Madhu Sudan was born on September 12, 1966. He is an Indian-American computer scientist who has been a Gordon McKay Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences since 2015.
Career
He earned his bachelor's degree in computer science from IIT Delhi in 1987 and his doctoral degree in computer science from the University of California, Berkeley in 1992. His dissertation at the University of California, Berkeley was titled Efficient Checking of Polynomials and Proofs and the Hardness of Approximation Problems. He worked as a research staff member at the IBM Thomas J. Watson Research Center in Yorktown Heights, New York from 1992 to 1997 and later became a researcher at the Massachusetts Institute of Technology (MIT). From 2009 to 2015, he was a long-term researcher at Microsoft Research New England before joining the faculty at Harvard University in 2015.
Research contribution and awards
In 1998, he received the Sloan Research Fellowship. In 2002, he was awarded the Rolf Nevanlinna Prize at the 24th International Congress of Mathematicians (ICM). This prize honors important work in the mathematical parts of computer science. Sudan was recognized for his research on probabilistically checkable proofs, which are methods to rewrite mathematical proofs in computer language so they can be checked more thoroughly for accuracy. He also helped develop error-correcting codes. For this work, he received the ACM's Distinguished Doctoral Dissertation Award in 1993 and the Gödel Prize in 2001. He was an invited speaker at the ICM in 1998. In 2008, he became a Fellow of the ACM. In 2012, he became a fellow of the American Mathematical Society. In 2014, he won the Infosys Prize in the mathematical sciences. In 2017, he was elected to the National Academy of Sciences. In 2021, he was awarded the IEEE Richard W. Hamming Medal for 2022.
Sudan has contributed to many areas of theoretical computer science, including probabilistically checkable proofs, the difficulty of finding approximate solutions to optimization problems, list decoding, and error-correcting codes.