Group testing is the problem of inferring a (hopefully) small subset of items/individuals of interest from a large populations via pooled/group tests — test outcomes are positive if and only if the pool being tested contains at least one item/individual of interest. Canonical examples include identifying diseased individuals in a population, item identification in RFID systems, identification of defective products in industrial systems, and streaming algorithms. The theory and algorithms also offer insights into more general non-linear sparse inverse problems. In this talk I'll survey some classical fundamental bounds and algorithms for a variety of models, and present some recent results.
Sidharth (Sid) Jaggi (B.Tech. IIT Bombay 2000, M.S./Ph.D. CalTech 2006, all in electrical engineering, post-doctoral associate MIT 2006). He joined The Chinese University of Hong Kong in 2007, and the School of Mathematics at the University of Bristol in 2020, where he is currently a Professor of Information and Coding Theory. His research group (somewhat unwillingly) calls itself the CAN-DO-IT Team (Codes, Algorithms, Networks: Design and Optimization for Information Theory). Topics he has worked in include sparse recovery/group-testing, covert communication, network coding, and adversarial channels.