Hanspeter Pfister
Hanspeter Pfister

Lead Instructor

AI for Scientific Discovery

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. 

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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.
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
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. 

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Bryan R. Moser, Ph.D.
Bryan R. Moser, Ph.D.

Lead Instructor

Digital Transformation of Projects with Model-based Project Design, Predictive AI, and Instrumented Teamwork

Dr. Bryan R. Moser is Academic Director of System Design & Management (SDM) at MIT, and a Senior Lecturer in both Engineering and the Sloan School of Management. He is also Associate Professor, Specially Appointed at the University of Tokyo, where he directs the Global Teamwork Lab (GTL).  Prior to returning to MIT in 2014, he worked for 25 years in industry; as a research engineer at the Basic Science Lab (A.I.) of Nissan Motor Company, as a Sr. Research Scientist at United Technologies Corporation, and as founder and President of Global Project Design, a firm pioneering software and methods for model-based project management. Moser focuses on engineering teamwork for complex systems problems and use of model-based methods to improve performance of diverse teams.  Moser received a bachelor’s in computer science and Engineering in 1987 and a Master of Science in Technology and Policy from the Massachusetts Institute of Technology in 1989. His doctorate in 2012 is from the University of Tokyo, Graduate School of Frontier Sciences.

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