Alex L. Wang
Assistant Professor of Quantitative Methods
Daniels School of Business
Purdue University
Office: KRAN 463
Email: wang5984@purdue.edu
[G. Scholar] [arXiv] [OO]
I am recruiting a postdoc for a project on statistical learning/distributed computing/optimization to be co-advised by Gesualdo Scutari and myself. Please see this link to learn about the Lillian Gilbreth Fellowships and this link for the proposed project. Application deadline November 1, 2023.
About
I am an assistant professor at Purdue University in the Daniels School of Business (Quantitative Methods Group).
I received my Ph.D. from the Computer Science Department at Carnegie Mellon University (2022), where I was supervised by Fatma Kılınç-Karzan. In Fall 2022, I was a postdoctoral researcher at Centrum Wiskunde & Informatica in the Optimization for and with Machine Learning project, where I was supervised by Monique Laurent.
My work has been recognized by the INFORMS Optimization Society 2021 Best Student Paper Award and an ICML Outstanding Paper Award 2022.
Research interests
My research focuses on extending classical optimization theory towards modern settings, especially those inspired by data science tasks.
Recent papers:
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Accelerated Gradient Descent via Long Steps
(ɑ) Benjamin Grimmer, Kevin Shu, and Alex L. Wang
September 2023
[arXiv] [mathematica proofs] -
Sharpness and well-conditioning of nonsmooth convex formulations in statistical signal recovery
(ɑ) Lijun Ding and Alex L. Wang
July 2023
[arXiv] [code] -
Hidden convexity, optimization, and algorithms on rotation matrices
(ɑ) Akshay Ramachandran, Kevin Shu, and Alex L. Wang
April 2023
[arXiv]