Explore Team Members by Research Interests

What is this page about?
This page allows filtering over the union set over all research interests specified in our group member's profiles. Members matching any of the selected interests will be displayed. Choosing a general interest like “Quantum Chemistry” will encompass all sub-categories within that field, e.g., “Computational Quantum Chemistry”.

What is this page's intended audience?
This page can be helpful for students seeking a Bachelor's or Master's thesis advisor aligned with their academic focus. For additional details on thesis opportunities, please visit our thesis request landing page. Furthermore, this page may be used by potential job applicants to identify group members of common interest.

Our primary Team page offers a different view of our team.


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Automatic Differentiation
Explainable AI
Inverse Problems
ML for Physical Sciences
Molecular Dynamics
Neural ODEs

Adrian Hill

Doctoral Researcher

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Anomaly Detection
Computer Vision
Deep Learning
Explainable AI
Natural Language Processing
Structured Prediction

Dr. Alexander Bauer

Postdoctoral Researcher

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Bayesian Learning
Biomedical Sensing
Computational Neuroscience
Diffusion Models
Explainable AI
Inverse Problems
Neuroimaging
Probabilistic ML

Dr. Ali Hashemi

Postdoctoral Researcher

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Biomedical Sensing
Biomedical Signal Processing
Computational Neuroscience
Independent Component Analysis
Kernel Methods
Speech and Audio Processing

Dr. Andreas Ziehe

Postdoctoral Researcher

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Equations Learner (EQL)
Generative Models
Lattice Field Theory
ML for Physical Sciences
Renormalization Group Theory

Dr. Ankur Singha

Postdoctoral Researcher

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Adversarial Machine Learning
Covariate Shift
Deep Learning
Explainable AI
Feature Attribution
Gaussian Process Regression
ML for Physical Sciences
Probabilistic ML
Quantum Computing
Representation Learning
Robust Machine Learning
Software for Machine Learning
Spurious Correlations

Dr. Christopher J. Anders

Postdoctoral Researcher

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Distributed Deep Learning
Explainable AI
Probabilistic ML

Dennis Grinwald

Doctoral Researcher

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Biomedical Sensing
Computational Neuroscience
Functional Near-infrared Spectroscopy (fNIRS) Measurement and Analysis Techniques
Inverse Problems, Particularly FNIRS Image Reconstruction
Machine Learning Methods for Multimodal Sensor Data To Improve The Utilization Of Physiological Information During Data Analysis
Multimodal Learning

Dr. Eike Middell

Postdoctoral Researcher

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Generative Models
ML for Physical Sciences
Quantum Chemistry (DFT, TDDFT, QM-MM, Fragment-based Quantum Chemistry)

Elron Pens

Doctoral Researcher

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Compressive Sensing and Inverse Problems
Diffusion Models
Foundational Models and Large-Scale Machine Learning
Generative Modelling
Information Theory
Multimodal Learning
Probabilistic ML
Source Separation

Fadli Damara

Doctoral Researcher

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Efficient Machine Learning
Explainable AI
Generative Models
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Explainable AI
Higher-order Explanations
Kernel Methods
Methods for Explainable AI
Uncertainty Estimation

Florian Bley

Doctoral Researcher

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Anomaly Detection
Hierarchical Learning
Model Distillation
Multimodal Learning
Probabilistic ML
Representation Learning
Robust Machine Learning
Self-supervised Learning
Unsupervised Learning

Florian C.F. Schulz

Doctoral Researcher

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Expectation Estimation Incl. Kernel Mean Embeddings
High-Dimensional Statistics
Kernel Methods
Probabilistic ML

Hannah Marienwald

Doctoral Researcher

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Anomaly Detection
Clustering
Explainable AI
Kernel Methods
Neural Networks
Unsupervised Learning

Dr. Jacob Kauffmann

Postdoctoral Researcher

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Computer Vision
Diffusion Models
Generative Models
Inverse Problems
Large Language Models
Multimodal Learning
Normalizing Flows
Probabilistic ML
Score-based Models
Software for Machine Learning
Transformers

Jannik Wolff

Doctoral Researcher

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Applications Of Machine Learning in Neuroscience
Biomedical Sensing
Computational Neuroscience
Electrophysiology
Modeling, Especially Behavioral Modeling
Reinforcement Learning

Dr. Johannes Niediek

Postdoctoral Researcher

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Computational Neuroscience
Explainable AI
Graph Neural Networks
Language Education
Probabilistic ML

Johannes Maeß

Doctoral Researcher

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Anomaly Detection
Computational Pathology
Digital Pathology
Explainable AI