Li Ang Zhang

Li Ang Zhang
Codirector, Center for Scalable Computing and Analysis; Professor of Policy Analysis, Pardee RAND Graduate School; Information Scientist
He/Him

Education

Ph.D. in chemical engineering, University of Pittsburgh; B.S. in chemical engineering, Carnegie Mellon University

Overview

Li Ang Zhang (he/him) is an information scientist at RAND and a professor of policy analysis at Pardee RAND Graduate School. Additionally, he serves as the codirector of the RAND Center for Scalable Computing and Analysis. His research delves into the intersection of machine learning (ML), optimization, and mathematical modeling as applied to complex 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. Presently, his work delves into exploring the boundaries of AI in defense contexts, leveraging AI for enhanced space awareness, and conducting red-team assessments on large language models.

Prior to RAND, he specialized in developing model-based decision support systems to combat clinical challenges associated with sepsis, a severe inflammatory syndrome. He received his Ph.D. in chemical engineering from the University of Pittsburgh.

Selected Publications

Menthe, Lance, Li Ang Zhang, Edward Geist, Joshua Steier, Aaron B. Frank, Erik Van Hegewald, Gary J. Briggs, Keller Scholl, Yusuf Ashpari, and Anthony Jacques, Understanding the Limits of Artificial Intelligence for Warfighters: Volume 1, Summary, RAND Corporation (RR-A1722-1), 2024

Zhang, Li Ang, Yusuf Ashpari, and Anthony Jacques, Understanding the Limits of Artificial Intelligence for Warfighters: Volume 3, Predictive Maintenance, RAND Corporation (RR-A1722-3), 2024

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

Publications