Gautam Kamath is an Assistant Professor at the University of Waterloo's Cheriton School of Computer Science, and a faculty affiliate at the Vector Institute. He runs The Salon. He is interested in reliable and trustworthy statistics and machine learning, including considerations such as data privacy and robustness.

He was a Microsoft Research Fellow at the Simons Institute for the Theory of Computing for the Fall 2018 semester program on Foundations of Data Science and the Spring 2019 semester program on Data Privacy: Foundations and Applications. Before that, he completed his Ph.D. at MIT, affiliated with the Theory of Computing group in CSAIL. He was advised by Costis Daskalakis.

Before MIT, He spent four years at Cornell University, graduating in May 2012 with a degree in Computer Science and Electrical and Computer Engineering. At Cornell, he worked with Bobby Kleinberg.

Nilanjana is the Professor of Quantum Information Theory, in the Department of Applied Mathematics and Theoretical Physics (DAMTP) of the University of Cambridge, and a Fellow of Pembroke College. Since 2002, her research has been primarily in the field of Quantum Information Theory. She has been working on various topics in this field including quantum channels and their capacities, quantum source coding, quantum entropies, entanglement theory, concentration of quantum states, ergodicity of quantum channels, memory effects in quantum information theory and the subfield of one-shot information theory. She is one of the pioneers of the popular international workshop, entitled Beyond i.i.d. in Information Theory, which is held annually in different cities of the world since 2013.

Rashmi is an Assistant Professor in the Computer Science Department at Carnegie Mellon University, where she is a part of both the Systems group and the Theory group. She leads the CMU TheSys research group, and also a part of the Parallel Data Lab (PDL). Her research interests broadly lies in computer/networked systems and information/coding theory, and the wide spectrum of intersection between the two areas.

Her current focus is on robustness and resource efficiency in data systems spanning across the system stack including storage, communication, and computation. The key thrusts include storage and caching systems, systems for machine learning, and real-time video communication.

Abstract: Differential privacy is a rigorous and well-accepted notion of data privacy. It enables data analyses which are simultaneously useful and protect the privacy of individuals who provided their data. Differential privacy has been deployed in practice by a number of organizations, including the US Census Bureau, Google, and Apple. In this tutorial, we will discuss privacy attacks, failures of best-effort or heuristic approaches to privatization, the definition and properties of differential privacy, basic privatization mechanisms, methods for private machine learning, and potentially some real-world deployments of differential privacy.

Abstract: Entropies play a fundamental role in both classical and quantum information theory. Expressions for optimal rates of various information theoretic tasks are given in terms of entropic quantities. Entropies also satisfy a host of interesting mathematical properties. In this lecture series, different families of entropies that are relevant in information theory will be introduced and their properties and operational interpretations will be elucidated. The lecture series will end with a discussion of a novel, universal proof of uniform continuity bounds of various classical and quantum entropies, employing the elegant notion of majorization flow.

Abstract:Distributed systems often operate in regimes prone to failures, unavailabilities, and high variance in performance. Redundancy is a common approach employed to impart resilience and mitigate these adverse effects. Given the tight constraints on resources, this brings up the critical challenge of introducing resource-efficient redundancy at various levels of the system stack. Coding theory provides many valuable tools to address this challenge, and these tools are increasingly finding their way into modern computer systems. This tutorial will present coding theory tools for distributed data storage systems, distributed computation systems, and video-streaming communication. The topics will cover both theoretical results as well as practical aspects, including examples of real-world deployments.