Tom Goldstein

Tom Goldstein is the Volpi-Cupal Endowed Professor of Computer Science at the University of Maryland, with appointments in Applied Mathematics and Electrical and Computer Engineering. His research focuses on AI system development, optimization methods, and their applications in computer vision and signal processing. He emphasizes the intersection of theoretical foundations and practical hardware implementations.

Education: PhD in Applied Mathematics from UCLA (2010), BA from Washington University (2006). Prior to UMD, he held postdoctoral positions at Stanford University and Rice University.

Research interests include AI security/privacy, algorithmic bias, diffusion models, and large language model robustness. His work addresses challenges in model watermarking, adversarial attacks, and ethical AI deployment.

Notable awards include the Sloan Research Fellowship (2017), DARPA Young Faculty Award, and JPMorgan Faculty Research Award. He directs the Maryland Center for Machine Learning and has advised over 20 graduate students in AI-related disciplines.

Recent research trends emphasize multimodal systems, generative model analysis, and scalable training techniques. His work on diffusion models explores style similarity and content authenticity, while watermarking studies address anti-plagiarism and data provenance.

Labs/Teams: Leads the Maryland Center for Machine Learning, collaborating with UMIACS and ECE departments. Active in open-source AI initiatives and supercomputing applications for large model training.