Agentic AI for Scientific Discovery, part 2

The abstract covers both talks. George Karniadakis will present from 9 -- 9:45 a.m. and Nazanin Ahmadi will present from 10 -- 10:50 a.m.

Abstract: 

Scientific creativity rarely emerges from isolated computation; it often arises through interactions among ideas, models, data, and researchers. While current agentic AI systems can automate increasingly complex scientific workflows, a more fundamental challenge remains: Can AI systems generate genuinely novel hypotheses and contribute to creative scientific discovery? In this talk, we explore agentic AI through the lens of Scientific Machine Learning (SciML), where data-driven learning is coupled with mathematical structure and physical knowledge. We discuss how physics-informed neural networks, neural operators, and related SciML frameworks can serve as computational engines for scientific reasoning and model discovery. We then present an agentic framework designed to support iterative scientific discovery through memory, information sharing, multi-agent reasoning, hypothesis generation, mathematical and computational validation, and self-improvement. Rather than relying on a single model or agent, the system enables multiple specialized agents to propose, critique, test, and refine candidate scientific explanations. Through examples ranging from gene-expression trajectories to cancer-treatment dynamics, we demonstrate how these systems can integrate data, prior knowledge, and mathematical structure to uncover interpretable models and generate new scientific hypotheses. Ultimately, we ask how Agentic AI and Scientific Machine Learning can move beyond automating existing scientific tasks toward identifying unexplained patterns, formulating testable hypotheses, and proposing new directions for scientific research.

Biographies:

George Karniadakis is an elected member of the National Academy of Engineering, National Academy of Arts and Sciences, and a Vannevar Bush Faculty Fellow. He received his S.M. and Ph.D. from Massachusetts Institute of Technology (1984/87). He was appointed Lecturer in the Department of Mechanical Engineering at MIT and subsequently he joined the Center for Turbulence Research at Stanford / Nasa Ames. He joined Princeton University as Assistant Professor in the Department of Mechanical and Aerospace Engineering and as Associate Faculty in the Program of Applied and Computational Mathematics. He was a Visiting Professor at Caltech in 1993 in the Aeronautics Department and joined Brown University as Associate Professor of Applied Mathematics in the Center for Fluid Mechanics in 1994. After becoming a full professor in 1996, he continued to be a Visiting Professor and Senior Lecturer of Ocean/Mechanical Engineering at MIT. He is an AAAS Fellow (2018-), Fellow of the Society for Industrial and Applied Mathematics (SIAM, 2010-), Fellow of the American Physical Society (APS, 2004-), Fellow of the American Society of Mechanical Engineers (ASME, 2003-) and Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA, 2006-). He received the William Benter Prize (2026), SES G.I. Taylor medal (2014), the SIAM/ACM Prize on Computational Science & Engineering (2021), the Alexander von Humboldt award in 2017, the SIAM Ralf E Kleinman award (2015), the J. Tinsley Oden Medal (2013), and the CFD award (2007) by the US Association in Computational Mechanics. His h-index is 171 (highest in Applied Mathematics) and he has been cited over 184,000 times. In 2025 he was named by Research.com the number one math scientist in the world. (Speaking from 9 - 9:45 a.m.)

Nazanin Ahmadi is a PhD candidate in Biomedical Engineering at Brown University. Her current work focuses on agentic AI in scientific machine learning settings, with applications in systems biology, systems pharmacology, and pharmacometric modeling. She is a pioneer in the use of physics-informed neural networks in pharmacometrics, with an emphasis on interpretable and mechanistically grounded AI for model discovery and inverse problems. Her research interests include scientific machine learning, optimization algorithms, neural network architectures, and autonomous scientific workflows. She received a Machine Learning in Drug Discovery Symposium Lightning Talk Winner award at the Broad Institute in the first year of her PhD and was the second-place winner of the ASCPT Student & Trainee Research Pitch Competition. She is also an organizer of symposia and workshops in scientific machine learning. (Speaking from 10 - 10:50 a.m.)