James Foulds

James Foulds is an Associate Professor in the Department of Information Systems at the University of Maryland, Baltimore County (UMBC). His research focuses on socially conscious machine learning and artificial intelligence, emphasizing fairness, privacy, and computational social science using probabilistic models and Bayesian inference. He holds an NSF CAREER Award and a runner-up prize from the SoCal Machine Learning Symposium (2016). His work includes developing fair AI systems, privacy-preserving techniques, and Bayesian modeling approaches for intersectional fairness.

Dr. Foulds earned his postdoctoral training at UC San Diego and UC Santa Cruz, and completed his PhD at UC Irvine. He teaches courses in artificial intelligence, ethical AI, and data mining. His research has been supported by grants such as the NSF CISE Research Initiation Initiative (CRII) and the NSF EAGER grant for healthcare resource fairness.

Notable contributions include the Data Mining textbook (under revision), the Stochastic-CVB0 Julia package for topic modeling, and collaborations on AI fairness frameworks like the IEEE DEB Special Issue. He advises students on projects in fair machine learning, including equitable healthcare resource allocation and bias mitigation in resume filtering systems.