About
I am a
machine learning researcher at
Merantix Momentum.
Currently, my work is focused on combining ideas from high-dimensional
probability, nonparametric statistics and stochastic processes
for inference in inverse problems and time series data.
Over the last years, I have been particularly interested in models with
downstream applications in
biomedical research, causality and statistical hypothesis testing as well as
dimensionality reduction.
Academic research
In 2022, I obtained my PhD under the supervision of
Christof Schütte
in the
Biocomputing group at the Department of
Mathematics and Computer Science
of Freie Universität Berlin.
My
dissertation
investigates theoretical aspects of
nonparametric models for Markov processes
—with relevant applications in
fields like molecular dynamics (such as computational drug design)
and fluid mechanics (such as complex combustion processes).
My recent work together with
Claudia Schillings
addresses problems in
uncertainty quantification
and the data-driven numerical analysis of stochastic differential equations.
Industry experience
During my PhD program, I spent some time doing R
&D
in the aircraft engine development division of
Rolls-Royce Deutschland,
where I worked on security critical machine learning software
for system identification and control as well as pattern recognition
in sensor data. In this context, my main goal was to contribute
to robust models aimed at the detection of rare events
(such as specific failures of structural components),
possibly including synthetic data obtained from simulations
and rare event sampling.
After that, I stayed with the people at
dida for the past
five years
—mainly working
on design and devops of large-scale deep learning systems for
natural language processing and computer vision.
My projects tackled a variety of problems arising in
weather forecasting, semantic search, remote sensing and image segmentation.
In particular, we made use of ideas from
domain adaptation, few-shot learning and various
pretraining and fine-tuning techniques, aiming to transform recent
deep learning research into production software.
Publications and preprints
Reports and preprints
Zhu Li, Dimitri Meunier, Mattes Mollenhauer and Arthur Gretton.
"Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized
Least-Squares Algorithm".
Preprint, 2023.
link
Mattes Mollenhauer and Claudia Schillings.
"On the concentration of subgaussian vectors and positive quadratic
forms in Hilbert spaces".
Preprint, 2023.
link
Mattes Mollenhauer, Nicole Mücke and Tim Sullivan.
"Learning linear operators: Infinite-dimensional regression
as a well-behaved non-compact inverse problem".
Preprint, 2022.
link
Mattes Mollenhauer and Péter Koltai.
"Nonparametric approximation of conditional expectation operators."
Preprint, 2020.
link
Published
Andreas Bittracher, Mattes Mollenhauer, Péter Koltai and Christof Schütte.
“Optimal Reaction Coordinates: Variational Characterization and Sparse Computation”.
SIAM Multiscale Modeling
& Simulation, 21:449-488, 2023.
link
Zhu Li, Dimitri Meunier, Mattes Mollenhauer and Arthur Gretton.
“Optimal Rates for Regularized Conditional Mean Embedding Learning”.
Advances in Neural Information Processing Systems (NeurIPS). Vol. 36, 2022.
link
Mattes Mollenhauer, Stefan Klus, Christof Schütte and Péter Koltai.
“Kernel autocovariance operators of stationary processes: Estimation and convergence”.
Journal of Machine Learning Research, 23(327):1−34, 2022.
link
Mattes Mollenhauer, Ingmar Schuster, Stefan Klus and Christof Schütte.
“Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces”.
Advances in Dynamics, Optimization and Computation, 109-130, Springer, 2020.
link
Ingmar Schuster, Mattes Mollenhauer, Stefan Klus and Krikamol Muandet.
“Kernel Conditional Density Operators”.
23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
link
Stefan Klus, Brooke E. Husic, Mattes Mollenhauer and Frank Noé.
“Kernel methods for detecting coherent structures in dynamical data”.
Chaos: An Interdisciplinary Journal of Nonlinear Science 29.12, 2019.
link
Miscellaneous
"On the Statistical Approximation of Conditional Expectation Operators".
PhD thesis, Freie Universität Berlin. 2022.
link