Prof. Dr. Lars Linsen, Institut für Informatik

A main objective of scientific research is to create models that describe natural phenomena. Typically, such models introduce parameters, where the parameter values are not a priori known. Instead, simulations are run with multiple parameter settings for the given model leading to a simulation ensemble. Then, it is a data analysis task to interpret the simulation outcome. An interactive, user-centric approach allows the scientist to use domain knowledge to steer the analysis and interpret the findings. Visualization methods facilitate such an analysis. Given that natural phenomena are typically spatio-temporal, the ensemble consists of multifaceted data, where the facets refer to space, time, and parameters. Commonly, analysis methods are developed that target a subset of the facets. In this project, we propose a unifying concept for analyzing the multifaceted data of spatio-temporal ensembles. We propose a common and scalable data management in the form of what we call the multifaceted hypercube. We further propose different interactive visual analysis methods for the multifaceted hypercube. They target overview visualizations, direct visualizations of all facets and flexibly adjustable combinations thereof, interactive and automatic feature extraction from the multifaceted hypercube, uncertainty-aware visualizations of multifaceted regions as well as comparative visualizations of multiple multifaceted hypercubes. Thus, we are proposing a holistic approach for the main analysis objectives of simulation ensembles that require a joint consideration of all facets. We plan to evaluate our approach within real-world scenarios from different domains.

The project aims to improve the production of paints and coatings using modern measurement methods. The aim is to develop and use optimized, in-process analytics in the adaptation of paint formulations using modern methods of data evaluation through to an in-depth potential analysis for the application of AI.

The multiscale imaging strategy central to this initiative imposes novel data analysis challenges. The high complexity of the acquired data results from their nature of being volumetric, time-varying, large, multiscale, and forming cohorts). Meeting these challenges requires basic research in the fields of image analysis, machine learning, and visualization. Machine learning will be used to uncover inherent relationships between patterns at multiple scales. An interactive visual approach supports the user-centric analysis of detected features.

The deliverable of this project will be generally applicable, effective, and efficient methods supporting the overall goal of multiscale data analysis.

Cross-border migration movements of people with undocumented dates of birth have led to a need for forensic age assessments when the age of these people is of legal significance. In Germany and numerous other countries the age limit of 18 has the greatest practical relevance. Only the assessment of the ossification of the medial clavicular epiphysis currently allows proof beyond reasonable doubt of the completion of the 18th year of life. Thin-slice CT is currently considered the method of choice for imaging the medial clavicular epiphysis. The main objective of the project is to considerably improve the validity and reliability of age assessments based on a CT scan of the clavicles. This includes as a basis the establishment of a first reference population optimised in terms of sample size and age distribution, and free of individuals with pathologies or medications that may affect bone maturation. Three independent experts will determine stages of clavicular ossification years according to established scales to maximize validity. The use of machine learning techniques will objectify the assessment of the ossification status of the medial clavicular epiphysis; it will also allow the search for new features applicable at older ages, not readily available for the human eye. Ratings based on machine learning methodology (both using established scales and new predictors) will be transformed into an open demonstrator for a clinical decision support system which might later on help medicolegal specialists outside of the few existing expert centers worldwide to perform legal age assessment with an objectively derivable diagnostic accuracy.