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Computer Science Seminar Series

June 9, 2026

Abstract: In many modern applications—including machine learning, robotics, distributed systems, and network design—the input data, often represented as points in a finite metric space, can be massive in size. Efficient processing of such data requires compact representations that preserve the essential structural properties of the underlying space. Metric sketching provides a principled approach to achieving this compression. Among the most fundamental metric sketching primitives are spanners and tree covers, which capture distance relationships in a concise form. In the first part of the talk, Sujoy Bhore will discuss recent advances in geometric sketching. Traditional algorithmic models often assume complete knowledge of the input in advance; however, this assumption breaks down in evolving environments where the input changes over time. In these settings, algorithms must continuously adapt while maintaining strong performance guarantees. In the second part of the talk, Bhore will explore dynamic aspects of metric sketching, discuss related problems, and highlight emerging directions at the interface of geometry and uncertainty.

Speaker Biography: Sujoy Bhore is a faculty member in the Department of Computer Science and Engineering at the Indian Institute of Technology Bombay and a visiting fellow in the Department of Mathematics at the London School of Economics and Political Science. Previously, he held postdoctoral positions at TU Wien Informatics and in the Université libre de Bruxelles Department of Computer Science. Bhore received his PhD from the Stein Faculty of Computer and Information Science at Ben-Gurion University of the Negev. He has received a Kreitman Foundation Fellowship, a U.S.-Israel Binational Science Foundation fellowship, a London Mathematical Society Fellowship, and a Young Faculty Award and Krithi Ramamritham Award for Creative Research at IIT Bombay. Bhore’s research interests include computational geometry, algorithms, combinatorial optimization, and algorithmic aspects of machine learning.