Expertise: Optimization, reinforcement learning, machine learning, dynamic programming and data communication

Throughout his career, Dimitri Bertsekas has enjoyed engineering’s rich variety of challenges and how many of them can be viewed through a “unifying mathematical lens.”

An avid researcher, author and educator, Bertsekas has used this approach to contribute to advances in multiple research areas, including optimization, reinforcement learning, machine learning, dynamic programming and data communications.

In fall 2019, he joins the Arizona State University faculty as Fulton Chair of Computational Decision Making in the School of Computing, Informatics, and Decision Systems Engineering, one of the six Ira A. Fulton Schools of Engineering at ASU.

“I found ASU to be an exciting place for research where I can work with outstanding colleagues,” says Bertsekas.

Bertsekas has spent much of his career — since 1979 — as a faculty member at the Massachusetts Institute of Technology, where he held the position of McAfee Professor of Engineering.

At ASU, he’ll be teaching research-oriented seminars on the topics of optimization, optimal control, machine learning and artificial intelligence.

His main research focus at present is reinforcement learning — “a field that addresses large and challenging multistage decision problems, often with the use of neural networks and self-learning.”