Data Science and Machine Learning Course with MIT

Tommi S. Jaakkola serves as the Thomas Siebel Professor at MIT, holding joint appointments in Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society. After completing his M.Sc. in theoretical physics from Helsinki University of Technology in 1992 and Ph.D. in computational neuroscience from MIT in 1997, he briefly held a postdoctoral position in computational molecular biology at UCSC before joining the MIT faculty in 1998. His research spans foundational machine learning theory to practical applications, with particular focus on statistical inference and estimation tasks. His current work includes developing generative AI models for molecular sciences, automated drug design, and creating self-explaining models for transparent AI. His research group advances how machines can learn, predict, and control at scale in an efficient, principled, and interpretable manner. They develop innovative methods for machine learning that emphasize efficiency, scalability, and interpretability, particularly in areas such as drug design, biomedical applications, and strategic game-theoretic interactions. His exceptional contributions have been recognized with numerous honors, including the AISTATS Test of Time Award in 2022, the Jamieson Award for Excellence in Teaching in 2015, and election as an AAAI Fellow. Under his leadership, MIT's machine learning courses have grown significantly, with the undergraduate course now enrolling more than 500 students per term.