Dr. James Richard Foulds
Associate Professor · Tenured
Department of Computer Science and Electrical Engineering Department of Information Systems
College of Engineering and Information Technology
He/Him/His/Himself
About
Dr. James Foulds is an Assistant Professor in the Department of Information Systems at UMBC. His research aims to improve the role of artificial intelligence in society regarding fairness and privacy, and to promote the practice of computational social science. He received the NSF CAREER and CRII awards for his work on fairness and bias in artificial intelligence. He earned his Ph.D. in computer science at the University of California, Irvine. He was a postdoctoral scholar at the University of California, Santa Cruz, followed by the University of California, San Diego. His master’s and bachelor’s degrees were earned with first-class honours at the University of Waikato, New Zealand, where he also contributed to the Weka data mining system.
Research interests
My research interests are broadly in the area of socially conscious machine learning and artificial intelligence. My work aims to improve AI’s role in society regarding fairness and privacy, and to promote the practice of computational social science, using probabilistic models and Bayesian inference.
Teaching interests
Machine learning, data mining, artificial intelligence
Education
- Postdoc — University of California, San Diego (2017)
- Postdoc, Computer Science — University of California, Santa Cruz (2015)
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Ph D, Computer Science
— University of California, Irvine (2014) Latent Variable Modeling for Networks and Text: Algorithms, Models and Evaluation Techniques
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MS, Computer Science
— University of Waikato (2008) Learning Instance Weights in Multi-Instance Learning
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Other, Computer Science
— University of Waikato (2006) Learning to play the game of go
Presentations
- Can Generative AI be Egalitarian? 2025
Grants and Contracts
- III: Small: SocialAnnotator: Selecting Efficient Data Annotators by Exploiting Social Relationships and Contexts 2019
- CRII: RI: A Little Uncertainty is Good for Everyone: Bayesian Models for Fairness, and Fairness for Bayesian Models 2019
- PIPP Phase I: Integrating Data, Knowledge, and Expertise Through Modeling and Immersive Analytics for Equitable Pandemic Prevention
- SCH: GDRSense: Robust Sensing and Fair Predictive Models for Psychotropic Medication Management
Research in Progress
- A Mandate Beyond Accuracy: Forging the Algorithm's Social Contract 2025
Courses Taught
- Data Mining