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
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).
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
Little is known about the time-varying determinants of kidney graft failure in children. We performed a retrospective study of primary pediatric kidney transplant recipients (younger than 18 years) from the Eurotransplant registry (1990-2020). Piece-wise exponential additive mixed models were applied to analyze time-varying recipient, donor, and transplant risk factors. Primary outcome was death-censored graft failure.
The association between protein intake and the need for mechanical ventilation (MV) is controversial. We aimed to investigate the associations between protein intake and outcomes in ventilated critically ill patients.
The goal of this work is to generate large statistically representative data sets to train machine learning models for disruption prediction provided by data from few existing discharges. Such a comprehensive training database is important to achieve satisfying and reliable prediction results in artificial neural network classifiers. Here, we aim for a robust augmentation of the training database for multivariate time series data using Student $t$ process regression. We apply Student $t$ process regression in a state space formulation via Bayesian filtering to tackle challenges imposed by outliers and noise in the training data set and to reduce the computational complexity. Thus, the method can also be used if the time resolution is high. We use an uncorrelated model for each dimension and impose correlations afterwards via colouring transformations. We demonstrate the efficacy of our approach on plasma diagnostics data of three different disruption classes from the DIII-D tokamak. To evaluate if the distribution of the generated data is similar to the training data, we additionally perform statistical analyses using methods from time series analysis, descriptive statistics and classic machine learning clustering algorithms.
Europe was hit by several, disastrous heat and drought events in recent summers. Besides thermodynamic influences, such hot and dry extremes are driven by certain atmospheric situations including anticyclonic conditions. Effects of climate change on atmospheric circulations are complex and many open research questions remain in this context, e.g., on future trends of anticyclonic conditions. Based on the combination of a catalog of labeled circulation patterns and spatial atmospheric variables, we propose a smoothed convolutional neural network classifier for six types of anticyclonic circulations that are associated with drought and heat. Our work can help to identify important drivers of hot and dry extremes in climate simulations, which allows to unveil the impact of climate change on these drivers. We address various challenges inherent to circulation pattern classification that are also present in other climate patterns, e.g., subjective labels and unambiguous transition periods.