameters.\r\n\r\nIn Part III, I will introduce a representation-based multi-task learning framework that generalizes the distance-based similarity notion discussed in Parts I and II. This framework is closely related to modern applications of fine-tuning in image classification and natural language processing. I will discuss how this study enhances our understanding of the effectiveness of fine-tuning and the influence of data contamination on representation multi-task learning.\r\n\r\nFinally, I will summarize the talk and briefly introduce my broader research interests. I will also discuss my work on imbalanced classification error control, highlighting its role in improving classification performance for minority groups and its applications in biomedical and business contexts. The three main sections of this talk are based on a series of papers [TF23, TWXF22, TWF24, TGF23] and a short course I co-taught at NESS 2024 [STL24]. The segment on imbalanced classification error control is based on our recent paper [TF24]. More about me and my research can be found at https:\/\/yet123.com.\r\n\r\n[TF23] Tian, Y., & Feng, Y. (2023). Transfer Learning under High-dimensional Generalized Linear Models. Journal of the American Statistical Association, 118(544), 2684-2697.\r\n[TWXF22] Tian, Y., Weng, H., Xia, L., & Feng, Y. (2022). Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models. arXiv preprint arXiv:2209.15224. \r\n[TWF24] Tian, Y., Weng, H., & Feng, Y. (2024). Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms. ICML 2024. \r\n[TGF23] Tian, Y., Gu, Y., & Feng, Y. (2023). Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness. arXiv preprint arXiv:2303.17765. \r\n[STL24] A (Selective) Introduction to the Statistics Foundations of Transfer Learning. (2024).\r\n[TF24] Tian, Y., & Feng, Y. (2024). Neyman-Pearson Multi-class Classification via Cost-sensitive Learning. Journal of the American Statistical Association, 1-15. \r\n\r\nSpeaker bio: Ye Tian is a final-year Ph.D. student in Statistics at Columbia University. His research lies at the intersection of statistics, data science, and machine learning, focusing on three main topics: (1) reliable transfer learning; (2) high-dimensional statistics; and (3) privacy and fairness of the learning system.",
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