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.
Motors are becoming better and cheaper—opening profitable new applications across industries. In this course for engineers and product designers, you will learn to assess and design electric motors, generators, and drive systems, with emphasis on electric drives, including traction drives and drive motors. You will also explore how modern embedded controllers enable command through digital computation, breathing life into electric machines and motion control applications.
Sangbae Kim
Prof. Sangbae Kim, is the director of the Biomimetic Robotics Laboratory and a Professor of Mechanical Engineering at MIT. His research focuses on the bio-inspired robot design by extracting principles from animals. Kim's achievements on bio-inspired robot development include the world's first directional adhesive inspired from gecko lizards, and a climbing robot, Stickybot, that utilizes the directional adhesives to climb smooth surfaces featured in TIME's best inventions in 2006.