Mark Braverman (mathematician)

Mark Braverman is a computer scientist and mathematician primarily known for his work in theoretical computer science, specifically in communication complexity, information theory, and their connections to other areas such as algorithmic game theory and machine learning.

Braverman received his Ph.D. in Computer Science from the University of Toronto in 2007, under the supervision of Stephen Cook. He subsequently held postdoctoral positions at Microsoft Research and the Institute for Advanced Study.

He is currently a Professor of Computer Science at Princeton University.

Braverman's research contributions include significant advances in understanding the limits of computation in distributed settings, particularly through the lens of communication complexity. He has developed novel techniques for proving lower bounds on communication complexity and applying these bounds to analyze the performance of algorithms in various distributed models.

His work on information complexity, a concept that measures the amount of information about the inputs that is revealed during a computation, has had a significant impact on the field. He has made fundamental contributions to understanding the relationship between communication complexity and information complexity.

Braverman's research also extends to areas such as algorithmic game theory, where he has studied the computational complexity of finding equilibria in games, and machine learning, where he has explored the theoretical foundations of learning algorithms. He has received several awards and honors for his work, including the Presburger Award in 2010 and a Sloan Research Fellowship.