Speakers

Frank Hutter is a Hector-Endowed Fellow and PI at the ELLIS Institute Tübingen, as well as Full Professor for Machine Learning at the University of Freiburg (Germany). Frank holds a PhD from the University of British Columbia (UBC, 2009) and a Diplom (eq. MSc) from TU Darmstadt (2004). He received the 2010 CAIAC doctoral dissertation award for the best thesis in AI in Canada, and with his coauthors, several best paper awards and prizes in international competitions on machine learning, SAT solving, and AI planning. He is a Fellow of EurAI and ELLIS, the director of the ELLIS unit Freiburg and the recipient of 3 ERC grants. Frank is best known for his research on automated machine learning (AutoML), including neural architecture search, efficient hyperparameter optimization, and meta-learning. He co-authored the first book on AutoML and the prominent AutoML tools Auto-WEKA, Auto-sklearn and Auto-PyTorch, won the first two AutoML challenges with his team, is co-teaching the first MOOC on AutoML, co-organized 15 AutoML-related workshops at ICML, NeurIPS and ICLR, and founded the AutoML conference as general chair in 2022 and 2023. In recent years, his focus has been on the intersection of foundation models and AutoML, including the first foundation model for tabular data, TabPFN, and improving pretraining and fine-tuning with AutoML. Frank Hutter recently founded Prior Labs in 2025, a startup focused on building the next generation of tabular foundation models, along with publishing the TabPFNv2 paper in Nature.

Yan Liu is a full professor in the Computer Science Department, Viterbi School of Engineering at USC. I was an assistant professor from 2010 to 2016, and an associate professor from 2016 to 2020. Before joining USC, She was a research staff member at the IBM T.J. Watson Research Center from 2006 to 2010. She received my M.S. and Ph.D. from Carnegie Mellon University. Her research interests include machine learning for time series, physics-informed machine learning, and interpretable machine learning, with applications to health, sustainability, and social media.

Andrew Gordon Wilson is a Professor in the Courant Institute of Mathematical Sciences and Center for Data Science at New York University. He received his PhD in machine learning from the University of Cambridge, and BSc in mathematics and physics from the University of British Columbia. Prior to joining NYU, he was a professor at Cornell, and a postdoc at CMU. His work is focused on developing a prescriptive foundation for building intelligent systems. This work involves a mix of methods, empiricism, theory, and applications, often concerning deep neural networks, Gaussian processes, large language models, Bayesian methods, uncertainty representation, and scientific applications. He has been EXPO Chair, Tutorial Chair, Workshop Chair, and Senior Area Chair at the main machine learning conferences. He has also received several awards, including the NSF CAREER Award, the Amazon Research Award, and best paper, reviewer, area chair, and dissertation awards. Outside of work, Andrew is a classical pianist.

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge. In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM). Mihaela was elected IEEE Fellow in 2009 and Fellow of the Royal Society in 2024. She has received numerous awards, including the Johann Anton Merck Award (2024), the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award. She was a Turing Fellow at The Alan Turing Institute in London between 2016 and 2024. Mihaela is personally credited as inventor on 35 USA patents (the majority of which are listed here), many of which are still frequently cited and adopted in standards. She has made over 45 contributions to international standards for which she received 3 ISO Awards. In 2019, a Nesta report determined that Mihaela was the most-cited female AI researcher in the U.K.

Josh Gardner is a Research Scientist on the Foundation Modeling team at Apple. Prior to joining Apple, Josh completed his PhD at the University of Washington, advised by Ludwig Schmidt. Josh’s research centers on the empirical foundations of machine learning – in particular, the impact of data on foundation models, and improving foundation models’ understanding of new data modalities beyond images and text.

Hao Wang is an assistant professor in the Department of Computer Science at Rutgers University, where I direct a Machine Learning Lab. Previously I was a Postdoctoral Research Associate at the Computer Science & Artificial Intelligence Lab of Massachusetts Institute of Technology, working with Prof. Dina Katabi and Prof. Tommi Jaakkola. He obtained Ph.D degree in CSE department, Hong Kong University of Science and Technology. His research interest focuses on statistical machine learning, deep learning, and large language models (LLMs). Currently, he mainly work on Bayesian deep learning, probabilistic methods, game-theoretic approaches, and their applications in trustworthy & safe AI (interpretability, robustness, alignment, etc.), healthcare, recommender systems, computer vision (including multimodal LLMs), natural language processing (including LLMs), network analysis, and data mining. He is also a Microsoft Fellow and received the Baidu Research Fellowship.