TILOS-HDSI Seminar: Engineering Interpretable and Faithful AI Systems
2026-04-08 11:00 - 12:00 (America/Los_Angeles) | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, 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 and interpretable decision-making frameworks. I first introduce Information Pursuit (IP), an interpretable-by-design prediction framework that replaces opaque reasoning with a sequence of informative, user-interpretable queries, yielding concise explanations alongside accurate predictions. I then present Parsimonious Concept Engineering (PaCE), an approach that improves faithfulness and alignment by selectively removing undesirable internal activations, mitigating hallucinations and biased language while preserving linguistic competence. Results across text, vision, and medical tasks illustrate how these ideas advance transparency without sacrificing performance. Together, these contributions point toward a broader direction for building AI systems that are powerful, faithful, and aligned with human values.

René Vidal is the Penn Integrates Knowledge and Rachleff University Professor of Electrical and Systems Engineering and Radiology at the University of Pennsylvania, where he directs the Center for Innovation in Data Engineering and Science (IDEAS) and serves as Co-Chair of Penn AI. He is also an Amazon Scholar, Affiliated Chief Scientist at NORCE, and former Associate Editor-in-Chief of IEEE Transactions on Pattern Analysis and Machine Intelligence. Professor Vidal’s research advances the mathematical foundations of deep learning and trustworthy AI, with broad impact across computer vision and biomedical data science. His contributions have been recognized with major honors, including the IEEE Edward J. McCluskey Technical Achievement Award, the D’Alembert Faculty Award, the J.K. Aggarwal Prize, the ONR Young Investigator Award, the NSF CAREER Award, and best paper awards in machine learning, computer vision, signal processing, control, and medical robotics. He is a Fellow of ACM, AIMBE, IEEE, and IAPR, and a Sloan Fellow.

2026 Robotics Summit: The Next 25 Years of Robotics
2026-04-10 | University of Pennsylvania School of Engineering and Applied Science, Philadelphia, PA, United States

Optimization for ML and AI Seminar: A survey of the mixing times of the Proximal Sampler algorithm
2026-04-10 10:00 - 11:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, 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 time. We survey the mixing time guarantees of the Proximal Sampler algorithm and show they match the guarantees for the Langevin dynamics. When the target distribution satisfies log-concavity or isoperimetry, the Proximal Sampler has rapid convergence guarantees. We illustrate the proof technique via the strong data processing inequality along the Gaussian channel and its time reversal under isoperimetry.

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. He received his BS degrees in Mathematics and in Computer Science from MIT, his MEng in Computer Science from MIT, his MA in Statistics from UC Berkeley, and his PhD in Computer Science from UC Berkeley. He has done postdoctoral research at the University of Wisconsin-Madison and at the Georgia Institute of Technology.

ICLR 2026 Workshop: Principled Design for Trustworthy AI - Interpretability, Robustness, and Safety across Modalities
2026-04-27 | 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 workshop focuses on developing trustworthy AI systems by principled design: models that are interpretable, robust, and aligned across the full lifecycle – from training and evaluation to inference-time behavior and deployment. We aim to unify efforts across modalities (language, vision, audio, and time series) and across technical areas of trustworthiness spanning interpretability, robustness, uncertainty, and safety.

TILOS-SDSU Seminar: A Modular AgenticAI Architecture for Commercially Scalable and Compliant Robotics
2026-04-29 11:00 - 12:00 | 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, and data efficiency required for rigorous enterprise safety and compliance (e.g., SOC2). To address this, Brain Corp, in collaboration with UCSD, proposes a robust hybrid architecture underpinning the BrainOS platform. In this framework, visual inputs (via VLMs) and task commands (via LLMs) feed directly into a distinct Perception block anchored by a Contextual Grounding Layer with Semantic Mapping. This rich, grounded perception then informs a hybrid Action block, where the reasoning capabilities of VLA models operate safely alongside proven deterministic controls such as deep learning, reinforcement learning, model predictive control, etc. Crucially, an underlying Directed Safety Layer and strict Enterprise Infrastructure wrap this entire process. By isolating adaptable AI reasoning from hard-coded physical controls, this architecture provides a framework designed to securely manage the unpredictable realities of varied environments. Ultimately, this approach addresses the compliance bottleneck, laying the foundation to scale safely across diverse commercial applications and power the continuous, real-world data engine necessary to fuel next-generation physical AI.

Sahil Rajesh Dhayalkar is a Staff Autonomy Engineer and Perception Team Lead at Brain Corporation. He specializes in architecting real-time perception pipelines across LiDAR, RGB, and depth sensors, with his work currently deployed on production robots in dynamic commercial environments. During his tenure, he has pioneered the real-time computer vision pipeline for on-robot object detection at the edge, spearheaded “Localize From Anywhere,” a global localization system utilizing Vision-Language Models and RGB images, and auto-calibration, a targetless calibration of ranging sensors on robots. He holds a Master’s degree in Computer Science from Arizona State University. His research interests include robotic perception, large language models, deep learning, neuro-symbolic reasoning, and optimizations.
