Computer Science & Engineering,
The Ohio State University
My research interests span several areas such as privacy-preserving data analysis, machine learning, optimization, and information and coding 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 statistics, information and coding theory, and optimization.
My research focuses on tackling current challenges in data analysis and machine learning especially those of direct impact on society. Much of my recent research effort has been devoted to developing practical algorithms with rigorous guarantees for privacy-preserving data analysis. The goal of this area of research is to enable conducting highly accurate analyses over 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! Part of my research also addresses fundamental questions in machine learning and privacy.
Since Fall 2017, I have been an assistant professor in the Department of Computer Science and Engineering at The Ohio State University. 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, in 2012.
- Sep 2018: Our paper on Model-Agnostic Private Learning has been accepted for oral presentation at NIPS 2018.
- July 2018: I am serving on the PC of TPDP 2018 (Workshop on Theory & Practice of Differential Privacy). Submission deadline (2-4 page abstract): July 27 (new date!). The workshop includes a wide range of topics revolving around data privacy. If you have results related to differential privacy, you are encouraged to submit your work by the deadline here!
- June 2018: Our paper on mini-batch SGD for over-parameterized learning will be presented as a long talk at ICML!
- April 2018: Participating in BIRS program on Mathematical Foundations of Data Privacy, Banff, CA [Video of my talk].
- Jan 2018: Teaching a class on Privacy-Preserving Data Analysis and Differential Privacy this Spring!
- Dec 2017: Our paper "Learners That Leak Little Information" has been accepted to ALT 2018!
- Sep 2017: Our paper "Practical Locally Private Heavy Hitters" has been accepted at NIPS 2017!
- Aug 2017: Teaching the Machine Learning Theory class CSE 5523 this Fall!
- Aug 2017: Joined the CSE Department at OSU as Assistant Professor.
Note to Prospective Students:
I am looking for bright, self-motivated PhD students to recruit. If you are interested in working with me on one or more of the topics in the link below, feel free to send me a brief email with your background and your resume (including any relevant coursework you have done). Also, taking one of my classes (e.g., CSE 5223: Machine learning theory this Fall) and doing very well is a plus. If you are not currently admitted to OSU, you are encouraged to apply. If that is the case, you are still welcome to send me an email, but, please keep in mind that admissions are decided by the department/university committees.
Some Research Topics: here
- Anupama Nandi (PhD)