4TU+.AMI Community Event 2026

Jeanine Duistermaat is a Professor of Applied Statistics at Eindhoven University of Technology. She has worked on statistical genetics, statistical bioinformatics, and modeling data along a continuum, such as weather data and images. Since 2024, she has been a member of the Royal Netherlands Academy of Arts and Sciences (KNAW).

Title and abstract: TBA

Title: Mathematics running through our veins

Abstract: This presentation focuses on methods and algorithms for personalizing mathematical models of the cardiovascular system using clinical data. These models are based on fluid and solid mechanics and span multiple scales. Therefore, not only is their robust numerical solution challenging, but they also need to be integrated with data in cost-effective data assimilation frameworks.

Title: Bias-Optimal Bounds for SGD: A Computer-Aided Lyapunov Analysis

Abstract: Stochastic Gradient Descent (SGD) has become a cornerstone algorithm in optimization, popular for training a wide range of machine learning models. The typical non-asymptotic analysis of SGD requires assumptions to be made on the problem, which are not always easy or possible to verify in practice. In this work, we provide an analysis that does not make such unrealistic assumptions. We extend previous studies to incorporate a larger range of step-sizes, and obtain bounds which are, in some sense, optimal. The proof design is inspired by the Performance Estimation Problem framework, which both guides our proof and numerically demonstrates the optimality of our bounds.

The talk is based on the following paper: https://arxiv.org/abs/2505.17965

Pitches from  Sara Giarrusso (Eindhoven), Gregor Gantner (Twente) and Maike Meier (Groningen).

Silke Glas is an Assistant Professor in the Department of Applied Mathematics at University of Twente, where her research focus is on model reduction, especially structure-preserving model reduction and model reduction on manifolds.

Title and abstract: TBA

Title: Convergence of energy-based learning in resistive circuits 

Abstract: Energy-based learning is a biologically plausible alternative to the popular back-propagation method for training artificial neural networks. It deals with models that are governed by a parameterized energy function. Learning is achieved by shaping the energy function such that its minima coincide with given data. The energy-based perspective is a promising avenue for training analog circuits in an energy-efficient manner, especially because training can be performed using relatively simple, local parameter updates. The purpose of this talk is to make some steps towards a theoretical understanding of energy-based learning, applied to nonlinear resistive networks. For these networks, we propose an energy-based learning algorithm and we establish conditions under which this algorithm converges to a suitable vector of parameters, explaining the data. 

There will be a dinner at the end of the evening. Participants of the dinner will be responsible for their own expenses.