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Research Group Fabian Theis

Link to Fabian Theis

Fabian Theis

Prof. Dr.

Principal Investigator

Mathematical Modelling of Biological Systems

Fabian Theis

holds the Chair of Mathematical Modeling of Biological Systems and TU Munich and is director of the Institute of Computational Biology at the Helmholtz Zentrum München.

He conducts research in the field of computational biology. The main focus of his work is the application of machine learning methods to biological questions, in particular as a means of modeling cell heterogeneities on the basis of single cell analyses and also of integrating 'omics' data into systems medicine approaches.

Team members @MCML

Link to Leon Hetzel

Leon Hetzel

Mathematical Modelling of Biological Systems

Link to Till Richter

Till Richter

Mathematical Modelling of Biological Systems

Link to Anna Schaar

Anna Schaar

Mathematical Modelling of Biological Systems

Link to Philipp Weiler

Philipp Weiler

Mathematical Modelling of Biological Systems

Publications @MCML

[10]
T. Uscidda, L. Eyring, K. Roth, F. J. Theis, Z. Akata and M. Cuturi.
Disentangled Representation Learning with the Gromov-Monge Gap.
Preprint at arXiv (Oct. 2024). arXiv.
Abstract

Learning disentangled representations from unlabelled data is a fundamental challenge in machine learning. Solving it may unlock other problems, such as generalization, interpretability, or fairness. Although remarkably challenging to solve in theory, disentanglement is often achieved in practice through prior matching. Furthermore, recent works have shown that prior matching approaches can be enhanced by leveraging geometrical considerations, e.g., by learning representations that preserve geometric features of the data, such as distances or angles between points. However, matching the prior while preserving geometric features is challenging, as a mapping that fully preserves these features while aligning the data distribution with the prior does not exist in general. To address these challenges, we introduce a novel approach to disentangled representation learning based on quadratic optimal transport. We formulate the problem using Gromov-Monge maps that transport one distribution onto another with minimal distortion of predefined geometric features, preserving them as much as can be achieved. To compute such maps, we propose the Gromov-Monge-Gap (GMG), a regularizer quantifying whether a map moves a reference distribution with minimal geometry distortion. We demonstrate the effectiveness of our approach for disentanglement across four standard benchmarks, outperforming other methods leveraging geometric considerations.

MCML Authors
Link to Luca Eyring

Luca Eyring

Interpretable and Reliable Machine Learning

Link to Karsten Roth

Karsten Roth

Interpretable and Reliable Machine Learning

Link to Fabian Theis

Fabian Theis

Prof. Dr.

Mathematical Modelling of Biological Systems

Link to Zeynep Akata

Zeynep Akata

Prof. Dr.

Interpretable and Reliable Machine Learning


[9]
L. Eyring, D. Klein, T. Palla, N. Kilbertus, Z. Akata and F. J. Theis.
Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation.
12th International Conference on Learning Representations (ICLR 2024). Vienna, Austria, May 07-11, 2024. URL.
Abstract

In optimal transport (OT), a Monge map is known as a mapping that transports a source distribution to a target distribution in the most cost-efficient way. Recently, multiple neural estimators for Monge maps have been developed and applied in diverse unpaired domain translation tasks, e.g. in single-cell biology and computer vision. However, the classic OT framework enforces mass conservation, which makes it prone to outliers and limits its applicability in real-world scenarios. The latter can be particularly harmful in OT domain translation tasks, where the relative position of a sample within a distribution is explicitly taken into account. While unbalanced OT tackles this challenge in the discrete setting, its integration into neural Monge map estimators has received limited attention. We propose a theoretically grounded method to incorporate unbalancedness into any Monge map estimator. We improve existing estimators to model cell trajectories over time and to predict cellular responses to perturbations. Moreover, our approach seamlessly integrates with the OT flow matching (OT-FM) framework. While we show that OT-FM performs competitively in image translation, we further improve performance by incorporating unbalancedness (UOT-FM), which better preserves relevant features. We hence establish UOT-FM as a principled method for unpaired image translation.

MCML Authors
Link to Luca Eyring

Luca Eyring

Interpretable and Reliable Machine Learning

Link to Niki Kilbertus

Niki Kilbertus

Prof. Dr.

Ethics in Systems Design and Machine Learning

Link to Zeynep Akata

Zeynep Akata

Prof. Dr.

Interpretable and Reliable Machine Learning

Link to Fabian Theis

Fabian Theis

Prof. Dr.

Mathematical Modelling of Biological Systems


[8]
D. S. Fischer, A. C. Schaar and F. J. Theis.
Modeling intercellular communication in tissues using spatial graphs of cell.
Nature Biotechnology 41 (Mar. 2023). DOI.
MCML Authors
Link to Fabian Theis

Fabian Theis

Prof. Dr.

Mathematical Modelling of Biological Systems


[7]
L. Heumos, A. C. Schaar, C. Lance, A. Litinetskaya, F. Drost, L. Zappia, M. D. Lücken, D. C. Strobl, J. Henao, F. Curion, S.-c. Best Practices Consortium, H. B. Schiller and F. J. Theis.
Best practices for single-cell analysis across modalities.
Nature Reviews Genetics 24 (Mar. 2023). DOI.
MCML Authors
Link to Fabian Theis

Fabian Theis

Prof. Dr.

Mathematical Modelling of Biological Systems


[6]
L. Hetzel, S. Boehm, N. Kilbertus, S. Günnemann, M. Lotfollahi and F. J. Theis.
Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution.
36th Conference on Neural Information Processing Systems (NeurIPS 2022). New Orleans, LA, USA, Nov 28-Dec 09, 2022. PDF.
MCML Authors
Link to Leon Hetzel

Leon Hetzel

Mathematical Modelling of Biological Systems

Link to Niki Kilbertus

Niki Kilbertus

Prof. Dr.

Ethics in Systems Design and Machine Learning

Link to Stephan Günnemann

Stephan Günnemann

Prof. Dr.

Data Analytics & Machine Learning

Link to Fabian Theis

Fabian Theis

Prof. Dr.

Mathematical Modelling of Biological Systems


[5]
M. Lotfollahi, M. Naghipourfar, M. D. Luecken, M. Khajavi, M. Büttner, M. Wagenstetter, Ž. Avsec, A. Gayoso, N. Yosef, M. Interlandi, S. Rybakov, A. V. Misharin and F. J. Theis.
Mapping single-cell data to reference atlases by transfer learning.
Nature Biotechnology 40 (Aug. 2022). DOI.
MCML Authors
Link to Fabian Theis

Fabian Theis

Prof. Dr.

Mathematical Modelling of Biological Systems


[4]
M. Lange, V. Bergen, M. Klein, M. Setty, B. Reuter, M. Bakhti, H. Lickert, M. Ansari, J. Schniering, H. B. Schiller, D. Pe’er and F. J. Theis.
CellRank for directed single-cell fate mapping.
Nature Methods 19.2 (Jan. 2022). DOI.
MCML Authors
Link to Marius Lange

Marius Lange

Dr.

* Former member

Link to Fabian Theis

Fabian Theis

Prof. Dr.

Mathematical Modelling of Biological Systems


[3]
D. S. Fischer, A. C. Schaar and F. J. Theis.
Learning cell communication from spatial graphs of cells.
Preprint at bioRxiv (Jun. 2021). DOI.
MCML Authors
Link to Fabian Theis

Fabian Theis

Prof. Dr.

Mathematical Modelling of Biological Systems


[2]
M. Lotfollahi, A. K. Susmelj, C. De Donno, Y. Ji, I. L. Ibarra, F. A. Wolf, N. Yakubova, F. J. Theis and D. Lopez-Paz.
Compositional perturbation autoencoder for single-cell response modeling.
Preprint at bioRxiv (May. 2021). DOI.
MCML Authors
Link to Fabian Theis

Fabian Theis

Prof. Dr.

Mathematical Modelling of Biological Systems


[1]
M. Lotfollahi, M. Naghipourfar, M. D. Luecken, M. Khajavi, M. Büttner, Ž. Avsec, A. V. Misharin and F. J. Theis.
Query to reference single-cell integration with transfer learning.
Preprint at bioRxiv (Jul. 2020). DOI.
MCML Authors
Link to Fabian Theis

Fabian Theis

Prof. Dr.

Mathematical Modelling of Biological Systems