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Machine Learning Consulting Unit at the MCML

The Machine Learning Consulting Unit (MLCU) is part of the of the MCML and offers applied researchers scientific consulting regarding the application and evaluation of machine learning methods.

Empowering Research Through Expert Consulting

Our primary goal is to provide consulting to applied sciences, for example medicine, psychology, biology and others. We aim to provide solutions, that based on our experience and expertise are most suitable to answer the research question at hand.

Consulting is free of charge (ca. 8h per project) for members of the MCML and the LMU. Consulting outside the MCML and LMU is also possible, but needs to be negotiated on a case by case basis. We also welcome joint research projects with the goal of publication and other forms of cooperation.

If you are interested in consulting, please contact us. Our experience shows, that it is advisable to register for consulting as early in the project as possible or even at the planning stage.

Team

Link to Andreas Bender

Andreas Bender

Dr.

Coordinator Statistical and Machine Learning Consulting

Machine Learning Consulting Unit (MLCU)

Link to Bernd Bischl

Bernd Bischl

Prof. Dr.

Statistical Learning & Data Science

Link to Ludwig Bothmann

Ludwig Bothmann

Dr.

Statistical Learning & Data Science

Link to David Rügamer

David Rügamer

Prof. Dr.

Data Science Group

Contact

If you are interested in consulting, please register using our webform.

For other request contact mlcu[at]stat.uni-muenchen.de

For statistical consulting also consider contacting the Statistical Consulting Unit (StaBLab).

Recent and Current Projects

Find a selection of projects that resulted from consulting requests in the past

  • Personality prediction from eye-tracking data

  • Landmark recognition from satellite imaging

  • Survival prediction based on radiomics and image data

  • Classifying neck pain status using scalar and functional biomechanical variables using functional data boosting

  • Interpretable machine learning models for classifying low back pain status using functional physiological variables

  • Wildlife image classification

  • Clinical predictive modeling of post-surgical recovery in individuals with cervical radiculopathy

  • Automated classification of atmospheric circulation patterns using Deep Learning

  • Classification of rain types

  • Clustering of German tourist types

  • Prediction of sports injuries in football

Publications of the MLCU

2024

[28]
A. Mittermeier, M. Aßenmacher, B. Schachtner, S. Grosu, V. Dakovic, V. Kandratovich, B. Sabel and M. Ingrisch.
Automatische ICD-10-Codierung.
Die Radiologie 64 (Aug. 2024). DOI.
[27]
A. Solderer, S. P. Hicklin, M. Aßenmacher, A. Ender and P. R. Schmidlin.
Influence of an allogenic collagen scaffold on implant sites with thin supracrestal tissue height: a randomized clinical trial.
Clinical Oral Investigations 28.313 (May. 2024). DOI.
[26]
F. Coens, N. Knops, I. Tieken, S. Vogelaar, A. Bender, J. J. Kim, K. Krupka, L. Pape, A. Raes, B. Tönshoff, A. Prytula and C. Registry.
Time-Varying Determinants of Graft Failure in Pediatric Kidney Transplantation in Europe.
Clinical Journal of the American Society of Nephrology 19.3 (Mar. 2024). DOI.
[25]
W. H. Hartl, P. Kopper, L. Xu, L. Heller, M. Mironov, R. Wang, A. G. Day, G. Elke, H. Küchenhoff and A. Bender.
Relevance of Protein Intake for Weaning in the Mechanically Ventilated Critically Ill: Analysis of a Large International Database.
Critical Care Medicine 50.3 (Mar. 2024). DOI.
[24]
B. X. W. Liew, F. Pfisterer, D. Rügamer and X. Zhai.
Strategies to optimise machine learning classification performance when using biomechanical features.
Journal of Biomechanics 165 (Mar. 2024). DOI.
[23]
B. X. W. Liew, D. Rügamer and A. V. Birn-Jeffery.
Neuromechanical stabilisation of the centre of mass during running.
Gait and Posture 108 (Feb. 2024). DOI.
[22]
T. Weber, M. Ingrisch, B. Bischl and D. Rügamer.
Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction.
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024). Waikoloa, Hawaii, Jan 04-08, 2024. DOI.
[21]
J. Gertheiss, D. Rügamer, B. Liew and S. Greven.
Functional Data Analysis: An Introduction and Recent Developments.
Biometrical Journal (2024). To be published. Preprint at arXiv. arXiv. GitHub.

2023

[20]
L. Bothmann, L. Wimmer, O. Charrakh, T. Weber, H. Edelhoff, W. Peters, H. Nguyen, C. Benjamin and A. Menzel.
Automated wildlife image classification: An active learning tool for ecological applications.
Ecological Informatics 77 (Nov. 2023). DOI.
[19]
T. Weber, M. Ingrisch, B. Bischl and D. Rügamer.
Unreading Race: Purging Protected Features from Chest X-ray Embeddings.
Under review. Preprint at arXiv (Nov. 2023). arXiv.
[18]
B. X. W. Liew, F. M. Kovacs, D. Rügamer and A. Royuela.
Automatic variable selection algorithms in prognostic factor research in neck pain.
Journal of Clinical Medicine (Sep. 2023). DOI.
[17]
F. Ott, D. Rügamer, L. Heublein, B. Bischl and C. Mutschler.
Auxiliary Cross-Modal Representation Learning With Triplet Loss Functions for Online Handwriting Recognition.
IEEE Access 11 (Aug. 2023). DOI.
[16]
B. X. W. Liew, D. Rügamer, Q. Mei, Z. Altai, X. Zhu, X. Zhai and N. Cortes.
Smooth and accurate predictions of joint contact force timeseries in gait using overparameterised deep neural networks.
Frontiers in Bioengineering and Biotechnology 11 (Jul. 2023). DOI.
[15]
K. Rath, D. Rügamer, B. Bischl, U. von Toussaint and C. Albert.
Dependent state space Student-t processes for imputation and data augmentation in plasma diagnostics.
Contributions to Plasma Physics 63.5-6 (May. 2023). DOI.

2022

[14]
I. Ziegler, B. Ma, E. Nie, B. Bischl, D. Rügamer, B. Schubert and E. Dorigatti.
What cleaves? Is proteasomal cleavage prediction reaching a ceiling?.
Workshop on Learning Meaningful Representations of Life (LMRL 2022) at the 36th Conference on Neural Information Processing Systems (NeurIPS 2022). New Orleans, LA, USA, Nov 28-Dec 09, 2022. URL.
[13]
E. Pretzsch, V. Heinemann, S. Stintzing, A. Bender, S. Chen, J. W. Holch, F. O. Hofmann, H. Ren, F. Küchenhoff, J. Werner and Angele.
EMT-Related Genes Have No Prognostic Relevance in Metastatic Colorectal Cancer as Opposed to Stage II/III: Analysis of the Randomised, Phase III Trial FIRE-3 (AIO KRK 0306; FIRE-3).
Cancers 14.22 (Nov. 2022). DOI.
[12]
K. Rath, D. Rügamer, B. Bischl, U. von Toussaint, C. Rea, A. Maris, R. Granetz and C. Albert.
Data augmentation for disruption prediction via robust surrogate models.
Journal of Plasma Physics 88.5 (Oct. 2022). DOI.
[11]
W. Ghada, E. Casellas, J. Herbinger, A. Garcia-Benadí, L. Bothmann, N. Estrella, J. Bech and A. Menzel.
Stratiform and Convective Rain Classification Using Machine Learning Models and Micro Rain Radar.
Remote Sensing 14.18 (Sep. 2022). DOI.
[10]
M. Mittermeier, M. Weigert, D. Rügamer, H. Küchenhoff and R. Ludwig.
A deep learning based classification of atmospheric circulation types over Europe: projection of future changes in a CMIP6 large ensemble.
Environmental Research Letters 17.8 (Jul. 2022). DOI.
[9]
F. Ott, D. Rügamer, L. Heublein, B. Bischl and C. Mutschler.
Joint Classification and Trajectory Regression of Online Handwriting Using a Multi-Task Learning Approach.
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2022). Waikoloa, Hawaii, Jan 04-08, 2022. DOI.
[8]
A. Python, A. Bender, M. Blangiardo, J. B. Illian, Y. Lin, B. Liu, T. C.D. Lucas, S. Tan, Y. Wen, D. Svanidze and J. Yin.
A downscaling approach to compare COVID-19 count data from databases aggregated at different spatial scales.
Journal of the Royal Statistical Society. Series A (Statistics in Society) 185.1 (Jan. 2022). DOI.

2021

[7]
T. Weber, M. Ingrisch, M. Fabritius, B. Bischl and D. Rügamer.
Survival-oriented embeddings for improving accessibility to complex data structures.
Workshop on Bridging the Gap: from Machine Learning Research to Clinical Practice at the 35th Conference on Neural Information Processing Systems (NeurIPS 2021). Virtual, Dec 06-14, 2021. arXiv.
[6]
T. Weber, M. Ingrisch, B. Bischl and D. Rügamer.
Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation.
Workshop on Deep Generative Models and Downstream Applications at the 35th Conference on Neural Information Processing Systems (NeurIPS 2021). Virtual, Dec 06-14, 2021. PDF.
[5]
M. Mittermeier, M. Weigert and D. Rügamer.
Identifying the atmospheric drivers of drought and heat using a smoothed deep learning approach.
Workshop on Tackling Climate Change with Machine Learning at the 35th Conference on Neural Information Processing Systems (NeurIPS 2021). Virtual, Dec 06-14, 2021. PDF.
[4]
M. P. Fabritius, M. Seidensticker, J. Rueckel, C. Heinze, M. Pech, K. J. Paprottka, P. M. Paprottka, J. Topalis, A. Bender, J. Ricke, A. Mittermeier and M. Ingrisch.
Bi-Centric Independent Validation of Outcome Prediction after Radioembolization of Primary and Secondary Liver Cancer.
Journal of Clinical Medicine 10.16 (Aug. 2021). DOI.
[3]
A. Python, A. Bender, A. K. Nandi, P. A. Hancock, R. Arambepola, J. Brandsch and T. C. D. Lucas.
Predicting non-state terrorism worldwide.
Science Advances 7.31 (Jul. 2021). DOI.

2019

[2]
G. König and M. Grosse-Wentrup.
A Causal Perspective on Challenges for AI in Precision Medicine.
2nd International Congress on Precision Medicine (PMBC 2019). Munich, Germany, Oct 14-15, 2019.
[1]
J. Goschenhofer, F. M. J. Pfister, K. A. Yuksel, B. Bischl, U. Fietzek and J. Thomas.
Wearable-based Parkinson's Disease Severity Monitoring using Deep Learning.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2019). Wuerzburg, Germany, Sep 16-20, 2019. DOI.