Johannes Müller

Portrait of Johannes Müller

Dr. · Postdoctoral Researcher
TU Berlin

Research profile

Johannes Müller works on the mathematical foundations of learning. His research develops geometric and optimization viewpoints on reinforcement learning, scientific machine learning, neural PDE solvers and sampling.

Research areas

Numerics · Machine Learning · Sampling

Five research keywords

information geometry · reinforcement learning · scientific machine learning · neural PDE solvers · measure transport

Contact and links

Public email

johannes.christoph.mueller@tu-berlin.de

Institution

TU Berlin

Academic links

Personal academic website · ORCID 0000-0001-8729-0466 · arXiv publications

Curriculum vitae

DateAppointment / education
2025–presentPostdoctoral researcher with Benjamin Gess, Institute of Mathematics, Technische Universität Berlin.
2023–2024Scientific employee, Junior Professorship for Mathematics of Machine Learning, RWTH Aachen University.
2020–2023PhD researcher, International Max Planck Research School Mathematics in the Sciences, Leipzig; supervisors: Guido Montúfar and Nihat Ay.
2019MSc in Mathematics, University of Freiburg.
2018MSc in Interdisciplinary Mathematics, University of Warwick.
2016BSc in Mathematics, University of Freiburg.

Publications

This catalogue contains all publications by the member, including work from before joining SAiS. Journal versions and preprints are maintained as one record per scientific work.

  • Error Estimates for the Deep Ritz Method with Boundary Penalty

    Published · Mathematical and Scientific Machine Learning (MSML 2022). We estimate the error of the Deep Ritz Method for linear elliptic equations. For Dirichlet boundary conditions, we estimate the error when the boundary values are imposed through the boundary penalty method…

  • Deep Ritz revisited

    Published · Workshop on Integration of Deep Neural Models and Differential Equations at ICLR 2020. Recently, progress has been made in the application of neural networks to the numerical analysis of partial differential equations (PDEs). In the latter the variational formulation of the Poisson problem is used…

  • On the space-time expressivity of ResNets

    Published · Workshop on Integration of Deep Neural Models and Differential Equations at ICLR 2020. Residual networks (ResNets) are a deep learning architecture that substantially improved the state of the art performance in certain supervised learning tasks. Since then, they have received continuously growing attention. ResNets have…

Current SAiS projects

FluCo
Postdoctoral researcher in the ERC-funded team.

Talks and posters

Talks

DateTalk or event
Oct 2026The Advective Fisher–Rao Geometry of Deterministic Measure Transport, Analysis–Probability seminar, MPI MiS.
Aug 2026The Advective Fisher–Rao Geometry on Deterministic Flows of Measures, Institute for Data Science Foundations, TU Hamburg.
Jun 2026Implicit Bias in Neural Network Optimization, SIAM Conference on Optimization, Edinburgh.
Apr 2026Workshop on Structured Learning: Constraints and Geometry in Reinforcement Learning and Scientific Machine Learning, University of Freiburg.
Jun 2024Geometry and Convergence of Natural Policy Gradient Methods, Learning Theory and Statistical Optimization Seminar, University of Oxford.
Jun 2024Geometry of Optimization in Scientific Machine Learning and Reinforcement Learning, Geometric Deep Learning workshop, University of Cambridge.
Jan 2024Natural Gradients for Scientific Machine Learning, Postgraduate Seminar, RWTH Aachen University.
Apr 2023Theoretical Analysis of Boundary Penalties for Neural-Network-Based PDE Solvers, Machine Learning + X Seminar, Brown University, online.
Feb 2023Geometry of Sequential Decision Problems, Optimization and Data Science Seminar, University of California San Diego, online.
Nov 2022Geometry of Markov Decision Processes, annual meeting of Priority Programme 2298, Tutzing.
Oct 2022Geometry of Natural Policy Gradient Methods, Applied Mathematics Colloquium, UCLA.
Sep 2022Minisymposium on Algebraic Geometry and Machine Learning, SIAM Mathematics of Data Science Conference, San Diego.
Aug 2022Workshop on Algebraic Geometry, Combinatorics, and Machine Learning, MPI MiS.
May 2022Algebraic Statistics 2022, University of Hawaiʻi at Mānoa.
Apr 2020Mathematics of Machine Learning seminar, MPI MiS and UCLA.

Poster presentations

DatePoster or event
Feb 2024Geometry and Convergence of Natural Policy Gradient Methods, Symposium on Sparsity and Singular Structures, RWTH Aachen University.
Jan 2024Fisher–Rao Gradient Flows of Linear Programs and State-Action Natural Policy Gradients, Symposium on Sparsity and Singular Structures, RWTH Aachen University.
Jan 2024Geometry and Convergence of Natural Policy Gradient Methods, Mini-Workshop on Reinforcement Learning, University of Mannheim.
Jun 2022Solving Infinite-Horizon POMDPs with Memoryless Stochastic Policies in State-Action Space, RLDM, Brown University.
Apr 2022Geometry of Memoryless Stochastic Policy Optimization in Infinite-Horizon POMDPs, ICLR, online.
Nov 2021A Posteriori Estimates and Convergence Guarantees for Neural-Network-Based PDE Solvers, Isaac Newton Institute, Cambridge.
Oct 2021Geometry of Memoryless Stochastic Policy Optimization in Infinite-Horizon POMDPs, BIRS workshop, online.
Aug 2021Geometry of Discounted Stationary Distributions of Markov Decision Processes, Isaac Newton Institute, Cambridge.
Aug 2021Geometry of Discounted Stationary Distributions of Markov Decision Processes, Mathematics of Machine Learning Conference, Bielefeld.
Apr 2020Deep Ritz Revisited; Space–Time Expressivity of Residual Networks, ICLR DeepDiffEq workshop, online.

Teaching

TermCourse or supervision
Summer 2025Teaching assistant, Mathematics for Physicists IV, TU Berlin.
Summer 2024Seminar: Approximation Properties of Neural Networks, RWTH Aachen University.
Winter 2024Teaching assistant, Mathematical Foundations of Deep Learning, RWTH Aachen University.
2024Master’s thesis supervision: Reza Zolnouri, “The Role of Geometry in Policy Mirror Descent.”
2024Master’s thesis supervision: Jonas Nießen, “Optimization Guarantees for Physics-Informed Neural Networks.”
2022Research-intern supervision: Friedrich Wicke, “State-Action Geometry of Multi-Agent Problems.”