معرفی
Prasad Raghavendra is a Professor in the Electrical Engineering and Computer Sciences (EECS) Department at the University of California, Berkeley. His research focuses on theoretical computer science, particularly in optimization, complexity theory, approximation algorithms, hardness of approximation, and statistics. He is affiliated with the Center for the Theoretical Foundations of Learning, Inference, Information, Intelligence, Mathematics and Microeconomics at Berkeley (CLIMB) and the Simons Institute for the Theory of Computing (SITC).
- PhD in Computer Science and Engineering, University of Washington, Seattle (2009)
- M.S. in Computer Science and Engineering, University of Washington, Seattle (2007)
- B.S. in Computer Science, Indian Institute of Technology, Madras, India (2005)
Raghavendra's research spans theoretical computer science with a focus on optimization, complexity theory, approximation algorithms, and the hardness of approximation problems. He has made significant contributions to understanding Constraint Satisfaction Problems (CSPs), Sum-of-Squares SDP hierarchies, and their applications in high-dimensional statistics. His work bridges theoretical computer science with statistical inference, exploring computational-statistical gaps and developing efficient algorithms for problems in robust statistics, community detection, and tensor decomposition.
Raghavendra's recent publications demonstrate a clear trajectory toward the intersection of theoretical computer science and high-dimensional statistics. His work increasingly focuses on Sum-of-Squares SDP hierarchies for statistical problems, robust algorithms for planted models, community detection in stochastic block models, and heavy-tailed statistics. The publications show a progression from foundational work on CSPs and approximation algorithms toward applications in machine learning and statistical inference, with particular attention to computational barriers and optimal algorithms in high-dimensional settings.
- Michael and Sheila Held Prize (2018)
- Okawa Research Grant (2015)
- NSF Faculty Early Career Development Award (CAREER) (2013)
- Sloan Research Fellow (2012)
Raghavendra has advised numerous PhD students who have gone on to positions at institutions like Stanford Statistics, Google Research, and academic positions. His current and past students include David X. Wu, Sidhanth Mohanty, Tarun Kathuria, and others. His research has been supported by multiple grants including an NSF CAREER award and Okawa Research Grant, focusing on theoretical foundations of learning, inference, and computational complexity. He regularly teaches advanced courses including CS 270 (Combinatorial Algorithms and Data Structures), CS 294 (Constraint Satisfaction Problems), and CS 294 (Efficient Algorithms and Computational Complexity in Statistics).
Raghavendra is affiliated with the Center for the Theoretical Foundations of Learning, Inference, Information, Intelligence, Mathematics and Microeconomics at Berkeley (CLIMB) and the Simons Institute for the Theory of Computing. His work often involves collaborations with researchers in theoretical computer science, statistics, and mathematics at Berkeley and beyond. His research group focuses on developing theoretical foundations for high-dimensional statistical problems and exploring computational barriers in inference tasks.
