Biography

Stephan Mandt is an Associate Professor of Computer Science and Statistics at the University of California, Irvine, where he leads research at the intersection of deep generative modeling, uncertainty quantification, neural data compression, and AI for science. His work advances the foundations and applications of generative AI, with a particular focus on resource-efficient learning and inference algorithms, as well as AI-driven scientific discovery.

Before joining UCI, Stephan directed the machine learning group at Disney Research in Pittsburgh and Los Angeles. He previously held postdoctoral positions at Princeton and Columbia University. He earned his Ph.D. in Theoretical Physics from the University of Cologne as a recipient of the German National Merit Scholarship.

Stephan's research is supported by NSF, DARPA, IARPA, DOE, Disney, Intel, and Qualcomm. He has been recognized as a Chan Zuckerberg Investigator and AI Resident, and he has received the NSF CAREER Award, the UCI ICS Mid-Career Excellence in Research Award, and the German Research Foundation's Mercator Fellowship. He is also 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.

An active contributor to the machine learning community, Stephan serves as an Action Editor for the Journal of Machine Learning Research and Transactions on Machine Learning Research. He has organized tutorials at NeurIPS, AAAI, and UAI and regularly serves as Senior Area Chair for NeurIPS, ICML, and ICLR. He recently served as Program Chair for AISTATS 2024 and as General Chair for AISTATS 2025.

Short Bio

Stephan Mandt is an Associate Professor of Computer Science and Statistics at the University of California, Irvine. His research contributes to the foundations and applications of generative AI, with a focus on generative modeling of 2D, 3D, and sequential data, compression, resource-efficient learning, inference algorithms, and AI-driven scientific discovery. He is a Chan Zuckerberg Investigator and AI Resident and has received the NSF CAREER Award, the UCI ICS Mid-Career Excellence in Research Award, and a Kavli Fellowship. Before UCI, he led the machine learning group at Disney Research and held postdoctoral positions at Princeton and Columbia. Stephan frequently serves as a Senior Area Chair for NeurIPS, ICML, and ICLR and was most recently Program Chair for AISTATS 2024 and General Chair for AISTATS 2025.


Research Interests

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.

    News

    • Serving as General Chair for AISTATS 2025 and Program Chair for AISTATS 2024 (with Yingzhen Li).
    • Appointed Guest Professor at ETH Zurich (2025).
    • Named a Chan Zuckerberg AI Resident (2024), supporting a group of several PhD students and postdocs at UCI.
    • Awarded the Outstanding Paper Award at NeurIPS 2023.
    • Awarded the Mercator Fellowship of the German Research Foundation (DFG).
    • Received the Dean's Mid-Career Award for Excellence in Research.
    • Honored with the NSF CAREER Award for Variational Inference for Resource-Efficient Learning.
    • Visiting Professor, Google Brain (2019).

    Tutorials