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.
Chuchu Fan is an Assistant Professor in the Department of Aeronautics and Astronautics (AeroAstro) and Laboratory for Information and Decision Systems (LIDS) at MIT. Chuchu is the recipient of an NSF CAREER Award, an AFOSR Young Investigator Program (YIP) Award, and the 2020 ACM Doctoral Dissertation Award.
Explore the latest advances in robot learning, safety certification, and testing—and discover the myriad ways generative AI is revolutionizing robotics.
Autonomous robots. Self-driving cars. Smart refrigerators. Now embedded in countless applications, deep learning provides unparalleled accuracy relative to previous AI approaches. Yet, cutting through computational complexity and developing custom hardware to support deep learning can prove challenging for many enterprises—and the cost of getting it wrong can be catastrophic. Do you have the advanced knowledge you need to keep pace in the deep learning revolution?
Over the past eight years, the amount of computing required to run these neural nets has increased over a hundred thousand times, which has become a significant challenge. Gain a deeper understanding of key design considerations for deep learning systems deployed in your hardware.
Fuel your organization’s ability to produce large volumes of highly integrated, complex, customized products by leveraging intelligent design and manufacturing strategies powered by the latest in artificial intelligence. In this highly interactive course, you’ll join a group of accomplished global peers to explore the latest smart manufacturing strategies and hardware, acquire skills to develop machine learning-based design templates, and participate in generative design sessions.