Ravindran Kannan


Ravindran Kannan ; born 12 March 1953, is a theoretical computer scientist. His work has mainly focused on efficient algorithms for problems of a mathematical flavor that arise in Computer Science, Machine Learning and Optimization.
Ravi was a Principal Researcher at Microsoft Research Lab, India. Before joining Microsoft, he was William K. Lanman Jr. Professor of Computer Science and Professor of Applied Mathematics at Yale University. Prior to that he has been on the faculty of CMU and MIT. The ACM Special Interest Group on Algorithms and Computation Theory presented its 2011 Knuth Prize to Ravi Kannan for developing influential algorithmic techniques aimed at solving long-standing computational problems.
Ravi Kannan did his B.Tech at IIT, Bombay. He received his PhD in 1980 at Cornell University under Leslie Earl Trotter, Jr.

Awards and honors

Key contributions

1. Algorithm for approximating the volume of high dimensional convex sets via Markov Chains
2. Algorithms for Integer Programming the Frobenius Problem drawing on Geometry of
Numbers
3. Randomized algorithms for Principal Component Analysis and Matrix Compression
4. Algorithmic version of the regularity lemma in Graph Theory

Selected works

Books

  • 2020. Foundations of Data Science..
  • 2009. Spectral Algorithms.

Other representative publications