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McKelvey School of Engineering

Brown School

ESE Seminar: Hong Hu

Thursday, February 15 | 11:00 AM - 12:00 PM

Preston M. Green Hall, Rodin Auditorium, L0120
135 N Skinker Blvd, St. Louis, MO 63112, USA

High-dimensional Information Processing: Optimality and Universality

Abstract: The proliferation of large-scale datasets has driven extensive applications of high-dimensional information processing algorithms in various fields. These algorithms, designed for distilling useful information from complex data, need to be grounded in rigorous understanding to ensure they are reliably and efficiently implemented. At the core of such comprehension are two pivotal questions: (1) How to design these algorithms in a principled way to achieve the optimal performance? (2) Can the theoretical analysis done in some idealized settings reflect the algorithms’ effectiveness in practical scenarios?

In this talk, I will present our work investigating these two questions by harnessing some unique and powerful properties emerging in high dimensions. In the first part of the talk, I will focus on signal recovery in the high-dimensional linear models and show how we can design the regularization function of SLOPE algorithm to achieve optimal statistical accuracy. In the second part, I will talk about a universality phenomenon in non-linear machine learning models that allows us to rigorously analyze the training and test errors, using results obtained in the linear models. Throughout the discussions, I will illustrate how the high-dimensional nature of data can be a blessing, enabling precise characterizations and optimal designs of information processing algorithms in practice.

Event Type

Seminar/Colloquia

Schools

McKelvey School of Engineering

Topic

Science & Technology

Department
Electrical & Systems Engineering
Hashtag

#Seminar

Event Contact

Aaron Beagle | abeagle@wustl.edu

Speaker Information

Hong Hu is currently a postdoctoral researcher in the Department of Statistics at the University of Pennsylvania. He received his Ph.D. in Engineering Sciences from Harvard University in 2021 and B.E. in Automation from Tsinghua University in 2015. His research interests lie in the fields of signal processing and machine learning, with a particular focus on developing theoretical underpinnings for algorithms that process high-dimensional data. His work aims to facilitate systematic and refined designs of information processing algorithms in real applications.

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