Speakers | Foundation Models for Structured Data

Keynote Speakers

David Holzmüller

Research Scientist, INRIA

“TabICLv2: Advancing Open Tabular Foundation Models”

David Holzmüller is a Paris-based researcher at INRIA, working on machine learning for tabular data and uncertainty quantification in collaboration with the groups of Gaël Varoquaux and Francis Bach. His research spans tabular foundation models (TabICL), deep learning models (RealMLP), benchmarks (TabArena), and more. Previously, he obtained his PhD from the University of Stuttgart under the supervision of Ingo Steinwart, exploring topics such as active learning, neural network theory, neural networks for atomistic simulations, and sampling.

Abhimanyu Das

Principal Research Scientist, Google

“Multimodal Time-Series Foundation Models”

Abhimanyu Das is a Principal Research Scientist at Google, where he leads teams on Time Series and Tabular modeling. His current research interests include foundation models for predicting and reasoning over structured data. He is one of the authors of the TimesFM family of models. He obtained his PhD in Computer Science from the University of Southern California , and a B. Tech in Computer Science from IIT Delhi. His research has received a Distinguished Paper Award at ICML 2011, a Best Paper Award at WSDM 2014, and a Best Paper Honorable Mention at WWW 2018.

Katharina Eggensperger

Associate Professor, TU Dortmund University

“Scaling and Understanding Models for (Scientific) Tabular Data”

Katharina Eggensperger is an associate professor for ML and AI at the Lamarr Institute and TU Dortmund University. Before that, she led an early-career research group in the Cluster of Excellence Machine Learning for Science at the University of Tübingen. She received her PhD from the University of Freiburg, under the supervision of Frank Hutter and Marius Lindauer. Her research focuses on automated machine learning for tabular data, with the goal of advancing both AutoML methods and foundation models to better support applications in science. She has co-developed widely used open-source tools for AutoML and hyperparameter optimization. Her work emphasizes rigorous empirical evaluation and benchmarking to advance robust, reproducible machine learning research.