About the Authors
  Vladimir Braverman
Vladimir Braverman
Associate professor
Department of Computer Science
Johns Hopkins University
Baltimore, Maryland, USA
Vladimir Braverman is an associate professor in the Department of Computer Science at Johns Hopkins University, with a secondary appointment in the Department of Applied Mathematics and Statistics. He is a member of the Algorithms and Complexity group, the Johns Hopkins Mathematical Institute for Data Science (MINDS), the Institute for Data Intensive Engineering and Science (IDIES) and the Machine Learning group. Braverman graduated from UCLA; before that, he was leading a research group at HyperRoll, a startup that was acquired by Oracle in 2009. Braverman’s research interests include efficient sublinear algorithms (such as sketches and coresets) and their applications to data science. He is a recipient of an NSF CAREER award, a Google Faculty Award and a Cisco Faculty Award.
  Robert Krauthgamer
Robert Krauthgamer
Department of Computer Science and Applied Mathematics
The Weizmann Institute of Science
Rehovot, Israel
Robert Krauthgamer (called “Robi” by his friends and colleagues) received his Ph.D. at the Weizmann Institute of Science in 2001 under Uriel Feige. He was subsequently a postdoc in Berkeley's theory group, and then a Research Staff Member at the theory group in the IBM Almaden Research Center. Since 2007, he has been a faculty member at the Weizmann Institute of Science. Robi's main research area is the design of algorithms for problems involving combinatorial optimization, finite metric spaces, high-dimensional geometry, data analysis, and related areas. His favorite sport since youth has been swimming. Once he swam across the Sea of Galilee in a 10km competitive race, and was the last one to arrive at the finish line.
  Lin F. Yang
Lin F. Yang
Assistant Professor
Department of Electrical and Computer Engineering
University of California, Los Angeles
Los Angeles, California, USA
Lin F. Yang is an assistant professor in the Electrical and Computer Engineering Department at the University of California, Los Angeles. His research focuses on developing and applying fast algorithms for machine learning and data science. His current research focuses on reinforcement learning theory and applications, learning for control, non-convex optimization, and streaming algorithms. He received a Ph.D. in physics and a Ph.D. in computer science from Johns Hopkins University and was a postdoctoral fellow at the Department of Operations Research and Financial Engineering, Princeton University. He was a recipient of the Simons Research Fellowship, the Dean Robert H. Roy Fellowship, and the JHU MINDS best dissertation award.