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2 events,
Virtual Event
Virtual Event
Presenter: Guannan Wang, Associate Professor, The College of William & Mary Description: Generative AI has emerged as a powerful tool for synthesizing biomedical images, offering new solutions to challenges such […]
Virtual Event
Virtual Event
Presenter: Fabrizio Falasca, New York University Description: A central challenge in climate science and applied mathematics is developing data-driven models of multiscale systems that capture both stationary statistics and responses […] |
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1 event,
Hybrid Event
Hybrid Event
Presenter: Yunyi Shen, Ph.D. Candidate, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology Description: Practitioners often aim to infer an unobserved population trajectory using sample snapshots at […] |
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2 events,
Hybrid Event
Hybrid Event
Presenter: Jiaqi Li, William H. Kruskal Instructor, University of Chicago Description:Modern machine learning (ML) algorithms achieve remarkable empirical success, yet providing rigorous statistical guarantees remains a major challenge, particularly in […] 
Presenter: Seshadhri Comandur, Professor of Computer Science, UCSC Description: There has been a lot of literature on graph machine learning over the past few years, and a bewildering array of […] |
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1 event,
Hybrid Event
Hybrid Event
Presenter: Qi Xu, Postdoctoral Researcher, Department of Statistics & Data Science, Carnegie Mellon University Description: Multi-modality data are increasingly common across science medicine and technology, such as imaging, text, sensors, […] |
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Presenter: Dongbin Xiu, Professor, Ohio State University Description:We present a data-driven modeling framework for scientific discovery, termed Flow Map Learning (FML). This framework enables the construction of accurate predictive models […] |
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2 events,
Presenter: Liam Stanton, Professor, San Jose State University Description: In this talk, I will present a multiscale model for cellular membranes, which is trained on molecular dynamics simulations. The model […] 
Presenter: Sifan Liu, Assistant Professor, Department of Statistical Science, Duke University Description:Mean-field variational inference (MFVI) approximates a target distribution with a product distribution in the standard coordinate system, offering a […] |
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1 event,On Wednesday, February 25, 2026 at 3:00PM in Humanities 1, Room 210, join SJRC scholars on the death of infrastructure, AI, and underwater network cables and his collaborative comic book on Actor Network Theory. |
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