{ "135498-21876888": { "datetime_modified":"20250507T114034", "datetime_start":"20250520T090000", "datetime_end":"20250520T110000", "has_end_time":1, "date_start":"2025-05-20", "date_end":"2025-05-20", "time_start":"09:00:00", "time_end":"11:00:00", "time_zone":"America\/Detroit", "event_title":"Exploring Interpretable Latent Structure in Modern Data by Bayesian Modeling: Theory and Applications", "occurrence_title":"", "combined_title":"Exploring Interpretable Latent Structure in Modern Data by Bayesian Modeling: Theory and Applications: Sunrit Chakraborty", "event_subtitle":"Sunrit Chakraborty", "event_type":"Lecture \/ Discussion", "event_type_id":"13", "description":"The increasing complexity of modern data necessitates flexible statistical approaches capable of uncovering various latent structures, often intricately linked to population heterogeneity. This dissertation explores probabilistic models, particularly latent variable models and hierarchical models, within a Bayesian framework across various settings. It highlights their effectiveness in extracting meaningful patterns and representations from data, while also deepening our theoretical and computational understanding of these models.\r\n\r\nThe first chapter develops models and theoretical insights for hierarchical topic models, characterized by latent tree-structured topic hierarchies that yield a rich structure formed by multiple topic polytopes sharing faces. The second chapter builds on these geometric insights, extending them to continuous convolutional kernels. In particular, it sheds light on identifiability in general nonparametric mixtures of such distributions, where each component is nearly supported on a low-dimensional affine subspace. The third chapter revisits topic models from a different angle, exploring connections between Latent Dirichlet Allocation and mixtures of product multinomial models via tensor decomposition of the Dirichlet distribution. The fourth chapter examines general hierarchical models in grouped-data settings and extends strong identifiability theory from mixture models to these settings, establishing inverse bounds tailored to specific asymptotic regimes. The fifth chapter develops a nonparametric spatio-temporal model for dynamic velocity fields, with an emphasis on scalable inference. Finally, the last chapter addresses Bayesian methods in sequential decision-making and provides regret guarantees for Thompson sampling in sparse linear contextual bandit problems by analyzing the posterior under dynamic environments.\r\n\r\nA recurring theme of this dissertation is the asymptotic analysis of the posterior distribution of model parameters. Parameter learning is significantly more challenging than density estimation for latent variable models, often with complex dependencies across components. Nevertheless, such analysis enhances interpretability and informs the performance of downstream tasks that incorporate these models. It also reveals structural properties that can be leveraged for efficient inference. Overall, this dissertation bridges theoretical and practical aspects of Bayesian modeling and emphasizes its potential to extract interpretable structures from complex datasets.", "occurrence_notes":null, "guid":"135498-21876888@events.umich.edu", "permalink":"http:\/\/events.umich.edu\/event\/135498", "building_id":"1000167", "building_name":"West Hall", "campus_maps_id":"163", "room":"438", "location_name":"West Hall", "has_livestream":0, "cost":"", "tags":["Dissertation"], "website":"", "sponsors":[ { "group_name":"Department of Statistics Dissertation Defenses", "group_id":"4045", "website":"" } ], "image_url":"", "styled_images":{ "event_thumb":"", "event_large":"", "event_large_2x":"", "event_large_lightbox":"", "group_thumb":"", "group_thumb_square":"", "group_large":"", "group_large_lightbox":"", "event_large_crop":"", "event_list":"", "event_list_2x":"", "event_grid":"", "event_grid_2x":"", "event_feature_large":"", "event_feature_thumb":"" }, "occurrence_count":1, "first_occurrence":21876888 } , "129602-21864137": { "datetime_modified":"20250211T122734", "datetime_start":"20250520T090000", "datetime_end":"20250520T160000", "has_end_time":1, "date_start":"2025-05-20", "date_end":"2025-05-20", "time_start":"09:00:00", "time_end":"16:00:00", "time_zone":"America\/Detroit", "event_title":"Redefining the Crown", "occurrence_title":"", "combined_title":"Redefining the Crown: The Voices of Black Breast Cancer Survivors", "event_subtitle":"The Voices of Black Breast Cancer Survivors", "event_type":"Exhibition", "event_type_id":"7", "description":"In Winter 2025, the Lane Hall exhibit space will feature a portraiture series titled Redefining the Crown showcasing the powerful stories of six Black breast cancer survivors.\r\n\r\nBased on a photo essay by U-M Faculty Versha Pleasant (MD\/MPH) and Ava Purkiss (PhD) in Medicine at Michigan, this exhibition examines the cultural and personal significance of hair within Black communities, particularly through the lens of breast cancer treatment and recovery. The term \"crown\" is deeply symbolic in Black culture, signifying beauty, strength, and identity. The featured photo essay by photographer Tafari Stevenson-Howard captures the intimate journeys of Ann Chatman, Tanisha Kennedy, Felecia McDaniel, Shantell Elaine McCoy, Tamara Lynn Myles, and Veleria Banks.\r\n\r\nThrough their narratives and portraits, the exhibit examines how these women have navigated the profound impact of hair loss caused by chemotherapy, inviting the audience to witness their stories with radical empathy. It explores the cultural pride and personal identity intricately tied to their hair, and how these elements are redefined amidst their battles with breast cancer.\r\n\r\nThe exhibit will be on view from January 21, 2025 to August 8, 2025. This exhibition is presented with support from IRWG, the Department of Women's and Gender Studies, and Michigan Medicine. \r\n\r\nLocated on the first floor of Lane Hall (204 S. State Street), the Exhibit Space is free and open to the public, M-F, 9am-4pm.", "occurrence_notes":null, "guid":"129602-21864137@events.umich.edu", "permalink":"http:\/\/events.umich.edu\/event\/129602", "building_id":"1000183", "building_name":"Lane Hall", "campus_maps_id":"84", "room":"", "location_name":"Lane Hall", "has_livestream":0, "cost":"", "tags":["institute for research on women and gender","Women's And Gender Studies","women","african american","Art"], "website":"https:\/\/www.michiganmedicine.org\/medicine-michigan\/redefining-crown", "sponsors":[ { "group_name":"Institute for Research on Women and Gender", "group_id":"1031", "website":"http:\/\/irwg.umich.edu\/" }, { "group_name":"Michigan Medicine", "group_id":"1033", "website":"https:\/\/www.facebook.com\/MichiganMedicine\/" }, { "group_name":"Women's and Gender Studies Department", "group_id":"2809", "website":"http:\/\/www.lsa.umich.edu\/wgs" } ], 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Then we might have just the right space for you.\r\n\r\nAll members of the U-M community are invited to join our weekly virtual CoderSpaces to get research support and connect with others.\r\n\r\nTuesdays, 9:30-11 a.m. ET, via Zoom (Meeting ID:94181215786)\r\nWednesdays, 1:30-3 p.m. ET, via Zoom (Meeting ID: 98659357324)", "occurrence_notes":null, "guid":"117253-21865831@events.umich.edu", "permalink":"http:\/\/events.umich.edu\/event\/117253", "building_id":"", "building_name":"", "campus_maps_id":"", "room":"", "location_name":null, "has_livestream":0, "cost":"", "tags":["Data","Data Analysis","Data Collection","Data Curation","Data Linkage","Data Management","Data Science","Machine Learning","Social Science","Social Sciences"], "website":"", "sponsors":[ { "group_name":"Institute for Social Research", "group_id":"3285", "website":"http:\/\/isr.umich.edu" } ], "image_url":"", "styled_images":{ "event_thumb":"", "event_large":"", "event_large_2x":"", "event_large_lightbox":"", "group_thumb":"", "group_thumb_square":"", "group_large":"", "group_large_lightbox":"", "event_large_crop":"", "event_list":"", "event_list_2x":"", "event_grid":"", "event_grid_2x":"", "event_feature_large":"", "event_feature_thumb":"" }, "occurrence_count":53, "first_occurrence":21865812 } }