Vasileios Charisopoulos
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Assistant Professor |
I'm an assistant professor of Electrical & Computering Engineering at the University of Washington, Seattle. I'm also an affiliate faculty with the National Institute for Theory & Mathematics in Biology.
Prospective students: If you are interested in working with me, please apply to the UW ECE PhD program and mention my name in your application!
About me
Prior to joining UW, I was an AI & Science Postdoctoral Scholar at the UChicago Data Science Institute; my mentor was Rebecca Willett. I received my PhD in Operations Research & Information Engineering from Cornell University in 2023, where I was fortunate to be advised by Damek Davis. While at Cornell, I also collaborated with Anil Damle and Austin Benson from Cornell CS. I have been a research intern at Google Research, working on large-scale linear and quadratic programming solvers with the Algorithms and Optimization team, as well as Google Cloud, working on analytics for virtual machine scheduling. I received my Diploma in Electrical & Computer Engineering in 2017 from the National Technical University of Athens (NTUA), Greece, under the supervision of Petros Maragos, and worked as a researcher in the TROPICAL team at INRIA with Xavier Allamigeon and Stephane Gaubert before starting my PhD.
I was named a Rising Star in Computational and Data Sciences by the UT Austin Oden Institute in 2023 and received the Cornelia Ye Outstanding Teaching Assistant Award by Cornell University in 2021.
You can grab a recent copy of my CV here.
Selected talks
Multi-frequency progressive refinement for learned inverse scattering (Slides) (IFDS talk).
Nonlinear tomographic reconstruction via nonsmooth optimization (Slides).
A superlinearly convergent subgradient method for sharp semismooth problems (Slides).
Communication-efficient distributed eigenspace estimation (Slides).
Research
My research interests span continuous optimization, high-dimensional statistical estimation and numerical linear algebra. I have also worked on tropical geometry for machine learning and automated theorem proving.