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1. The Power of Encounters 2. Algorithmic fairness in online decision-making

Series: CSA Golden Jubilee Frontier Lecture Series

Speaker: 1. Prof. Peter Druschel Director Max Planck Institute for Software Systems 2. Prof. Avrim Blum Professor and Chief Academic Officer. Toyota Technological Institute at Chicago (TTIC) Chicago, USA

Date/Time: Jan 29 15:00:00

Location: Faculty Hall, Indian Institute of Science

Faculty Advisor:

1. A secure encounter is an agreement by two anonymous devices to have met at a given time and place. An associated shared secret enables the devices to subsequently confirm their encounter and communicate securely. In this talk, I will sketch how this simple idea enables fascinating new forms of privacy-preserving, contextual, secure communication among personal and IoT devices, and enables users to produce selective evidence of their personhood and physical whereabouts. Encounters enable powerful forms of secure group communication among devices connected by chains of encounters, subject to spatial, temporal, and causality constraints. Applications range from connecting event attendees and virtual guest books to disseminating targeted health risk warnings, soliciting information and witnesses related to an incident, and tracing missing persons, all while maintaining users’ privacy. Encounters also enable selective proofs of device (co-)location at a given time and place. Finally, encounters can provide evidence of a unique physical trajectory, which suggests a human user and promises a new defense to Sybil attacks. 2. There is growing concern about fairness in algorithmic decision making: Are algorithmic decisions treating different groups fairly? How can we make them fairer? What do we even mean by fair? In this talk I will discuss some of our work on this topic, focusing on the setting of online decision making. For instance, a classic result states that given a collection of predictors, one can adaptively combine them to perform nearly as well as the best of those predictors in hindsight (achieve “no regret”) without any stochastic assumptions. Can one extend this guarantee so that if the predictors are themselves fair (according to a given definition), then the overall combination is fair as well (according to the same definition)? I will discuss this and other issues. This is joint work with Suriya Gunasekar, Thodoris Lykouris, and Nati Srebro.

Speaker Bio:
1. Peter Druschel is the founding director of the Max Planck Institute for Software Systems (MPI-SWS) and Associate Chair of the Chemistry, Physics, and Technology Section of the Max Planck Society in Germany. Previously, he was a Professor of Computer Science and Electrical and Computer Engineering at Rice University in Houston, Texas. His research interests include distributed systems, mobile systems, privacy and compliance. He is the recipient of an NSF CAREER Award, an Alfred P. Sloan Fellowship, the ACM SIGOPS Mark Weiser Award, a Microsoft Research Outstanding Collaborator Award, and the EuroSys Lifetime Achievement Award. Peter is a member of Academia Europaea and the German Academy of Sciences Leopoldina. 2. Avrim Blum received his BS, MS, and PhD from MIT in 1987, 1989, and 1991 respectively. He then served on the faculty in the Computer Science Department at Carnegie Mellon University from 1992 to 2017. In 2017 he joined the Toyota Technological Institute at Chicago as Chief Academic Officer. Prof. Blum’s main research interests are in Theoretical Computer Science and Machine Learning, including Machine Learning Theory, Approximation Algorithms, Algorithmic Game Theory, and Database Privacy, as well as connections among them. Some current specific interests include multi-agent learning, multi-task learning, semi-supervised learning, and the design of incentive systems. He is also known for his past work in AI Planning. Prof. Blum has served as Program Chair for the IEEE Symposium on Foundations of Computer Science (FOCS), the Innovations in Theoretical Computer Science Conference (ITCS), and the Conference on Learning Theory (COLT). He has served as Chair of the ACM SIGACT Committee for the Advancement of Theoretical Computer Science and on the SIGACT Executive Committee. Prof. Blum is recipient of the AI Journal Classic Paper Award, the ICML/COLT 10-Year Best Paper Award, the Sloan Fellowship, the NSF National Young Investigator Award, and the Herbert Simon Teaching Award, and he is a Fellow of the ACM.

Host Faculty: Prof. Shalabh Bhatnagar