Stephan Mandt

Stephan Mandt is an Associate Professor of Computer Science and Statistics at the University of California, Irvine. Previously, he led the machine learning group at Disney Research in Pittsburgh and Los Angeles and held postdoctoral positions at Princeton and Columbia University. Stephan holds a Ph.D. in Theoretical Physics from the University of Cologne, where he received the German National Merit Scholarship. He is furthermore a recipient of the NSF CAREER Award, the UCI ICS Mid-Career Excellence in Research Award, the German Research Foundation’s Mercator Fellowship, a Kavli Fellow of the U.S. National Academy of Sciences, a member of the ELLIS Society, and a former visiting researcher at Google Brain. His research is currently supported by NSF, DARPA, IARPA, DOE, Disney, Intel, and Qualcomm. Stephan is an Action Editor of the Journal of Machine Learning Research and Transaction on Machine Learning Research, held tutorials at NeurIPS, AAAI, and UAI, and regularly serves as an Area Chair for NeurIPS, ICML, AAAI, and ICLR. He currently serves as Program Chair for AISTATS 2024 and will continue to serve as General Chair for AISTATS 2025.

The following research areas are of interest to the group:

• Deep Generative Models: We have a broad interest in deep generative models such as variational autoencoders and diffusion models, aiming to improve their scope (e.g., video diffusion, factorized VAEs, point process models) and inference efficiency (e.g., augmented spaces, iterative inference etc).
• Uncertainty Quantification: Our group focuses on teaching neural networks to “know what they don’t know”. To this end, proper uncertainty quantification and calibration is crucial (e.g., through variational inference, ensemble methods, Bayesian neural networks, approaches inspired by statistical physics etc.)
• Neural Data Compression: We are dedicated to exploring methods that seek to explore the potential of deep learning-based approaches as alternatives to conventional image and video codecs for data compression.
• Machine Learning and Science: Our research explores the applications of machine learning in physics, chemistry, climate science, and related domains. We also investigate physics-inspired machine learning algorithms and theories.
• PhD student applicants: Unfortunately, I will not be able to respond to most inquiries regarding PhD openings or comment on your applications to my group. If you indicate your interests to work with me in the application questions, I will make sure to review them carefully. Online Application page.
• UCI undergraduate students, read this first: Thank you for your interest in working on research projects with us. Due to the high demand for generative AI opportunities we can only accommodate a limited number of students each year. When reaching out, kindly include your resume and UCI transcript and describe what kind of research interests you the most. You should have already excelled in CS 178 with a top grade (A or A+) and ideally have taken additional courses in AI/ML. Your understanding of these constraints is greatly appreciated.

Education

Ph.D., Theoretical Physics, University of Cologne