About me

I am an Associate Professor in the Department of Computer Science and Engineering and a core faculty member at the Translational Data Analytics Institute (TDAI) at The Ohio State University. I am also a part-time visiting scientist at Google. My research interests span several areas such as privacy-preserving data analysis, machine learning, optimization, and information theory. I am broadly interested in studying the tension/harmony between data analysis and machine learning on one hand and other notions of central importance to people and society such as privacy and security. In my work, I enjoy applying various tools from several areas such as learning theory, optimization, statistics, and information theory.

My research focuses on tackling current challenges in data analysis and machine learning especially those of direct impact on society. My recent research effort has been devoted to developing practical algorithms with rigorous guarantees for privacy-preserving data analysis and machine learning. The goal of this area of research is to design highly accurate machine learning and data analysis algorithms that use private, personal data while providing rigorous guarantees of privacy for individuals whose data are collected; that is, to achieve the seemingly paradoxical goal of learning from private data without learning private data! A major part of my research focuses on addressing fundamental questions in machine learning and differential privacy.

Before joining OSU, I was a data-science postdoctoral fellow at University of California, San Diego. Prior to this, I was a postdoc in the Department of Computer Science and Engineering at The Pennsylvania State University. I completed my PhD in Electrical and Computer Engineering at University of Maryland, College Park.

I am supported by multiple NSF grants, including the NSF CAREER Award, and a Google Faculty Research Award. I am also a recipient of OSU's 2022 Lumely Faculty Research Award.

Curriculum Vitae

News:

May 2023: Two papers to appear at ICML 2024!
December 2023: A paper on private non-convex optimization under KL condition to appear at ALT 2024!
May 2023: I am now an Associate Professor with Tenure!
April 2023: Two papers to appear at ICML 2023!
December 2022: One paper to appear at AISTATS 2023!
Sep 2022: Three papers to appear at NeurIPS 2022!
May 2022: I got the NSF CAREER Award to support my research on extending the foundations of privacy-preserving machine learning!
April 2022: I am honored to receive OSU's College of Engineering Lumely Faculty Research Award for year 2022!
April 2022: Two new papers on differentially private learning and stochastic optimization (links to preprints: paper 1, paper 2)
September 2021: Our paper on Differentially Private Stochastic Optimization in Convex and Non-convex Settings is accepted at NeurIPS 2021.
August 2021: The NSF AI Institute for Future Edge Networks and Distributed Intelligence (AI-EDGE) is officially announced!! A $20M AI Institute funded by NSF. AI-EDGE is led by OSU (PI: Prof. Ness Shroff), with 29 investigators coming from 11 leading research universities.
June 2021: I am currently a part-time visiting scientist at Google, NYC!

Ph.D. Students:

Current:

Michael Menart (PhD candidate): Fall 2019 - Present
Xinyu Zhou (PhD): Fall 2020 - Present
Conor Snedeker (PhD): Spring 2023 - Present

Former:

Anupama Nandi (PhD): Spring 2018 - Spring 2022 (Graduated).

Open PhD Positions:

There are currently open Graduate Research Assistantship positions in my group!

I am looking for bright, self-motivated PhD students. If you are interested in working with me (check out the research directions above), feel free to send me a brief email with your background and your resume (and any relevant coursework you have done).

If you are not currently admitted to OSU, you are encouraged to apply! Please keep in mind that admissions are decided by the department/university committees.