COM3-02-42
660 17898

www.comp.nus.edu.sg/~arnab

Arnab BHATTACHARYYA

Associate Professor

  • Ph.D. (Computer Science, Massachusetts Institute of Technology, 2012)
  • M.Sc. (Computer Science, Massachusetts Institute of Technology, 2006)
  • B.Sc. (Computer Science & Physics, Minor: Mathematics, Massachusetts Institute of Technology, 2005)

Arnab Bhattacharyya is an assistant professor at the School of Computing at NUS. He obtained his bachelor’s, master’s and doctoral degrees in computer science from the Massachusetts Institute of Technology. Subsequently, he was a postdoctoral associate at Princeton University and Rutgers University. He was previously an assistant professor and a Ramanujan Fellow at the Indian Institute of Science, Bangalore. Dr. Bhattacharyya's research area is theoretical computer science and the foundations of data science, in a broad sense. Specifically, he is interested in algorithms for problems involving high-dimensional data, statistics, coding theory, and complexity theory. Dr. Bhattacharyya's work has been presented at several premier international conferences and journals. He is the recipient of an NRF Fellowship for AI (2019) and an Amazon Research Award (2018).

RESEARCH INTERESTS

  • Algorithms for High-Dimensional Data

  • Statistical and Causal Inference

  • Property Testing

  • Complexity Theory

  • Coding Theory

RESEARCH PROJECTS

RESEARCH GROUPS

TEACHING INNOVATIONS

SELECTED PUBLICATIONS

  • Near-Optimal Learning of Tree-Structured Distributions by Chow-Liu (ACM Symposium on Theory of Computing, 2021)
  • Parameterized Intractability of Even Set and Shortest Vector Problem (Journal of the ACM, 2021)
  • Learning and Testing Causal Models with Interventions (Neural Information Processing Systems, 2018)
  • An Optimal Algorithm for ℓ1-Heavy Hitters in Insertion Streams and Related Problems (ACM Transactions on Algorithms, 2018)
  • Every locally characterized affine-invariant property is testable (ACM Symposium on Theory of Computing, 2013)

AWARDS & HONOURS

  • NRF Fellowship for Artificial Intelligence (Class of 2019)

  • Amazon Research Award, 2018

MODULES TAUGHT