ML Seminar: Aldo Scutari (IE, Purdue)

CSE DSI Machine Learning seminars will be held Tuesdays 11a.m. - 12 p.m. Central Time in hybrid mode. We hope facilitate face-to-face interactions among faculty, students, and partners from industry, government, and NGOs by hosting some of the seminars in-person. See individual dates for more information.

This week's speaker, Aldo Scutari (IE, Purdue), will be giving a talk titled, "Statistical Inference over Networks: Decentralized Optimization Meets High-Dimensional Statistics".


There is growing interest in solving large-scale statistical machine learning problems over decentralized networks, where data are distributed across the nodes of the network and no centralized coordination is present (we termed these systems as “mesh” networks). Inference from massive datasets poses  a fundamental challenge at the nexus of the computational and statistical sciences: ensuring the quality of statistical inference when computational resources, like time and communication, are constrained.   While statistical-computation tradeoffs have been largely explored in the centralized setting, our understanding over mesh networks is limited: (i) distributed schemes, designed and performing well in the classical low-dimensional regime, can break down in the high-dimensional case; and (ii) existing convergence studies may fail to predict algorithmic behaviors, with some findings directly contradicted by empirical tests. This is mainly due to the fact that the majority of distributed algorithms  have been designed and studied only from the optimization perspective, lacking the statistical dimension. This talk will discuss some vignettes from  high-dimensional statistical inference suggesting  new analyses (and designs) aiming at bringing statistical thinking in distributed optimization.


Gesualdo Scutari  is a Professor with the School of Industrial Engineering and Electrical and Computer Engineering (by courtesy) at  Purdue University, West Lafayette, IN, USA, and he is a Purdue Faculty Scholar. His research interests include continuous optimization, equilibrium programming, 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 serves as an IEEE Signal Processing Distinguish Lecturer (2023-2024). He served on the editorial broad of several IEEE journals and he is currently an Associate Editor of SIAM Journal on Optimization. He is an IEEE Fellow.

Start date
Tuesday, Nov. 14, 2023, 11 a.m.
End date
Tuesday, Nov. 14, 2023, Noon

Keller Hall 3-180 or Via Zoom.