I am interested in decision-making, learning, and resource allocation/scheduling problems that arise in networking and information systems, in general. My current research focus is on efficient inference in Edge AI systems, working on Hierarchical Inference, Inference Offloading, and the Age of Information. Some of the mathematical tools used in my research so far include Regret Analysis, Design and Analysis of Approximation Algorithms, Queuing Theory, Stochastic Network Calculus,  and Markov Decision Processes.

Feb 2024: Presented Improved Decision Module for Hierarchical Inference at AAAI Deployable AI workshop.

Feb 2024: Our first work on Hierarchical Inference is published in IEEE Transaction on Machine Learning in Communications and Networking (TMLCN). Congrats Vishnu! Pdf available on arXiv.

Feb 2024: Happy to be recognized as a Distinguished Member of INFOCOM 2024 TPC!

October 2023: Invited talk: “Getting the Best of Both Worlds (IoT and Edge/cloud) using Hierarchical Inference”, IEEE World Forum on IoT, Oct. 2023.

October 2023: Poster: Improved Decision Module Selection for Hierarchical Inference in Resource-Constrained Edge Devices, Mobicom 2023.

May 2023: The Case for Hierarchical Deep Learning Inference at the Network Edge is accepted NetAI workshop, MobiSys 2023.

April 2023: Our work on Offloading Algorithms for ML inference offloading was accepted to IEEE Transactions on Parallel and Distributed Systems (TPDS). Congrats Andrea Fresa!

March 2023: Our work on Online Algorithms for Hierarchical Inference at the Network Edge is available on arXiv.

January 2023: Getting the Best of Both Worlds (IoT and Edge) using Hierarchical Inference, invited talk at Indian Institute of Science (IISC), Bengaluru, India.