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 […]
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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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Presenter: Francois Ribalet, Research Associate Professor, School of Oceanography, University of Washington Description: François Ribalet will present new observational technologies and computational approaches for studying phytoplankton responses to ocean warming. Using SeaFlow, a custom-built automated flow cytometer deployed on over 100 research cruises, his team has collected nearly 850 billion cell measurements across global oceans. […] 
Presenter: Aditi Krishnapriyan, Assistant Professor, UC Berkeley Description: Recent advances in large-scale scientific datasets are creating new opportunities for machine learning (ML) methods to more effectively capture scientific phenomena with greater accuracy and reach. In this talk, I will discuss how these advances are both shifting ML design paradigms and enabling new scientific inquiries. This […] |
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Presenter: Amanda Coston, Assistant Professor, University of California Berkeley Description: Performance evaluation plays a central role in decisions about whether and how predictive algorithms should be deployed in high-stakes settings. Yet, in many real-world domains, evaluation is fundamentally difficult: the data available for assessment are often biased, incomplete, or noisy, and the act of deploying […]
Presenter: Ching-Yao Lai, Assistant Professor, Stanford University Description: I will discuss examples utilizing neural networks (NNs) to find solutions to partial differential equations (PDEs) that facilitate new discoveries. Despite being […]
Presenter: Amanda Coston, Assistant Professor, University of California Berkeley Description: Performance evaluation plays a central role in decisions about whether and how predictive algorithms should be deployed in high-stakes settings. Yet, in many real-world domains, evaluation is fundamentally difficult: the data available for assessment are often biased, incomplete, or noisy, and the act of deploying […] |
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