Polina Golland MIT
Polina Golland

Participating Instructor

Transforming Healthcare with AI: Strategy, Implementation, and Clinical Impact

Polina Golland is the Sunlin (1966) and Priscilla Chou Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology and a principal investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her research interests span computer vision and machine learning, with a focus on developing new techniques for biomedical image analysis and understanding.

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Dr. Collin M. Stultz
Collin Stultz

Participating Instructor

Transforming Healthcare with AI: Strategy, Implementation, and Clinical Impact

Dr. Collin M. Stultz is a Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), a faculty member in the Harvard-MIT Division of Health Sciences and Technology, a Professor in the Institute of Medical Engineering and Sciences at MIT, a member of the Research Laboratory of Electronics (RLE), and an associate member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). 

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Discover what it takes to move powerful AI technologies from development into real-world healthcare settings in this timely two-day course. With the guidance of renowned healthcare AI expert Professor Regina Barzilay, you’ll learn how to evaluate AI tools for healthcare, navigate implementation challenges, and make informed decisions about when—and how—to put AI to work in a clinical setting.
Katri Nousiainen
Katri Nousiainen

Lead Instructor

Law and Technology

Professor Katri L. Nousiainen is a lawyer and a professional in emerging technologies, economics and legal education. In addition, she is affiliated faculty with the Yale Law School, Information Society Project (ISP) as an ISP Fellow; the University of Cambridge Law (the United Kingdom) as a Research Scholar; and Hanken School of Economics (Finland) as a Teaching and Research Faculty. Besides, she is a Teaching Faculty at Harvard University. 

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Artificial Intelligence Beyond Hype and Assumption

AI is increasingly shaping how organizations operate and compete, yet much of its adoption is influenced by incomplete understanding and broad assumptions about its potential. This often results in initiatives that lack clarity, struggle to scale, or fail to deliver meaningful value in real business contexts.

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.
This course introduces the modeling and mathematical foundations of modern AI. Starting from essential refreshers in calculus and linear algebra, we build toward the core structures underlying today’s supervised, unsupervised, and generative models. Case studies develop skill in translating real-world problems into the abstract language of modern AI pipelines.
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.
Anticipate where your industry is headed—and secure a competitive advantage—by mastering the latest discrete choice models and techniques. In this five-day course, you’ll work with leading MIT experts to discover how to apply discrete choice techniques; analyze challenges related to data collection, model formulation, estimation, testing, and forecasting; and assess online applications that drive optimization and personalization of results.
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