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Research Talk Chasing the Constant: Bridging Theory and Practice in Privacy-Preserving Machine Learning
Export Event Save iCal Send to Google Calendar Send to Yahoo Calendar Send to Outlook Live Send to Microsoft Outlook Save iCal Preserve formatting in description (only supported in some calendar applications) Close Thursday, November 06, 2025, 10:30am - 12:00pm

Speaker: Jalaj Kumar Upadhyay

Location : CoRE 301

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Event Type: Research Talk

Abstract: At the heart of the first large-scale deployments of Google Gboard’s private next-word prediction and Apple’s private federated learning framework lies a simple and perhaps the most fundamental primitive: differentially private counting in the continual release model. This primitive serves as a subroutine not only in federated learning, but also in a wide range of applications, including histogram estimation, non-interactive local learning, graph statistics, stochastic convex optimization, and matrix analysis.In this talk, I will present my recent work establishing deep connections between private continual counting and a concept in operator algebra (factorization norms), which not only advances the foundations of privacy-preserving learning but also improves more than three decades-old results in operator algebra. I will also highlight my results in private graph analysis, where my work resolved an open problem in differential privacy, the first efficient and exact sampling algorithm from a logconcave distribution defined over non-convex sets, and improved results in discrepancy of shortest paths. I will conclude with my future plans. My goal is to design next-generation privacy-preserving deep learning algorithms that adapt to time-varying data sensitivity and system constraints, and to extend these methods into decentralized AI/ML solutions that reduce reliance on large data centers. This vision aims to make privacy-preserving machine learning both theoretically rigorous and practically deployable, paving the way for scalable, sustainable, responsible, and inclusive AI systems.

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Contact  Professor Jie Gao

Zoom
https://rutgers.zoom.us/j/93390185278?pwd=lxVEJ9OpJtuS53lNEVr6XaotJzo6QB.1
Meeting ID: 933 9018 5278
Password: 468326