Tommi Jaakkola, a professor of computer science and engineering, has been named as the inaugural holder of the Thomas Siebel Professorship in the Department of Electrical Engineering and Computer Science (EECS) and the Institute for Data, Systems, and Society (IDSS).

The appointment was announced by EECS Department Head Anantha Chandrakasan, Vannevar Bush Professor of EECS, and IDSS Director Munther A. Dahleh, William Coolidge Professor of EECS. “The appointment recognizes Professor Jaakkola’s leadership in the area of machine learning and his outstanding mentorship and educational contributions,” Chandrakasan and Dahleh wrote in a message to EECS faculty. “Professor Jaakkola is internationally well-known in the fields of machine learning and natural language processing, as well as in computational biology. He is widely respected as an original researcher and has made high-impact contributions.”

The new professorship was established through the generous contribution of veteran software entrepreneur Thomas Siebel, Chairman and CEO of C3IoT. Siebel is well-known at MIT for having established the Siebel Scholars program, which annually provides support for 16 MIT graduate students (five in EECS, five in Biological Engineering, five in the MIT Sloan School of Management, and one in Energy Science).

At the core of Jaakkola’s research are inferential and estimation questions in complex modeling tasks, ranging from developing the underlying theory and associated algorithms to translating such advances into applications. He has been a leading contributor to developing distributed probabilistic inference algorithms from this field’s inception to its current state as a well-established area of research.

From the modeling point of view, Jaakkola’s work covers a broad spectrum of areas, from the interface between generative and discriminative modeling, rethinking modeling from the point of view of randomization and combinatorial optimization, to recovery questions associated with continuous embedding of objects. In natural language processing (NLP), his contributions solving hard combinatorial inference problems such as natural language parsing, developing deep convolutional representations of text, and reframing complex models to reveal interpretable rationales for prediction. Several of his papers have received best-paper awards at leading events.

In addition, Jaakkola “has made outstanding educational contributions,” Chandrakasan and Dahleh noted. He established and oversaw the growth of the graduate machine learning course , teaching it for many years until Professor Leslie Kaelbling took it over for further development. Together with Professor Regina Barzilay, he developed the undergraduate machine learning course, which now enrolls more than 500 students per term. He modernized the advanced NLP course, again taught with Barzilay, from the point of view of neural approaches to NLP. In 2015, Jaakkola received the Jamieson Award for Excellence in Teaching in recognition of his educational contributions.

He has also made valuable professional contributions in his field and within EECS. He has held editorial positions on prestigious journals such as the Journal of Machine Learning Research and the Journal of Artificial Intelligence Research. He has also co-chaired or overseen areas of major conferences, including the Conference on Neural Information Processing Systems (NIPS), the Conference on Uncertainty in Artificial Intelligence (UAI), and the Conference on Artificial Intelligence and Statistics (AISTATS). He served for many years on the EECS Faculty Search Committee and has been a member of other committees as well. He has also contributed to the career paths of many students and postdocs that he has supervised and mentored at MIT. Former students and postdocs from his research group now hold positions in leading universities such as MIT, CMU, and UC Berkeley.

As an affiliate member of IDSS, Jaakkola has been instrumental in both the hiring and recruitment of statistics faculty as well as the creation of programs in statistics. He has served on the IDSS Statistics Faculty Search Committee from the start, and worked with the IDSS Statistics PhD Committee to develop a proposal for a dual PhD degree. He is also a participant in the Statistics and Data Science MicroMasters.