Education

• MS in Mathematics, National University of Singapore
• BS in Mathematics, Indian Institute of Science

About Me

• Hometown:  New Delhi
• Field of Study:  Theoretical Computer Science
• PhD Advisor: Huy Lê Nguyen

What are the specifics of your graduate education (thus far)?

I completed my master’s degree in Mathematics from the National University of Singapore, during which time I gradually moved away from number theory and algebraic geometry and towards theoretical computer science.

What are your research interests in a bit more detail? Is your current academic/research path what you always had in mind for yourself, or has it evolved somewhat? If so, how/why?

I’m primarily interested in working on the theoretical underpinnings of machine learning, so as to help make these tools more reliable and efficient. This could include things as different as looking for differentially private alternatives to existing methods, or a clearer mathematical understanding of when these methods work and when they fail.

What’s one problem you’d like to solve with your research/work?

I would like to understand why neural networks work as well as they do, and whether we can find more objective principles of neural network design than the heuristics we currently use.

What aspect of what you do is most interesting/fascinating to you? What aspects of your research (findings, angles, problems you’re solving) might surprise others?

I find it most interesting how solving problems in computer science often requires the use of extremely disparate techniques in mathematics, often ones which I might not have initially expected, given the problem setting.

What are your research/career goals, going forward?

I’m currently primarily focussed on understanding the state of the art in theoretical computer science and machine learning (perhaps with a greater emphasis on topics like optimisation or privacy). Moving forward, I’d like to try and work on problems whose solutions help lead to a fairer and healthier society.