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  • Headshot of Roberto Morabito

    TILOS-SDSU Seminar: Autopilots Need Parachutes: Reliability Lessons from LLM-Automated Embedded AI Systems

    Lamden Hall 341 (SDSU) and Virtual San Diego, CA, United States

    Roberto Morabito, EURECOM Abstract: Embedded AI systems are becoming increasingly complex to develop and maintain, requiring specialized workflows that span data processing, model conversion, optimization, and deployment across heterogeneous hardware platforms. Recently, large language models have emerged as a promising tool to automate parts of this lifecycle. In this talk, I present recent work investigating […]

  • Headshot of Dr. Matus Telgarsky

    TILOS-Optimization for ML and AI Seminar: Implicit bias results for Muon, Adam, and Friends

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

    Matus Telgarsky, New York University Abstract: This talk will give both an empirical overview and a few simple bonds controlling the optimization path, or implicit bias, of modern optimization methods such as Adam and Muon (and Friends). The talk will begin with empirical results demonstrating the implicit bias phenomenon with shallow networks and also transformers […]

  • Headshot of Dr. Rene Vidal

    TILOS-HDSI Seminar: Engineering Interpretable and Faithful AI Systems

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

    René Vidal, University of Pennsylvania Abstract: Large Language Models (LLMs) and Vision Language Models (VLMs) have achieved remarkable performance across a wide range of tasks. However, their growing deployment has exposed fundamental limitations in faithfulness, safety, and transparency. In this talk, I will present a unified perspective on addressing these challenges through principled model interventions […]

  • Headshot of Dr. Andre Wibisono

    Optimization for ML and AI Seminar: A survey of the mixing times of the Proximal Sampler algorithm

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

    Andre Wibisono, Yale University Abstract: Sampling is a fundamental algorithmic task with many connections to optimization. In this talk, we survey a recent algorithm for sampling known as the Proximal Sampler, which can be seen as a proximal discretization of the continuous-time Langevin dynamics, and achieves the current state-of-the-art iteration complexity for sampling in discrete […]

  • 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. Ellen Vitercik

    TILOS-HDSI Seminar: Machine learning for discrete optimization: Theoretical foundations

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

    Ellen Vitercik, Stanford University Abstract: Many of the most important optimization problems in practice are massive in scale, mathematically complex, and involve numerous unknown parameters. Machine learning offers a powerful way to address these challenges by uncovering hidden structure across problem instances, but integrating predictions into algorithms raises fundamental questions: which architectures align with combinatorial […]

  • 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. Tianyi Lin

    TILOS-HDSI Seminar: ComPO: Preference Alignment via Comparison Oracles

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

    Tianyi Lin, Columbia University Direct alignment methods are increasingly used for aligning large language models (LLMs) with human preferences. However, these methods suffer from the likelihood displacement, which can be driven by noisy preference pairs that induce similar likelihood for preferred and dis-preferred responses. To address this issue, we consider doing derivative-free optimization based on […]