Event
ECE Colloquium Series - Gesualdo Scutari, Purdue University
Friday, February 7, 2025
3:30 p.m.-4:30 p.m.
Jeong H. Kim Engineering Building, Room 1110
Darcy Long
301 405 3114
dlong123@umd.edu
Speaker: Gesualdo Scutari, Professor, Purdue University
Talk Title: Statistical inference over networks: Decentralized optimization meets high-dimensional statistics
Abstract: The interest in solving large-scale inferential problems within decentralized networks is rapidly increasing, particularly in systems where data are distributed across network nodes without centralized coordination, often referred to as “mesh” networks. Inference from massive datasets presents critical challenges at the intersection of computational and statistical sciences, particularly ensuring the quality of the performed analytic despite limited computation and communication resources at the network edge. While the trade-offs between statistical accuracy and computational efficiency have been studied in centralized settings, our comprehension in the context of mesh networks remains underdeveloped. This is because most decentralized algorithms have been designed with an optimization focus, often neglecting statistical principles. This talk discusses some vignettes from high-dimensional statistical inference, proposing new analyses and designs that bring statistical thinking to decentralized optimization, enhancing performance and reliability in high-dimensional, (stochastic) decentralized environments.
Bio: Gesualdo Scutari is the Pedro and Barbara Granadillo Professor in the Edwardson School of Industrial Engineering and the Elmore Family School of Electrical and Computer Engineering at Purdue University, West Lafayette, IN, USA. His research interests include continuous (distributed, stochastic) optimization, game theory, and their applications to signal processing and statistical learning. Among others, he was a recipient of the 2013 NSF CAREER Award, the 2015 IEEE Signal Processing Society Young Author Best Paper Award, and the 2020 IEEE Signal Processing Society Best Paper Award. He served as an IEEE Signal Processing Distinguish Lecturer (2023-2024) and on the editorial broad of several IEEE journals. Currently, he is an Associate Editor of the SIAM Journal on Optimization. He is a fellow of IEEE.
