Mohd Anwar

Foundational AI Lead, Office of Data Science Strategy (ODSS)

National Institutes of Health (NIH)

Bio

Mohd Anwar, PhD is the Lead of Foundational AI in the NIH Office of Data Science Strategy (ODSS). In this role, he provides programmatic oversight for ODSS initiatives in Foundational Artificial Intelligence and serves as the Program Lead for Multimodal AI program addressing high‑impact health challenges, including cancer, autoimmune disease, precision medicine, gene and immunotherapies, ICU mortality, rare and inherited diseases, and digestive diseases. He also contributes to the development of the NIH PRIMED‑AI program, serving as a co‑lead for the Model‑to‑Clinic Funding Opportunity. He supports Genesis mission activities and contributes to NIH-wide Super Intelligence strategy development through the Office of the Chief SI Officer. He also serves as an NIH representative to the National AI Research Resource initiative (NAIRR). His achievements have been recognized with the 2025 NIH Director’s Award and the 2023 NIBIB Director’s Rookie of the Year Award.

Topic

AI Assurance for Trustworthy Biomedical Research

Abstract

TBD

Douglas Summers-Stay

Computer Science Researcher

Army Research Laboratory (ARL)

Bio

Douglas Summers-Stay has a PhD from the University of Maryland Department of Computer Science. He has worked for the Department of Defense as a research scientist for 20 years. He is the author of Machinamenta: The thousand year quest to build a creative machine.

Topic

AI Safety as an Institution: Lessons from Army Decision Systems

Abstract

Like future powerful AI systems, the U.S. Army is a powerful entity that pursues its goals with potentially catastrophic consequences. Over the centuries, the Army has built up methods for keeping the pursuit of those goals in check, through other actors with other goals and other powers that interact with the chain of command in various ways to preserve the interests of various parties. We discuss some experiments with dual models which work together to guide a text generation, and experiments with meta-reasoning for consideration of a wider set of goals.

Vlad Pavlovic (shared with TAS '26)

Program Director, Information & Intelligent Systems Division

National Science Foundation (NSF)

Bio

Vladimir Pavlovic is a Professor of Computer Science at Rutgers University, where for more than twenty years he has led the SEQAM Lab. His work in probabilistic machine learning covers structured and multimodal data and has increasingly focused on the reliability of foundation models and agents built on that data. From 2022 to 2026 he served as a Program Director in NSF’s Division of Information and Intelligent Systems. There, he set a national funding strategy for AI and machine learning and managed a portfolio of more than 150 awards. He also developed or co-developed programs totaling over $200M. These include the $20M Safe Learning-Enabled Systems program, created with Open Philanthropy and Good Ventures to fund research on AI systems whose safety can be assured with high confidence. They also include the $8.5M Mathematical Foundations of AI program and the $6M AI for Mathematical Reasoning program, which grew out of a National Academies workshop he helped convene. He also led the US-Singapore Workshop on Responsible AI, which brought together government, academic, and industry partners from Singapore, Thailand, Malaysia, and Vietnam. He has authored nearly 300 peer-reviewed publications, holds five U.S. patents, and received his Ph.D. from the University of Illinois at Urbana-Champaign.

Topic

 Seeing Is Not Remembering: Toward Grounded, Trustworthy Multimodal Agents

Abstract

TBD

Reza Ghanadan

Professor and Executive Director of Innovations in AI

University of Maryland

Bio

Dr. Reza Ghanadan is Professor and Executive Director of Innovations in AI at the A. James Clark School of Engineering and the Institute for Systems Research at the University of Maryland, where he leads initiatives in AI engineering, robust and trustworthy AI systems, agentic intelligence, and applied AI for science and engineering. He is an affiliated research professor with the Department of Computer Science at the University of Maryland, the Artificial Intelligence Interdisciplinary Institute at Maryland (AIM), and the Center for Machine Learning at the University of Maryland. His work focuses on advancing scalable, reliable, and deployable AI systems that bridge foundational research with real-world impact across industry, government, and mission-critical applications. He is an IEEE Fellow, recognized for leadership in robust AI technologies and applications.

Topic

Engineering Trustworthy AI Agents: From Evaluation to Runtime Assurance

Abstract

AI agents are moving from generating responses to taking actions, using tools, and coordinating with other agents in consequential environments. This transition raises a fundamental engineering question: how do we build reliable, robust, and steerable AI systems, evaluate their behavior and limitations, and establish evidence that justifies their deployment and continued operation as conditions change?

This talk presents a systems perspective on engineering trustworthy AI agents, connecting risk-informed evaluation with runtime assurance. It examines what evaluations must establish beyond benchmark performance: capabilities and failure modes, latent capabilities that ordinary prompting may not reveal, and newly revealed or expanded capabilities as models, tools, and agent scaffolding evolve. Such capabilities may enable consequential behaviors beyond those previously tested. The talk addresses uncertainty and validity limits, including why failure to elicit a capability does not establish its absence. It also explores how AI models with strong mathematical reasoning capabilities can support mathematical formalization of agent requirements and behavioral properties, generate tests, help quantify risks, and check outcomes, with safeguards against shared blind spots.

The talk connects evaluation evidence to monitoring, explicit constraints, outcome verification, and targeted interventions for runtime control and recovery. These mechanisms aim to keep agents within defined operating limits while preserving useful capability. Multi-agent systems receive particular attention because delegation, shared state, and interacting behaviors complicate risk assessment, control, and accountability.

A running operational example illustrates how assurance evidence guides deployment decisions and responses to unexpected behavior. The talk concludes with an engineering and research agenda linking evaluation, validation, intervention, and learning from operational experience, while clarifying the limits of current methods and the roles of human oversight and organizational responsibility.

Raj Dasgupta

Research Scientist, Distributed Intelligent Systems Section

Naval Research Laboratory

Bio

Dr. Prithviraj (Raj) Dasgupta is the Head (Acting) of the Distributed Intelligent Systems Section at the U. S. Naval Research Laboratory, Washington, DC. His research interests are in the areas of distributed AI and game theory, focusing on multi-agent reinforcement learning, adversarial games and multi-robot task and motion planning. He has led several large projects in these areas funded by the Office of Naval Research, NASA and other federal agencies. He consistently serves on the program committees of premier AI and robotics conferences and is a senior member of IEEE. He received his Ph.D. in Computer Engineering in 2001 from the University of California, Santa Barbara.

Topic

Trustworthy Multi-Agent Autonomy in Challenged Contexts: Evidence from Maritime Capture-the-Flag

Abstract

Trustworthy multi-agent autonomy in adversarial games and other challenged contexts requires teams to demonstrate reliable behavior beyond win rates. This keynote presents the Maritime Capture-the-Flag (MCTF) challenge as a testbed for assessing AI in challenged contexts. MCTF is a multiplayer adversarial game in which teams of autonomous uncrewed surface vessels (USVs) learn and execute coordinated strategies to capture an opponent’s flag while defending their own. The challenge combines partial observability, dynamic adversaries, environmental uncertainty, heterogeneous teammates, communications constraints, and simulation-to-physical-system transfer. We assess operational trust in the MCTF challenge through stress testing across scenario variations, evaluation against unseen policies, field experiments, runtime monitoring, and characterization of residual risk. Such assessment enables us to distinguish mission effectiveness from robustness, resilience, predictability, human-oversight burden, and assurance coverage. Based on our lessons from the MCTF challenge, we argue that adversarial team games should extend beyond play-to-win to develop methods for determining when multi-agent autonomy is reliable, when it fails, and when humans should intervene.