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 RAND and a professor at Pardee RAND Graduate School. He is also the codirector of the RAND Center for Scalable Computing and Analysis. 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.
Zhang, Li Ang, Gavin S. Hartnett, Jair Aguirre, Andrew J. Lohn, Inez Khan, Marissa Herron, and Caolionn O'Connell, Operational Feasibility of Adversarial Attacks Against Artificial Intelligence, RAND Corporation (RR-A866-1), 2022
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
