Machine Learning Consulting Unit

Mission Statement

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.

The Machine Learning Consulting Unit (MLCU) is part of the Munich Center for Machine Learning (MCML) and offers applied researchers scientific consulting regarding the application and evaluation of machine learning methods. 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, see contact information below. Experience shows, that it is advisable to register for consulting as early in the project as possible or even at the planning stage.

Typical consulting requests

Typical inquiries include, but are not limited to

Recent and Current Projects, Cooperations and Publications

Some projects (see also publications list below) that resulted from cooperation with applied sciences in the past include


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

For other request contact mlcu[at]

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

Our team

Our team consists of senior statisticians and data scientists with multiple years of experience in methodological and applied research as well as industry.

Name       Position
Prof. Bernd Bischl       Principal Investigator
Dr. Andreas Bender       Coordinator
Dr. Ludwig Bothmann       PostDoc
Dr. David Rügamer       Professor for Data Science
Maximilian Weigert       PhD


  1. Weber, T., Ingrisch, M., Bischl, B., & Rügamer, D. (2023, August 21). Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
  2. Weber, T., Ingrisch, M., Bischl, B., & Rügamer, D. (2023). Unreading Race: Purging Protected Features from Chest X-ray Embeddings. ArXiv:2311.01349.
  3. Liew, B. X. W., Kovacs, F. M., Rügamer, D., & Royuela, A. (2023). Automatic Variable Selection Algorithms in Prognostic Factor Research in Neck Pain. Journal of Clinical Medicine, 12(19).
  4. Ott, F., Rügamer, D., Heublein, L., Bischl, B., & Mutschler, C. (2023). Auxiliary Cross-Modal Representation Learning With Triplet Loss Functions for Online Handwriting Recognition. IEEE Access, 11, 94148–94172.
  5. Bothmann, L., Wimmer, L., Charrakh, O., Weber, T., Edelhoff, H., Peters, W., Nguyen, H., Benjamin, C., & Menzel, A. (2023). Automated wildlife image classification: An active learning tool for ecological applications. Ecological Informatics, 77(102231).
  6. Rath, K., Rügamer, D., Bischl, B., von Toussaint, U., & Albert, C. (2023). Dependent state space Student-t processes for imputation and data augmentation in plasma diagnostics. Contributions to Plasma Physics.
  7. Vogel, M., Aßenmacher, M., Gubler, A., Attin, T., & Schmidlin, P. R. (2023). Cleaning potential of interdental brushes around orthodontic brackets-an in vitro investigation. Swiss Dental Journal, 133(9).
  8. Ziegler, I., Ma, B., Nie, E., Bischl, B., Rügamer, D., Schubert, B., & Dorigatti, E. (2022, October 24). What cleaves? Is proteasomal cleavage prediction reaching a ceiling? NeurIPS 2022 Workshop on Learning Meaningful Representations of Life (LMRL).
  9. Kaiser, P., Rügamer, D., & Kern, C. (2022, October 24). Uncertainty as a key to fair data-driven decision making. NeurIPS 2022 Workshop on Trustworthy and Socially Responsible Machine Learning (TSRML).
  10. Ghada, W., Casellas, E., Herbinger, J., Garcia-Benadí, A., Bothmann, L., Estrella, N., Bech, J., & Menzel, A. (2022). Stratiform and Convective Rain Classification Using Machine Learning Models and Micro Rain Radar. Remote Sensing, 14(18).
  11. Rath, K., Rügamer, D., Bischl, B., von Toussaint, U., Rea, C., Maris, A., Granetz, R., & Albert, C. (2022). Data augmentation for disruption prediction via robust surrogate models. Journal of Plasma Physics.
  12. Mittermeier, M., Weigert, M., Rügamer, D., Küchenhoff, H., & Ludwig, R. (2022). A Deep Learning Version of Hess & Brezowskys Classification of Großwetterlagen over Europe: Projection of Future Changes in a CMIP6 Large Ensemble. Environmental Research Letters.
  13. Liew, B. X. W., Kovacs, F. M., Rügamer, D., & Royuela, A. (2022). Machine learning for prognostic modelling in individuals with non-specific neck pain. European Spine Journal.
  14. Liew, B. X. W., Rügamer, D., Duffy, K., Taylor, M., & Jackson, J. (2021). The mechanical energetics of walking across the adult lifespan. PloS One, 16(11), e0259817.
  15. Mittermeier, M., Weigert, M., & Rügamer, D. (2021). Identifying the atmospheric drivers of drought and heat using a smoothed deep learning approach. NeurIPS 2021, Tackling Climate Change with Machine Learning.
  16. Weber, T., Ingrisch, M., Fabritius, M., Bischl, B., & Rügamer, D. (2021). Survival-oriented embeddings for improving accessibility to complex data structures. NeurIPS 2021 Workshops, Bridging the Gap: From Machine Learning Research to Clinical Practice.
  17. Weber, T., Ingrisch, M., Bischl, B., & Rügamer, D. (2021). Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation. NeurIPS 2021 Workshops, Deep Generative Models and Downstream Applications.
  18. Liew, B. X. W., Rügamer, D., Zhai, X. J., Morris, S., & Netto, K. (2021). Comparing machine, deep, and transfer learning in predicting joint moments in running. Journal of Biomechanics.
  19. Ott, F., Rügamer, D., Heublein, L., Bischl, B., & Mutschler, C. (2021, October 3). Joint Classification and Trajectory Regression of Online Handwriting using a Multi-Task Learning Approach. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
  20. Python, A., Bender, A., Blangiardo, M., Illian, J. B., Lin, Y., Liu, B., Lucas, T. C. D., Tan, S., Wen, Y., Svanidze, D., & Yin, J. (2021). 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).
  21. Fabritius, M. P., Seidensticker, M., Rueckel, J., Heinze, C., Pech, M., Paprottka, K. J., Paprottka, P. M., Topalis, J., Bender, A., Ricke, J., Mittermeier, A., & Ingrisch, M. (2021). Bi-Centric Independent Validation of Outcome Prediction after Radioembolization of Primary and Secondary Liver Cancer. Journal of Clinical Medicine, 10(16), 3668.
  22. Falla, D., Devecchi, V., Jimenez-Grande, D., Rügamer, D., & Liew, B. (2021). Modern Machine Learning Approaches Applied in Spinal Pain Research. In Journal of Electromyography and Kinesiology.
  23. Python, A., Bender, A., Nandi, A. K., Hancock, P. A., Arambepola, R., Brandsch, J., & Lucas, T. C. D. (2021). Predicting non-state terrorism worldwide. Science Advances, 7(31), eabg4778.
  24. Liew, B., Lee, H. Y., Rügamer, D., Nunzio, A. M. D., Heneghan, N. R., Falla, D., & Evans, D. W. (2021). A novel metric of reliability in pressure pain threshold measurement. Scientific Reports (Nature).
  25. Liew, B. X. W., Peolsson, A., Rügamer, D., Wibault, J., Löfgren, H., Dedering, A., Zsigmond, P., & Falla, D. (2020). Clinical predictive modelling of post-surgical recovery in individuals with cervical radiculopathy – a machine learning approach. Scientific Reports.
  26. Schratz, P., Muenchow, J., Iturritxa, E., Cortés, J., Bischl, B., & Brenning, A. (2020). Monitoring forest health using hyperspectral imagery: Does feature selection improve the performance of machine-learning techniques?
  27. Bender, A., Python, A., Lindsay, S. W., Golding, N., & Moyes, C. L. (2020). Modelling geospatial distributions of the triatomine vectors of Trypanosoma cruzi in Latin America. PLOS Neglected Tropical Diseases, 14(8), e0008411.
  28. Dorigatti, E., & Schubert, B. (2020). Joint epitope selection and spacer design for string-of-beads vaccines. BioRxiv.
  29. Pfister, F. M. J., Um, T. T., Pichler, D. C., Goschenhofer, J., Abedinpour, K., Lang, M., Endo, S., Ceballos-Baumann, A. O., Hirche, S., Bischl, B., & others. (2020). High-Resolution Motor State Detection in parkinson’s Disease Using convolutional neural networks. Scientific Reports, 10(1), 1–11.
  30. Stachl, C., Au, Q., Schoedel, R., Gosling, S. D., Harari, G. M., Buschek, D., Völkel, S. T., Schuwerk, T., Oldemeier, M., Ullmann, T., & others. (2020). Predicting personality from patterns of behavior collected with smartphones. Proceedings of the National Academy of Sciences.
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  31. Liew, B. X. W., Rügamer, D., Stöcker, A., & De Nunzio, A. M. (2020). Classifying neck pain status using scalar and functional biomechanical variables – development of a method using functional data boosting. Gait & Posture, 75, 146–150.
  32. Liew, B., Rügamer, D., De Nunzio, A., & Falla, D. (2020). Interpretable machine learning models for classifying low back pain status using functional physiological variables. European Spine Journal, 29, 1845–1859.
  33. Liew, B. X. W., Rügamer, D., Abichandani, D., & De Nunzio, A. M. (2020). Classifying individuals with and without patellofemoral pain syndrome using ground force profiles – Development of a method using functional data boosting. Gait & Posture, 80, 90–95.
  34. Dorigatti, E., & Schubert, B. (2020). Graph-theoretical formulation of the generalized epitope-based vaccine design problem. PLOS Computational Biology, 16(10), e1008237.
  35. Völkel, S. T., Schödel, R., Buschek, D., Stachl, C., Au, Q., Bischl, B., Bühner, M., & Hussmann, H. (2019). Opportunities and challenges of utilizing personality traits for personalization in HCI. Personalized Human-Computer Interaction, 31–65.
  36. Pfister, F. M. J., von Schumann, A., Bemetz, J., Thomas, J., Ceballos-Baumann, A., Bischl, B., & Fietzek, U. (2019). Recognition of subjects with early-stage Parkinson from free-living unilateral wrist-sensor data using a hierarchical machine learning model. JOURNAL OF NEURAL TRANSMISSION, 126(5), 663–663.
  37. Stachl, C., Au, Q., Schoedel, R., Buschek, D., Völkel, S., Schuwerk, T., Oldemeier, M., Ullmann, T., Hussmann, H., Bischl, B., & Bühner, M. (2019). Behavioral Patterns in Smartphone Usage Predict Big Five Personality Traits.
  38. Schuwerk, T., Kaltefleiter, L. J., Au, J.-Q., Hoesl, A., & Stachl, C. (2019). Enter the Wild: Autistic Traits and Their Relationship to Mentalizing and Social Interaction in Everyday Life. Journal of Autism and Developmental Disorders.
  39. König, G., & Grosse-Wentrup, M. (2019). A Causal Perspective on Challenges for AI in Precision Medicine.
  40. Goschenhofer, J., Pfister, F. M. J., Yuksel, K. A., Bischl, B., Fietzek, U., & Thomas, J. (2019). Wearable-based Parkinson’s Disease Severity Monitoring using Deep Learning. Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, 400–415.
    link | pdf
  41. Fossati, M., Dorigatti, E., & Giuliano, C. (2018). N-ary relation extraction for simultaneous T-Box and A-Box knowledge base augmentation. Semantic Web, 9(4), 413–439.
  42. Rietzler, M., Geiselhart, F., Thomas, J., & Rukzio, E. (2016). FusionKit: a generic toolkit for skeleton, marker and rigid-body tracking. Proceedings of the 8th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, 73–84.
  43. Rueger, S., Goschenhofer, J., Nath, A., Firsching, M., Ennen, A., & Bischl, B. (2022). Deep-Learning-based Aluminum Sorting on Dual Energy X-Ray Transmission Data. Sensor-Based Sorting and Control.