Praneeth Vepakomma, a doctoral student at MIT, will deliver a talk as part of CSRAI's Young Achievers Symposium, will deliver a talk as part of CSRAI's Young Achievers Symposium.
"Differential Privacy for Measuring Nonlinear Correlations between Sensitive Data at Multiple Parties”
Vepakomma’s work introduces a differentially private method to measure nonlinear correlations between sensitive data hosted across two entities. His group provides utility guarantees of their private estimator. Theirs is the first such private estimator of nonlinear correlations, to the best of their knowledge within a multi-party setup. The important measure of nonlinear correlation they consider is distance correlation. This work has direct applications to private feature screening, private independence testing, private k-sample tests, private multi-party causal inference and private data synthesis in addition to exploratory data analysis.
About the Speaker
Praneeth Vepakomma is currently a Ph.D. student at MIT. His research focuses on developing algorithms for distributed computation in statistics and machine learning under constraints of privacy, communication and efficiency. He won the Meta (previously Facebook) Ph.D. research fellowship and has been selected as a Social and Ethical Responsibilities of Computing Scholar by MIT’s Schwarzman College of Computing. He won a Baidu Best Paper Award at NeurIPS 2020-SpicyFL for his work on FedML. His work on NoPeek-Infer won the Mukh Best Paper Runner Up Award at IEEE FG-2021. He was Interviewed in the book, Data Scientist: The Definitive Guide to Becoming a Data Scientist, and his work on Split Learning was featured in Technology Review. He was previously a scientist at Apple (intern), Amazon, Motorola Solutions, PublicEngines, Corning (intern), and various startups, all of which were eventually acquired. A small sampling of problems that he works on includes private independence testing and private k-sample testing in statistics, bridging privacy with social choice theory, private mechanisms for training and inference in ML, privately estimating non-linear measures of statistical dependence between multiple parties, and split learning.
About the Young Achievers Symposium
The Young Achievers Symposium highlights early career researchers in diverse fields of AI for social impact. The symposium series seeks to focus on emerging research, stimulate discussions, and initiate collaborations that can advance research in artificial intelligence for societal benefit. All events in the series are free and open to the public unless otherwise noted. Penn State students, postdoctoral scholars, and faculty with an interest in socially responsible AI applications are encouraged to attend.