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.

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

Selection of projects (see also publications list below) 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

Our team

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

Publications

1. Liew, B. X. W., Pfisterer, F., Rügamer, D., & Zhai, X. (2024). Strategies to optimise machine learning classification performance when using biomechanical features. Journal of Biomechanics, 111998.
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2. Weber, T., Ingrisch, M., Bischl, B., & Rügamer, D. (2024, January). Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
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3. Liew, B. X. W., Rügamer, D., & Birn-Jeffery, A. (2023). Neuromechanical stabilisation of the centre of mass during running. Gait & Posture.
4. Weber, T., Ingrisch, M., Bischl, B., & Rügamer, D. (2023). Unreading Race: Purging Protected Features from Chest X-ray Embeddings. ArXiv:2311.01349.
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5. 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).
6. 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. https://doi.org/10.1109/ACCESS.2023.3310819
7. 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).
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8. Liew, B. X. W., Rügamer, D., Mei, Q., Altai, Z., Zhu, X., Zhai, X., & Cortes, N. (2023). Smooth and accurate predictions of joint contact force timeseries in gait using overparameterised deep neural networks. Frontiers in Bioengineering and Biotechnology: Biomechanics.
9. 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.
10. Gertheiss, J., Rügamer, D., Liew, B., & Greven, S. (2023). Functional Data Analysis: An Introduction and Recent Developments.
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11. Hartl, W. H., Kopper, P., Xu, L., Heller, L., Mironov, M., Wang, R., Day, A. G., Elke, G., Küchenhoff, H., & Bender, A. (2023). Relevance of Protein Intake for Weaning in the Mechanically Ventilated Critically Ill: Analysis of a Large International Database. Critical Care Medicine.
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12. Hendrix, P., Sun, C. C., Brighton, H., & Bender, A. (2023). On the Connection Between Language Change and Language Processing. Cognitive Science, 47(12), e13384.
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13. Coens, F., Knops, N., Tieken, I., Vogelaar, S., Bender, A., Kim, J. J., Krupka, K., Pape, L., Raes, A., Tönshoff, B., Prytula, A., & Registry, C. (2023). Time-Varying Determinants of Graft Failure in Pediatric Kidney Transplantation in Europe. Clinical Journal of the American Society of Nephrology.
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14. 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).
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15. 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).
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16. 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).
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17. 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).
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18. 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.
19. 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.
20. Beaudry, G., Drouin, O., Gravel, J., Smyrnova, A., Bender, A., Orri, M., Geoffroy, M.-C., & Chadi, N. (2022). A Comparative Analysis of Pediatric Mental Health-Related Emergency Department Utilization in Montréal, Canada, before and during the COVID-19 Pandemic. Annals of General Psychiatry, 21(1), 17.
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21. 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.
22. Pretzsch, E., Heinemann, V., Stintzing, S., Bender, A., Chen, S., Holch, J. W., Hofmann, F. O., Ren, H., Bösch, F., Küchenhoff, H., Werner, J., & Angele, M. K. (2022). 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), 5596.
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23. 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.
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24. 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.
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25. 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.
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26. 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.
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27. 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.
28. 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).
29. 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). https://doi.org/10.1111/rssa.12738
30. 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. https://doi.org/10.3390/jcm10163668
31. Falla, D., Devecchi, V., Jimenez-Grande, D., Rügamer, D., & Liew, B. (2021). Modern Machine Learning Approaches Applied in Spinal Pain Research. Journal of Electromyography and Kinesiology.
32. 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. https://doi.org/10.1126/sciadv.abg4778
33. 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).
34. Liew, B. X. W., Rügamer, D., De Nunzio, A., & Falla, D. (2021). Harnessing time-series kinematic and electromyography signals as predictors to discriminate amongst low back pain recovery status. Brain and Spine, 1, 100236.
35. 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.
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36. 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?
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37. 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. https://doi.org/10.1371/journal.pntd.0008411
38. Dorigatti, E., & Schubert, B. (2020). Joint epitope selection and spacer design for string-of-beads vaccines. BioRxiv. https://doi.org/10.1101/2020.04.25.060988
39. 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.
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40. 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.
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41. 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.
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42. Dorigatti, E., & Schubert, B. (2020). Graph-theoretical formulation of the generalized epitope-based vaccine design problem. PLOS Computational Biology, 16(10), e1008237. https://doi.org/10.1371/journal.pcbi.1008237
43. 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.
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44. 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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45. 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. https://doi.org/10.31234/osf.io/ks4vd
46. 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.
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47. König, G., & Grosse-Wentrup, M. (2019). A Causal Perspective on Challenges for AI in Precision Medicine.
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48. 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.
49. 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. https://doi.org/10.1007/s10803-019-04134-6
50. 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.
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51. 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. https://doi.org/10.3233/SW-170269
52. Casalicchio, G., Lesaffre, E., Küchenhoff, H., & Bruyneel, L. (2017). Nonlinear Analysis to Detect if Excellent Nursing Work Environments Have Highest Well-Being. Journal of Nursing Scholarship, 49(5), 537–547.
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53. 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.
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54. Bergmann, S., Ziegler, N., Bartels, T., Hübel, J., Schumacher, C., Rauch, E., Brandl, S., Bender, A., Casalicchio, G., Krautwald-Junghanns, M.-E., & others. (2013). Prevalence and severity of foot pad alterations in German turkey poults during the early rearing phase. Poultry Science, 92(5), 1171–1176.
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55. Ziegler, N., Bergmann, S., Huebei, J., Bartels, T., Schumacher, C., Bender, A., Casalicchio, G., Kuechenhoff, H., Krautwald-Junghanns, M.-E., & Erhard, M. (2013). Climate parameters and the influence on the foot pad health status of fattening turkeys BUT 6 during the early rearing phase. BERLINER UND MUNCHENER TIERARZTLICHE WOCHENSCHRIFT, 126(5–6), 181–188.
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56. 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. https://doi.org/10.2370/9783844085457