AI for Science has emerged as one of the most dynamic frontiers in modern engineering, reshaping domains from healthcare to infrastructure. At the center of this transformation is materials science—the discipline that sets the physical limits of our world.
What would Artificial General Intelligence look like if its first breakthrough were not in language, but in the invention of matter? Join the frontier of superintelligence applied to agentic materials discovery. In this condensed four-day course, you will move beyond static design to master autonomous AI workflows. Through hands-on clinics, you will build multi-agent systems that do not merely predict material properties, but reason, plan, and invent next-generation smart materials - integrating large-scale computational modeling with generative AI to solve complex engineering challenges across scales, from atoms to systems, from concept to physical realization.
Since 2004, Rob Jackson has served a Professor in the Department of Mechanical Engineering at Auburn University. He earned his B.S., M.S., and Ph.D. degrees in Mechanical Engineering from the Georgia Institute of Technology. His research focuses on tribology, contact mechanics, electrified contacts, and lubricated bearings—critical areas for advancing mechanical systems and energy efficiency, particularly in electric vehicles (EVs). Professor Jackson directs Auburn’s Tribology Program and created one of the first undergraduate minors in tribology in 2012.
Peter Lee, Ph.D. is an Institute Engineer and the Chief Tribologist at Southwest Research Institute (SwRI), an Adjunct Professor at Texas A&M, and summer lecturer at the University of California Merced. Peter Lee received a B.Eng in Automotive Engineering and a Ph.D. in Engine Tribology from the University of Leeds, UK. Prior to attending University, Peter worked as a qualified motor vehicle technician repairing both light and heavy-duty vehicles as well as various motorsport activities on the side.
Ready to revolutionize transportation systems and discover how disruptive innovations are reshaping the mobility sector? In this immersive five-day course, you will learn to analyze and optimize transportation systems using the latest research from MIT and beyond, delving into demand and network modelling, artificial intelligence, simulation, optimization and control. These methods are explored alongside selected future solutions, with a focus on user-centric new smart mobility services , automated and AI-driven vehicles and alternative energy vectors for decarbonizing transportation. Through real-world case studies, professionals from transport service providers, urban and mobility planning, automotive, and transportation sectors can gain actionable insights to address current and future transportation challenges.
Julie Shah is the H.N. Slater Professor and Department Head of Aeronautics and Astronautics at MIT, co-leader of MIT’s Work of the Future Initiative, and director of the Interactive Robotics Group. Her research focuses on autonomous systems, human-robot collaboration, and AI planning, with applications in aerospace, healthcare, and manufacturing. Recognized by the NSF CAREER award and MIT Technology Review’s "35 Innovators Under 35," she has also received the IEEE RAS Academic Early Career Award. Prof. Shah holds S.B., S.M., and Ph.D. degrees from MIT and is co-author of What to Expect When You’re Expecting Robots.
AI is transforming processes across countless disciplines—and the sciences are no exception. In this high-impact three-day course, you’ll master a range of practical AI skills—including predictive modeling, large language models, and AI-driven experiment planning—to streamline and enhance your scientific research and uncover new insights.
Hanspeter Pfister
Lead Instructor
Hanspeter Pfister is An Wang Professor of Computer Science in the School of Engineering and Applied Sciences at Harvard University. Before joining Harvard, he worked for over a decade at Mitsubishi Electric Research Laboratories where he was Associate Director and Senior Research Scientist.
AI is transforming processes across countless disciplines—and the sciences are no exception. In this high-impact three-day course, you’ll master a range of practical AI skills—including predictive modeling, large language models, and AI-driven experiment planning—to streamline and enhance your scientific research and uncover new insights.