Ph.D. in chemical engineering, University of Pittsburgh; B.S. in chemical engineering, Carnegie Mellon University
Li Ang Zhang (he/him) is an information scientist at the RAND Corporation and a professor at Pardee RAND Graduate School. His research interests include applying machine learning (ML), optimization, and mathematical modeling towards defense and technology policy challenges. In particular, he focuses on ML implementation challenges, including assessing edge ML devices and identifying the implications of adversarial examples. Most recently, he is investigating the limitations of AI in defense applications.
Prior to joining RAND, he focused on creating model-based decision support systems to tackle clinical problems in sepsis, a severe inflammatory syndrome. He received his Ph.D. in chemical engineering from the University of Pittsburgh.
Li Ang Zhang, Jia Xu, Dara Gold, Jeff Hagen, Ajay K. Kochhar, Andrew J. Lohn, Osonde A. Osoba, Air Dominance Through Machine Learning, RAND Corporation (RR-4311), 2020
Hartnett, Gavin S., Lance Menthe, Jasmin Léveillé, Damien Baveye, Li Ang Zhang, Dara Gold, Jeff Hagen, and Jia Xu, Operationally Relevant Artificial Training for Machine Learning: Improving the Performance of Automated Target Recognition Systems, RAND Corporation (RR-A683-1), 2020
Sherrill Lingel, Jeff Hagen, Eric Hastings, Mary Lee, Matthew Sargent, Matthew Walsh, Li Ang Zhang, David Blancett, Joint All-Domain Command and Control for Modern Warfare: An Analytic Framework for Identifying and Developing Artificial Intelligence Applications, RAND Corporation (RR-4408z1), 2020
Silberglitt, Richard, Cynthia R. Cook, Steven W. Popper, Lisa Pelled Colabella, Paul Dreyer, Eric Hastings, Alexander C. Hou, Alexis Levedahl, Edward Parker, Scott Savitz, and Li Ang Zhang, Systematic Method for Prioritizing Investments in Game-Changing Technologies: The Evaluation and Comparison Process Framework, RAND Corporation (RR-A632-1), 2022
