Prof. Johannes Lederer
University of Hamburg, Germany
Johannes is a data‑science professor, podcaster and entrepreneur. He holds the Chair of Mathematics for Data‑Driven Methods at the University of Hamburg. He earned his PhD at ETH Zürich and has held academic posts in Europe and the US. He is NeurIPS 2026 Area Chair, journal editor, and a regular keynote speaker on data science and AI.
Abstract 摘要
Extreme-value theory has deep roots in classical statistics, while modern AI has been propelled by advances in engineering and scaling. Surprisingly, the two fields share a fundamental connection: the score. This talk explores that connection and examines score estimation from several perspectives. Along the way, we discuss recent theoretical developments and state-of-the-art methods for both extremes and AI.
Speaker Biography 讲者简介
Johannes is a data-science professor, podcaster, and entrepreneur. Johannes holds the Chair of Mathematics for Data-Driven Methods in the Department of Mathematics, Computer Science, and Natural Sciences at University of Hamburg. After completing a MSc in Physics at ETH Zürich and Paris VI in 2010, he obtained his Doctor of Sciences under the supervision of Sara van de Geer and Peter Bühlmann at ETH Zürich in 2013. He then joined UC Berkeley as a postdoctoral researcher hosted by Martin Wainwright, before serving as the Jacob Wolfowitz Visiting Assistant Professor in the Department of Statistical Science at Cornell University starting 2013. In 2015, Johannes became Assistant Professor of Statistics and Adjunct Assistant Professor of Biostatistics at the University of Washington. He was then appointed as Professor at Ruhr University Bochum in 2018 and, in 2023, joined the University of Hamburg in his current role. Johannes was also an Affiliate Faculty at the University of Washington and Visiting Professor at UC Louvain (2017), University of Hong Kong (2020), and University Gustave Eiffel (2023). Moreover, he is an Area Chair for NeurIPS 2026, an Associate Editor of the Electronic Journal of Statistics and Sankhya A, an Editorial Board Reviewer of the Journal of Machine Learning Research, and was an Associate Editor of the Canadian Journal of Statistics. Johannes is a founding member of Hamburg Virtual Initiative for Science & Technology in AI, a member of Kiez of Computing and Data Science, and the outreach delegate for mathematics at University of Hamburg. He is also a frequent voice on social and classical media and a keynote speaker on data science and AI.
Topics 涉及主题
Extreme‑value theory Modern AI / Machine Learning Score‑based connections between statistics and AI