Divesh AGGARWAL

Associate Professor
Principal Investigator, Centre for Quantum Technologies

  • Ph.D. (Computer Science, ETH Zurich)
  • M.Sc. (Integrated Mathematics & Scientific Computing, Indian Institute of Technology, Kanpur)

Divesh Aggarwal received his Ph.D. in Computer Science from ETH Zurich in 2012. From 2012 to 2016, he spent two years each as a postdoctoral researcher at New York University and at EPFL. Since 2016, he has been a faculty member in the Department of Computer Science, and a Principal Investigator in the Centre for Quantum Technologies at the National University of Singapore. His research in the last decade has revolved around the following two themes. 1. Fine-grained complexity, and exponential algorithms for hard problems: A primary focus has been to understand the time complexity and the relation between the computational complexity of various computational problems, particularly lattice problems. This includes both finding faster algorithms, and proving lower bounds under reasonable complexity theoretic assumptions. 2. Randomization has played a very important role in many areas of computer science. In practice, a source has some entropy, but is not uniformly random. The goal of transforming these imperfect random sources into perfectly random bits has, over the last few decades, led to a large body of research on the theory of randomness extractors and related primitives, and their applications in classical areas like privacy amplification, and tamper-resilient cryptography. Some of his recent research centers around improving construction of such extractors in various settings, and their applications.

RESEARCH AREAS

Algorithms & Theory
  • Combinatorial Algorithms
  • Complexity Theory
  • Cryptography

RESEARCH INTERESTS

  • Lattices: Algorithms and Complexity

  • Pseudorandomness

  • Cryptography

  • Coding Theory

  • Algorithms for Hard Problems

RESEARCH PROJECTS

RESEARCH GROUPS

TEACHING INNOVATIONS

SELECTED PUBLICATIONS

AWARDS & HONOURS

MODULES TAUGHT

CS5230
Computational Complexity