I am a Research Scientist in the Central Applied Science team at Meta. My research lies broadly in the areas of Private Machine Learning and Data Analysis, specifically focusing on improving the performance (privacy - utility tradeoffs) of privacy mechanisms.
I received my Ph.D. in Electrical and Computer Engineering from University of Illinois at Urbana-Champaign (2020). During my Ph.D., I developed privacy preserving algorithms for distributed computation (thesis). I was advised by Prof. Nitin Vaidya and Prof. Subhonmesh Bose. I received a Master’s degree in Applied Mathematics (Optimization and Algorithms Stream) from University of Illinois, Urbana-Champaign in 2018.
I was an intern at Toyota InfoTechnology Center (Summer 2016) with Lewis Tseng and Kentaro Oguchi and worked on developing distributed algorithms for driver-assist systems.
Prior to that, I received Bachelor's and Master's degrees in Aerospace Engineering at Indian Institute of Technology, Bombay (Dual Degree, 2013), where I was also awarded Research Award (URA03) for my Master's thesis.
Research: Google Scholar, Publications
Contact: <las-t-name>+<3> {at} illinois [dot] edu / Linkedin
August/September 2025: We will present a lecture-style tutorial on tabular synthetic data, reviewing state-of-the-art in synthetic data,
April 2025: Recent work on exploiting public-private partitioning in datasets to improve synthetic data generation mechanisms was presented as a spotlight talk at Synthetic Data x Data Access Problem workshop (ICLR 2025) in Singapore.