Codes, Computation, and Privacy in Data Science
Data intensive tasks have been ubiquitous ever since the data science revolution. The immensity of contemporary datasets no longer allows computations to be done on a single machine, and distributed computations are inevitable. Since most users cannot afford to maintain a network of commodity servers, burdensome computations are often outsourced to third party cloud services. However, this approach opens a Pandora's box of potential woes, such as malicious intervention in computations, privacy infringement, and workload imbalance.
Error correcting codes are mathematical devices that were originally developed to obtain noise resilience in digital communication. Recently, these devices have found surprising applications in solving various problems in distributed computing. This newly emerging topic, which addresses resiliency, security, and privacy issues in distributed environments through a coding-theoretic lens, is often called coded computing. In this talk I will survey some of my work on the topic, which includes coding for distributed gradient descent, an exciting new framework called Lagrange Coded Computing, and finally, an important extension of Private Information Retrieval called Private Computation
Netanel received a B.Sc. in mathematics and computer science in 2010, an M.Sc. and Ph.D. in computer science in 2013 and 2017, respectively, all from the Technion, Israel. He is now a postdoctoral scholar at the Center for the Mathematics of Information (CMI) at the California Institute of Technology. He is an awardee of the IBM Ph.D. fellowship, the first prize in the Feder family competition for best student work in communication technology, and the Lester-Deutsche Postdoctoral Fellowship. His research interests include applications of coding techniques to computation, storage, and privacy.
Host: Professor Berk Sunar