Current research topics include compression for distributed learning and inference, controlled sensing for quickest change detection, nonparametric methods for quickest change detection, robust inference and learning, nonstationary multi-armed bandits, Markov decision processes and reinforcement learning, out-of-distribution detection in machine learning, adaptive line search for distributed compressed stochastic gradient descent and applications to distributed learning, robust mean estimation in high dimensions, multiuser multi-armed bandits for dynamic spectrum access.
Background
Research interests span the theoretical areas of statistical inference, machine learning, and information theory, with applications to data science, wireless communications, sensor networks, and cyberphysical systems.
Miscellany
Classes taught at Illinois include ECE 490: Introduction to Optimization, ECE 361: Digital Communications, ECE 313: Probability with Engineering Applications, ECE 365: Data Science and Engineering, ECE 459: Communications I, ECE 534: Random Processes, ECE 561: Statistical Inference for Engineers and Data Scientists, ECE 562: Advanced Digital Communications, ECE 563: Information Theory, ECE 559: Communications III, ECE 559: Wireless Communications, ECE 471VV: Wireless Communication Networks.