Privacy Preserving Machine Learning (IEEE FOCS 2022 Workshop)
Workshop on Algorithms for Large Data (Online) 2021
Models of Tesing
Vikrant Singhal
Sagnik Chatterjee
I am a Lecturer in the School of Computer Science of the University of Sydney . Prior to that, I was a postdoc first in the Stanford Theory Group , then at IBM Research Almaden . Even prior to that, I obtained my Ph.D. from the Computer Science department of Columbia University, where I was advised by Prof. Rocco Servedio . Long ago, in a distant land, I received a M.Sc. in Computer Science from the Parisian Master of Research in Computer Science , and an engineering degree from one of France's "Grand Schoo
My main areas of study are distribution testing (and, broadly speaking, property testing), learning theory , and, more generally, randomised algorithms and the theory of machine learning . One of my current focuses is on understanding the computational aspects of learning and statistical inference subject to various resource or information constraints. Another, not quite disjoint from the first, lies in reliable and rigorous approaches to data privacy, specifically differential privacy .
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Privacy Preserving Machine Learning (IEEE FOCS 2022 Workshop)
Workshop on Algorithms for Large Data (Online) 2021
Models of Tesing
Vikrant Singhal
Sagnik Chatterjee