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.

Dr Yingzhen Li is a Senior Lecturer in Machine Learning at Imperial College London, UK. Before that she worked at Microsoft Research Cambridge and Disney Research. She received her PhD from the University of Cambridge. Yingzhen is passionate about building reliable machine learning systems with probabilistic methods, and her published work has been applied in industrial systems and implemented in popular deep learning frameworks. She is a regularly invited speaker at international machine learning conferences and machine learning summer schools, and she gave an invited tutorial on approximate inference at NeurIPS 2020. Her work on Bayesian ML has also been recognised in AAAI 2023 New Faculty Highlights. She has co-organised many international research workshops on probabilistic inference and deep generative models. She regularly serves as Area Chair for ICML, ICLR and NeurIPS, and she is a Program Chair for AISTATS 2024. When not at work, Yingzhen enjoys reading, hiking, video games, and following news on latest technology developments.

I'm a PhD student studying machine learning at the University of California, Irvine, where I am advised by Dr. Padhraic Smyth. My work is supported by an HPI Fellowship.

Research interests: generative models, machine learning in non-Euclidean spaces, stochastic processes, functional data analysis

Previously: BSc in Mathematics at the Pennsylvania State University Schreyer Honors College.

Miguel Ángel Fernández-Torres received the Audiovisual Systems Engineering degree, the Master degree in Multimedia and Communications and the PhD degree in Multimedia and Communications from Universidad Carlos III de Madrid, Spain, in 2013, 2014 and 2019, respectively. During the PhD period, his research was related to spatio-temporal visual attention modelling and understanding, applying both Bayesian networks and deep learning. After two years as a teaching assistant at Universidad Carlos III de Madrid, Spain, he works at present as a postdoctoral researcher in the ERC USMILE, H2020 XAIDA and ESA DeepExtremes projects, taking part in the Image and Signal Processing Group at the Universitat de València, Spain. His current research within the area of Machine Learning for Earth and Climate Sciences involves the design and understanding of explainable deep generative models and machine attention mechanisms to be deployed for anomaly and extreme event detection.

In addition to his work on visual attention, he has participated in other projects within the Computer Vision field, which includes image and video analysis, and medical image analysis and classification. He had also the opportunity to study at Technische Universität Wien, Vienna, Austria, during his Bachelor's degree, in 2013, and to do two research stays at …