Institute for Data Science Foundations
Institute for Data Science Foundations
DE
DE
Conference Program
Monday, Sept 22, 2025
| 09:30 - 09:50 | Welcome address |
| 09:50 - 10:40 | Gabriele Steidl (TU Berlin, Germany)Telegrapher’s Generative Model via Kac Flows ▼We propose a new generative model based on the damped wave equation, also known as telegrapher’s equation. Similar to the diffusion equation and Brownian motion, there is a Feynman-Kac type relation between the telegrapher’s equation and the stochastic Kac process in 1D. The Kac flow evolves stepwise linearly in time, so that the probability flow is Lipschitz continuous in the Wasserstein distance and, in contrast to diffusion flows, the norm of the velocity is globally bounded. Furthermore, the Kac model has the diffusion model as its asymptotic limit. We extend these considerations to a multi-dimensional stochastic process which consists of independent 1D Kac processes in each spatial component. We show that this process gives rise to an absolutely continuous curve in the Wasserstein space and compute the conditional velocity field starting in a Dirac point analytically. Using the framework of flow matching, we train a neural network that approximates the velocity field and use it for sample generation. Our numerical experiments demonstrate the scalability of our approach, and show its advantages over diffusion models. This is joint work with Richard Duong, Jannis Chemseddine and Peter K. Friz. |
| 10:40 - 11:10 | Coffee Break |
| 11:10 - 11:35 | Christoph Lampert (Institute of Science and Technology, Austria)Generalization Guarantees for Multi-task and Meta-learning ▼tba |
| 11:35 - 12:00 | Simon Weissmann (University of Mannheim, Germany)Almost sure convergence rates for stochastic gradient methods ▼tba |
| 12:00 - 13:00 | Lunch |
| 13:00 - 13:50 | Lenaic Chizat (EPFL, Switzerland)Title: tba ▼tba |
| 13:50 - 14:15 | Viktor Stein (TU Berlin, Germany)Wasserstein Gradient Flows for Moreau Envelopes of f-Divergences in Reproducing Kernel Hilbert Spaces ▼tba |
| 14:15 - 14:40 | Kainth Rishi Sonthalia (Boston College, USA)Generalization with Non-Standard Spectra ▼tba |
| 14:40 - 15:10 | Coffee Break |
| 15:10 - 16:00 | Misha Belkin (University of California San Diego, USA)Title: tba ▼tba |
| 16:00 - 16:25 | Armin Iske (University of Hamburg, Germany)On the Convergence of Multiscale Kernel Regression under Minimalistic Assumptions ▼tba |
| 16:25 - 16:50 | Christoph Brune (University of Twente, Netherlands)Deep Networks are Reproducing Kernel Chains ▼tba |
| 16:50 - 17:20 | Coffee Break |
| 17:20 - 17:45 | Marcello Carioni (University of Twente, Netherlands)Atomic Gradient Descents ▼tba |
| 17:45 - 18:10 | Nisha Chandramoorthy (University of Chicago, USA)When, why and how are some generative models robust? ▼tba |
Tuesday, Sept 23, 2025
| 09:00 - 09:50 | Stefanie Jegelka (MIT, USA, and TU Munich, Germany)Title: tba ▼tba |
| 09:50 - 10:15 | Parvaneh Joharinad (Leipzig University and MPI for Mathematics in the Sciences, Germany)Title: tba ▼tba |
| 10:15 - 10:40 | Diaaeldin Taha (MPI for Mathematics in the Sciences, Germany)Title: tba ▼tba |
| 10:40 - 11:10 | Coffee Break |
| 11:10 - 11:35 | Amanjit Singh (University of Toronto, Canada)Bregman-Wasserstein gradient flows ▼tba |
| 11:35 - 12:00 | Adwait Datar (Hamburg University of Technology, Germany) Does the Natural Gradient Really Outperform the Euclidean Gradient? ▼tba |
| 12:00 - 13:00 | Lunch |
| 13:00 - 14:00 | Poster Session |
| 14:00 - 14:25 | Semih Cayci (RWTH Aachen University, Germany)Convergence of Gauss-Newton in the Lazy Training Regime: A Riemannian Optimization Perspective ▼tba |
| 14:25 - 14:50 | Johannes Müller (TU Berlin, Germany)Title: tba ▼tba |
| 14:50 - 15:15 | Alexander Friedrich (Umeå University, Sweden)A First Construction of Neural ODES on M-Polyfolds ▼tba |
| 15:15 - 15:40 | Thomas Martinetz (University of Lübeck, Germany)Good by Default? Generalization in Highly Overparameterized Networks ▼tba |
| 15:40 - 16:10 | Coffee Break |
| 16:10 - 17:00 | Francis Bach (INRIA Paris Centre, France)Denoising diffusion models without diffusions ▼Denoising diffusion models have enabled remarkable advances in generative modeling across various domains. These methods rely on a two-step process: first, sampling a noisy version of the data—an easier computational task—and then denoising it, either in a single step or through a sequential procedure. Both stages hinge on the same key component: the score function, which is closely tied to the optimal denoiser mapping noisy inputs back to clean data. In this talk, I will introduce an alternative perspective on denoising-based sampling that bypasses the need for continuous-time diffusion processes. This framework not only offers a fresh conceptual angle but also naturally extends to discrete settings, such as binary data. Joint work with Saeed Saremi and Ji-Won Park (https://arxiv.org/abs/2305.19473, https://arxiv.org/abs/2502.00557). |
| 19:00 - 22:00 | Dinner |
Wednesday, Sept 24, 2025
| 09:00 - 09:50 | Gitta Kutyniok (LMU Munich, Germany)Reliable and Sustainable AI: From Mathematical Foundations to Next Generation AI Computing ▼The current wave of artificial intelligence is transforming industry, society, and the sciences at an unprecedented pace. Yet, despite its remarkable progress, today’s AI still suffers from two major limitations: a lack of reliability and excessive energy consumption. This lecture will begin with an overview of this dynamic field, focusing first on reliability. We will present recent theoretical advances in the areas of generalization and explainability -- core aspects of trustworthy AI that also intersect with regulatory frameworks such as the EU AI Act. From there, we will explore fundamental limitations of existing AI systems, including challenges related to computability and the energy inefficiency of current digital hardware. These challenges highlight the pressing need to rethink the foundations of AI computing. In the second part of the talk, we will turn to neuromorphic computing -- a promising and rapidly evolving paradigm that emulates biological neural systems using analog hardware. We will introduce spiking neural networks, a key model in this area, and share some of our recent mathematical findings. These results point toward a new generation of AI systems that are not only provably reliable but also sustainable. |
| 09:50 - 10:15 | Marco Mondelli (Institute of Science and Technology, Austria)Title: tba ▼tba |
| 10:15 - 10:40 | Yury Korolev (University of Bath, United Kingdom)Large-time dynamics in transformer architectures with layer normalisation ▼tba |
| 10:40 - 11:10 | Coffee Break |
| 11:10 - 11:35 | Leon Bungert (University of Würzburg, Germany)Robustness on the interface of geometry and probability ▼tba |
| 11:35 - 12:00 | Martin Lazar (University of Dubrovnik, Croatia)Be greedy and learn: efficient and certified algorithms for parametrized optimal control problems ▼tba |
| 12:00 - 13:00 | Lunch |
| 13:00 - 14:00 | Poster Session |
| 14:00 - 14:50 | Frank Nielsen (Sony Computer Science Laboratories Inc., Japan) Title: tba ▼tba |
| 14:50 - 15:15 | Vahid Shahverdi (KTH, Sweden)Title: tba ▼tba |
| 15:15 - 15:40 | Jesse van Oostrum (Hamburg University of Technology, Germany) On the Natural Gradient of the Evidence Lower Bound ▼tba |
| 15:40 - 16:10 | Coffee Break |
| 16:10 - 17:00 | Jürgen Jost (MPI for Mathematics in the Sciences, Germany) Geometric and statistical methods of data analysis. In memoriam Sayan Mukherjee ▼tba |
| 17:00 - 17:25 | Michael Murray (University of Bath, United Kingdom)Title: tba ▼tba |
| 17:25 - 17:50 | Sebastian Kassing (TU Berlin, Germany)On the effect of acceleration and regularization in machine learning ▼tba |
Thursday, Sept 25, 2025
| 09:00 - 09:50 | Markos Katsoulakis (University of Massachusetts Amherst, USA)Title: tba ▼tba |
| 09:50 - 10:15 | Pavel Gurikov (Hamburg University of Technology, Germany)Physics-Informed Machine Learning for Sustainable Process Design: Predicting Solubility in Green Solvents ▼tba |
| 10:15 - 10:40 | Sebastian Götschel (Hamburg University of Technology, Germany)Hard-constraining Boundary Conditions for Physics-Informed Neural Operators ▼tba |
| 10:40 - 11:10 | Coffee Break |
| 11:10 - 11:35 | Jan Gerken (Chalmers University of Technology, Sweden)Emergent Equivariance in Deep Ensembles ▼tba |
| 11:35 - 12:00 | Timm Faulwasser (Hamburg University of Technology, Germany)The Optimal Control Perspective on Deep Neural Networks – Early Exits, Insights, and Open Problems ▼tba |
| 12:00 - 13:00 | Lunch |
| 13:00 - 13:50 | Matus Telgarsky (New York University, USA)Title: tba ▼tba |
| 13:50 - 14:15 | Ahmed Abdeljawad (Radon Institute for Computational and Applied Mathematics, Austria)Approximation Theory of Shallow Neural Networks ▼tba |
| 14:15 - 14:40 | Jethro Warnett (University of Oxford, United Kingdom)Stein Variational Gradient Descent ▼tba |
| 14:40 - 15:10 | Coffee Break |
| 15:10 - 16:00 | Kenji Fukumizu (Institute of Statistical Mathematics, Japan) Title: tba ▼tba |
| 16:00 - 16:25 | Vitalii Konarovskyi (University of Hamburg, Germany)Stochastic Modified Flows, Mean-Field Limits and Dynamics of Stochastic Gradient Descent ▼tba |
| 16:25 - 16:50 | Tim Jahn (TU Berlin, Germany)Learning Jump–Diffusion Dynamics from Irregularly-Sampled Data via Trajectory Generator Matching ▼tba |
| 16:50 - 17:20 | Coffee Break |
| 17:20 - 17:45 | Gianluca Finocchio (University of Vienna, Austria)Model-Free Identification in Ill-Posed Regression ▼tba |
| 17:45 - 18:10 | Marzieh Eidi (MPI for Mathematics in the Sciences/ScaDS AI Institute, Germany)Geometric learning in complex networks ▼tba |
