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  • Aerial view of Rio de Janeiro, Brazil

    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 […]

  • Headshot of Sahil Rajesh Dhayalkar

    TILOS-SDSU Seminar: A Modular AgenticAI Architecture for Commercially Scalable and Compliant Robotics

    TBA

    Sahil Rajesh Dhayalkar, Brain Corporation Abstract: Autonomous navigation in dynamic environments faces immense challenges. Traditional rigid, rules-based systems often fail due to a lack of semantic understanding needed to adapt to continuous environmental shifts. Conversely, emerging end-to-end Vision-Language-Action (VLA) models introduce a critical "black box" dilemma; they inherently lack the explicit application context, deterministic guardrails, […]

  • Headshot of Dr. Tengyu Ma

    Optimization for ML and AI Seminar: Self-play Algorithms for Math Theorem Proving

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

    Tengyu Ma, Stanford University Abstract: I will discuss RL algorithms for automated theorem proving with LLMs, especially in the possible future regime where we run out of high-quality training data. To keep improving the models with limited data, we draw inspiration from mathematicians, who continuously develop new results, partly by proposing novel conjectures or exercises […]

  • Headshot of Dr. Nigel Goldenfeld

    Optimization for ML and AI Seminar: A non-equilibrium phase transition with broken ergodicity leads to double descent and benign overfitting in machine learning

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

    Nigel Goldenfeld, UC San Diego Department of Physics and HDSI Abstract: The remarkable ability of modern neural networks to generalize improves with increasing network capacity, even when the number of model parameters or effective degrees of freedom exceeds the number of training data points. This phenomenon is all the more surprising given that generalization error […]

  • Poster for the 2026 IEEE/CVF Conference on Computer Vision and Pattern Recognition

    CVPR 2026 Workshop: Trustworthy, Robust, Uncertainty-Aware, and Explainable Visual Intelligence and Beyond (TRUE-V)

    IEEE/CVF Conference on Computer Vision and Pattern Recognition Denver, CO, United States

    Contemporary vision models and vision–language models are increasingly deployed in high-stakes domains, yet remain opaque, fragile, and difficult to align across tasks and modalities. This workshop aim to foster dialogue on the urgent need for transparent, reliable, and safe computer vision systems, especially in critical domains such as healthcare, transportation, and legal decision making. It brings together […]