Professor, Department of Electrical Engineering and Computer Science; Professor, Institute for Data, Systems, and Society (IDSS); Investigator, Computer Science and Artificial Intelligence Lab
Tommi Jaakkola is the Thomas Siebel Professor of Electrical Engineering and Computer Science and a member of MIT’s Computer Science and Artificial Intelligence Laboratory as well as the Institute for Data, Systems, and Society. His research focuses on how machines can learn, predict, and control in an efficient, principled, and interpretable manner. Jaakkola and his lab design new methods, theory, and algorithms to automate the use and generation of semi-structured data such as molecules, natural language text, images, or strategies. His applied research focuses on problems in molecular design and text analysis. His research interests include many aspects of machine learning, statistical inference and estimation, analysis and development of algorithms for modern estimation problems involving incomplete data sources. Jaakkola earned an MS in theoretical physics from Helsinki University of Technology and a PhD from MIT in computational neuroscience.
- Stokes, J., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N., MacNair, C., French, S., Carfrae, L., Bloom-Ackerman, Z., Tran, V., Chiappino-Pepe, A., Badran, A., Andrews, I., Chory, E., Church, G., Brown, E., Jaakkola, T., Barzilay, R., Collins, J. (2020). A deep learning approach to antibiotic discovery. Cell, 180(4).
Lee, G-H., Yuan, Y., Chang, S., Jaakkola, T. (2019). Tight certificates of adversarial robustness for randomly smoothed classifiers. In Neural Information Processing Systems (NeurIPS).
- Chang, S., Zhang, Y., Yu, M., Jaakkola, T. (2019). A game theoretic approach to class-wise selective rationalization. In Neural Information Processing Systems (NeurIPS).
- Feb. 20, 2020: MIT News, Artificial intelligence yields new antibiotic.
- July 9, 2019: MIT News, Machine learning for everyone.
- July 6, 2018: MIT News, Automating molecule design to speed up drug development.
- Sept. 8, 2017: MIT News, How neural networks think.