Sanjeev Khanna is an Indian-American computer scientist. He is currently a Henry Salvatori professor of Computer and Information Science at the University of Pennsylvania. His research interests include approximation algorithms, hardness of approximation, combinatorial optimization, and sublinear algorithms.
Sanjeev Khanna | |
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Born | |
Citizenship | United States |
Alma mater | Birla Institute of Technology and Science, Pilani University of Illinois at Urbana-Champaign |
Spouse | Delphine Khanna |
Scientific career | |
Fields | Theoretical computer science |
Thesis | A Structural View of Approximation (1996) |
Doctoral advisor | Rajeev Motwani |
Doctoral students | Wang-Chiew Tan |
Khanna received his undergraduate degrees in computer science and economics from Birla Institute of Technology and Science, Pilani, India in 1990, his M.S. degree in computer science from University of Illinois at Urbana-Champaign in 1992, and his doctoral degree in computer science from Stanford University, California, US in 1996. He joined University of Pennsylvania in 1999 after spending three years as a member of the Mathematical Sciences Research center at Bell Laboratories.
Khanna's primary research contributions are to the fields of approximation algorithms, hardness of approximation, combinatorial optimization, and sublinear algorithms. His doctoral work at Stanford University, "A Structural View of Approximation", [1] received the 1996 Arthur Samuel prize for the best PhD dissertation in the Computer Science Department. He is a Guggenheim Fellow (2007) and a Sloan Fellow (2000). [2] He is also a recipient of S. Reid Warren, Jr. and Lindback awards for distinguished teaching at University of Pennsylvania.
He serves on the Editorial board of Foundations and Trends in Theoretical Computer Science, and has previously served on the editorial boards of SICOMP, ACM TALG, Algorithmica, JCSS, and as an area editor for Encyclopaedia of Algorithms.
In 2018, the Association for Computing Machinery named him an ACM Fellow for his contributions to approximation algorithms, hardness of approximation, and sublinear algorithms. [3]
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