Alex L. Wang

Assistant Professor of Quantitative Methods
Daniels School of Business
Purdue University

Office: KRAN 463

[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.

If you are a motivated student interested in optimization and/or data science, I encourage you to apply to Purdue’s PhD program in Quantitative Methods and to mention my name in your application. See here for details. Application deadline January 15, 2024.


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 topics of interest include: semidefinite programming, first-order methods, and nonconvex quadratic programming.

Recent papers:

Last updated Sep 22, 2023