Hello all, I hope to remind you about this event. Note that the location is updated to Johnson 102. More details are here: https://research.engr.oregonstate.edu/soda/upcoming-events<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fresearch.engr.oregonstate.edu%2Fsoda%2Fupcoming-events&data=05%7C02%7Cai%40engr.orst.edu%7C2f75d13f67964c7987a108dc54fb8a80%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638478686528761150%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=ZDoNDizBDU0wNNKysit%2F94i5wgpj3icauww70PlqItc%3D&reserved=0> Cheers, Xiao Time: 1pm-2pm, April 11 Venue: Johnson 102 Title: Statistical Inference over Networks: Decentralized Optimization Meets High-dimensional Statistics Abstract: Abstract: There is growing interest in solving large-scale statistical 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. Speaker: Bio: 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 (distributed) 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 a fellow of IEEE. ======================= Xiao Fu, Ph.D. Associate Professor School of Electrical Engineering and Computer Science Oregon State University Corvallis OR 97331 3003 Kelley Engineering Center Homepage: https://web.engr.oregonstate.edu/~fuxia/