Vikrant Singhal

I am a Research Associate (since 2023) at Harvard University working for OpenDP, supervised by Salil Vadhan. My interests generally lie in CS Theory and Algorithms, though I mainly work on problems in the domain of Differential Privacy. I'm also interested in questions related to Statistics and Machine Learning, and my recent focus has been on the intersection of Privacy, Statistics, and Learning.

From 2021 to 2023, I was a postdoc at the University of Waterloo, where I had an amazing time working under Gautam Kamath.

Before that, I got my PhD and master's in 2021 from Northeastern University, where I was fortunate to be advised by Jonathan Ullman. I was a Research Intern at IBM Research for Summer 2020, where I was hosted by Thomas Steinke. I visited the University of Waterloo in Fall 2019, where I was supervised by Gautam Kamath. I was also a visiting graduate student at the Simons Institute for the Theory of Computing (University of California, Berkeley) for their Spring 2019 program: Data Privacy: Foundations and Applications.

Prior to joining Northeastern University, I was an undergrad at the University of Southern California, graduating in 2016 with a bachelor's in Computer Science, and a minor in Mathematics. At USC, I had a wonderful opportunity to work with David Kempe on problems related to graphs.

I have been extremely lucky to have had so many opportunities to collaborate closely and publish with multiple (13), really smart and cool researchers! Here is a list of them in the alphabetical order of their surnames: Shai Ben-David, Alex Bie, Mark Bun, Clément Canonne, Ehsan Emamjomeh-Zadeh, Gautam Kamath, David Kempe, Jerry Li, Argyris Mouzakis, Matthew Regehr, Or Sheffet, Thomas Steinke, and Jonathan Ullman.