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  • Optimization for ML and AI Seminar: (De)regularized Wasserstein Gradient Flows via Reproducing Kernels

    HDSI 123 and Virtual 3234 Matthews Ln, La Jolla, CA, United States

    Bharath Sriperumbudur, Pennsylvania State University Abstract: TBA Bharath Sriperumbudur is a professor in the Department of Statistics (with a courtesy appointment in the Department of Mathematics) at the Pennsylvania State University. His research interests include non-parametric statistics, machine learning, statistical learning theory, optimal transport and gradient flows, regularization and inverse problems, reproducing kernel spaces in […]

  • Optimization for ML and AI Seminar with Yuejie Chi (Yale)

    HDSI 123 and Virtual 3234 Matthews Ln, La Jolla, CA, United States

    Title and abstract TBA... Yuejie Chi is the Charles C. and Dorothea S. Dilley Professor of Statistics and Data Science at Yale University, with a secondary appointment in Computer Science, and a member of the Yale Institute for Foundations of Data Science. Before joining Yale, Dr. Chi was the Sense of Wonder Group Endowed Professor […]

  • TILOS-HDSI Seminar with Rene Vidal (University of Pennsylvania)

    HDSI 123 and Virtual 3234 Matthews Ln, La Jolla, CA, United States

    Title and abstract TBA... René Vidal, a global pioneer of data science, is the Rachleff University Professor, with joint appointments in the Department of Radiology in the Perelman School of Medicine and the Department of Electrical and Systems Engineering in the School of Engineering and Applied Science. Dr. Vidal has been named a Penn Integrates […]

  • Optimization for ML and AI Seminar with Andre Wibisono (Yale)

    HDSI 123 and Virtual 3234 Matthews Ln, La Jolla, CA, United States

    Title and abstract TBA... Andre Wibisono is an assistant professor in the Department of Computer Science at Yale University, with a secondary appointment in the Department of Statistics & Data Science. His research interests are in the design and analysis of algorithms for machine learning, in particular for problems in optimization, sampling, and game theory. […]

  • ICLR 2026 Workshop: Principled Design for Trustworthy AI – Interpretability, Robustness, and Safety across Modalities

    ICLR 2026 Riocentro Convention and Event Center, Rio de Janiero, Brazil

    Modern AI systems, particularly large language models, vision-language models, and deep vision networks, are increasingly deployed in high-stakes settings such as healthcare, autonomous driving, and legal decisions. Yet, their lack of transparency, fragility to distributional shifts between train/test environments, and representation misalignment in emerging tasks and data/feature modalities raise serious concerns about their trustworthiness. This […]

  • TILOS-HDSI Seminar with Ellen Vitercik (Stanford)

    HDSI 123 and Virtual 3234 Matthews Ln, La Jolla, CA, United States

    Title and abstract TBA... Ellen Vitercik is an Assistant Professor at Stanford with a joint appointment between the Management Science and Engineering department and the Computer Science department. Her research interests include machine learning, algorithm design, discrete and combinatorial optimization, and the interface between economics and computation. Before joining Stanford, Dr. Vitercik was a Miller […]