Research Group Björn Eskofier
Björn Eskofier
Prof. Dr.
Core PI
Björn Eskofier
is Director of the Institute for Artificial Intelligence in Medicine at LMU University Hospital Munich.
His research focuses on the development and application of machine learning and data analytics methods in medicine, with a particular emphasis on sensor-based monitoring, motion analysis, and AI-assisted therapy decision-making. Before joining LMU in 2025, he was Chair of Machine Learning and Data Analytics and founding speaker of the Department of Artificial Intelligence in Biomedical Engineering (AIBE) at FAU Erlangen-Nürnberg. He has held visiting professorships at Harvard Medical School, the Massachusetts Institute of Technology, and Stanford University.
Team members @MCML
PostDocs
René Raab
Dr.
→ Group Björn Eskofier
Artificial Intelligence in Medicine
Robert Richer
Dr.
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Artificial Intelligence in Medicine
Emmanuelle Salin
Dr.
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Artificial Intelligence in Medicine
Alexandros Tanzanakis
Dr.
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Artificial Intelligence in Medicine
PhD Students
Luca Abel
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Artificial Intelligence in Medicine
Thomas Altstidl
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Artificial Intelligence in Medicine
Amirreza Asemanrafat
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Artificial Intelligence in Medicine
Arijana Bohr
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Artificial Intelligence in Medicine
Simon Dietz
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Artificial Intelligence in Medicine
Sophie Fischerauer
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Artificial Intelligence in Medicine
Madeleine Flaucher
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Artificial Intelligence in Medicine
Mahdis Habibpour
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Artificial Intelligence in Medicine
Sebastian Höfler
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Artificial Intelligence in Medicine
Naga Venkata Sai Jitin Jami
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Artificial Intelligence in Medicine
Daniel Krauss
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Artificial Intelligence in Medicine
Rebecca Lennartz
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Artificial Intelligence in Medicine
Jindong Li
→ Group Björn Eskofier
Artificial Intelligence in Medicine
Matthias Lutz
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Artificial Intelligence in Medicine
Thomas Maier
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Artificial Intelligence in Medicine
Tatiana Merzhevich
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Artificial Intelligence in Medicine
Jonas Leo Müller
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Artificial Intelligence in Medicine
Björn Nieth
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Artificial Intelligence in Medicine
Marie Oesten
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Artificial Intelligence in Medicine
Jan Petermann
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Artificial Intelligence in Medicine
Maximilian Renz
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Artificial Intelligence in Medicine
Sara Rosato
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Artificial Intelligence in Medicine
Misha Sadeghi
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Artificial Intelligence in Medicine
Alexander Weiss
→ Group Björn Eskofier
Artificial Intelligence in Medicine
Recent News @MCML
Publications @MCML
2026
Cross-Corpus Depression Detection in Semi-Structured Clinical Interviews.
EMNLP 2026 - Conference on Empirical Methods in Natural Language Processing. Budapest, Hungary, Oct 24-29, 2026. To be published.
How Human-Like Are Large Language Models? A Register-Aware Linguistic Evaluation Framework.
EMNLP 2026 - Conference on Empirical Methods in Natural Language Processing. Budapest, Hungary, Oct 24-29, 2026. To be published. Preprint available. arXiv GitHub
How LLMs Build Fictional Worlds: Setting and Narrative Space in AI-Generated Creative Storytelling.
Preprint (Sep. 2026). arXiv GitHub
Enhancing IMU-Based Online Handwriting Recognition via Contrastive Learning with Zero Inference Overhead.
ICDAR 2026 - 20th International Conference on Document Analysis and Recognition. Vienna, Austria, Aug 30-Sep 04, 2026. To be published. Preprint available. arXiv
Do Depressive Facial Patterns Transfer Across Cultures and Contexts? Evidence from a German RCT and E-DAIC.
AI4Mental @KDD 2026 - International Workshop on AI for Cognitive and Mental Health Support at the 32nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Jeju Island, Republic of Korea, Aug 09-13, 2026. PDF GitHub
RadProPoser: Probabilistic Radar Tensor Human Pose Estimation That Knows Its Limits.
IJCNN 2026 - International Joint Conference on Neural Networks. Maastricht, Netherlands, Jun 21-26, 2026. To be published. Preprint available. arXiv GitHub
Radar-based inspiratory-to-expiratory time ratio estimation: a validation study.
Scientific Reports 16.8256. Mar. 2026. DOI
Tokenization vs. Augmentation: A Systematic Study of Writer Variance in IMU-Based Online Handwriting Recognition.
Preprint (Mar. 2026). arXiv
ContraLog: Log File Anomaly Detection with Contrastive Learning and Masked Language Modeling.
Preprint (Feb. 2026). arXiv
2025
Beat-to-beat aortic valve opening detection from impedance cardiography using machine learning.
Preprint (Dec. 2025). DOI
REECAP: Contrastive learning of retinal aging reveals genetic loci linking morphology to eye disease.
Preprint (Nov. 2025). DOI
2023
Federated electronic health records for the European Health Data Space.
The Lancet Digital Healths 5.11. Nov. 2023. DOI
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2025-10-06 - Last modified: 2026-09-14