• USA-2011

    For contributions to computational complexity, algorithms, and optimization that have helped reshape our understanding of computation. Sanjeev Arora is one of the architects of the Probabilistically Checkable Proofs (PCP) theorem, which revolutionized our understanding of complexity and the approximability of NP-hard problems. He helped create new approximation algorithms for fundamental optimization problems such as the Sparsest Cuts problem and the Euclidean Travelling Salesman problem, and contributed to the development of semi-definite programming as a practical algorithmic tool. He has played a pivotal role in some of the deepest and most influential results in theoretical computer science, and continues to inspire colleagues and new generations of researchers.

  • USA-2008

    For foundational work on probabilistically checkable proofs and approximate solutions to NP-hard optimization problems.

  • USA-1995

    For his dissertation "Probabilistic Checking of Proofs and Hardness of Approximation Problems."

In the past I have worked on: Computational Complexity (see my book on this topic), Probabilistically Checkable Proofs (PCPs), computing approximate solutions to NP-hard problems, and related issues. For several years now I am most interested in developing new theory for Machine Learning (including deep learning).