Get to know the members of the HIDSS4Health Data Science Groups.

Achim Streit
KIT
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The Streit team is enabling data-intensive science through generic informatics R&D in the areas data management, data analytics, federated computing and scheduling.

Ali Sunyaev
KIT
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The Sunyaev team designs, develops, and evaluates reliable and purposeful software and information systems within the scope of information security solutions and innovative health IT applications.

Alin Albu-Schäffer
KIT
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The Albu-Schäffer team addresses in cooperation with the Asfour team medical robotics and the related data acquisition and Interpretation.

Anne Koziolek
KIT
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The Koziolek team researches how to ease development of data-intensive systems, esp. the selection of appropriate machine learning algorithms with respect to suitability and performance.

Benedikt Brors
DKFZ
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The Brors team develops algorithms for personalized medicine, cancer epigenetics and single-cell sequencing and applies them in a clinical context as well as in large international consortia.

Carsten Dachsbacher
KIT
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The research group developes methods for interactive visualization, high performance computer graphics, and radiative transport simulations.

Carsten Rother
UNI HD
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We (C. Rother, B. Savchynskyy, U. Köthe) work on machine learning and combinatorial optimization (IP, LP) - e.g. 2D/3D tracking, explainable ML, NN+Graphical Models

Emilia Grass
KIT
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Our objective is to strengthen the resilience of healthcare systems against cyber-attacks by using stochastic programming, simulation and machine learning.

Filip Sadlo
UNI HD
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The Visual Computing Group develops novel techniques for visual data analysis, with a focus on topological analysis and feature extraction.

Franziska Mathis-Ullrich
KIT
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The Mathis-Ullrich team aims to increase efficiency and patient-safety during medical procedures through innovative robotic instruments, as well as robot-assistance and machine learning for surgical intervention.

Holger Fröning
UNI HD
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Performance, energy-efficiency and programmability: HPC/HPA, deep learning, reconfigurable logic. Example 1: deep learning on embedded systems. Example 2: simplified multi-GPU programming

Klaus Maier-Hein
DKFZ
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The Maier-Hein team develops machine learning algorithms, mathematical modelling approaches for computational image understanding and large-scale information processing.

Lena Maier-Hein
DKFZ
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The Maier-Hein team aims to improve the quality of interventional healthcare and its value computationally. It supports the physician throughout the entire process of disease diagnosis, therapy and follow-up with the right information at the right time.

Martin Frank
KIT
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The research group Computational Science and Mathematical Methods (CSMM) is an interdisciplinary research group working on different challenges in mathematical modeling. We are unified by our interest in method-oriented mathematics, in mathematical modeling inspired by applications, and in the didactics of mathematical modeling.
Our research focuses on bringing modern mathematical techniques such as modeling, simulation, optimization, inverse problems, uncertainty quantification, and machine learning/artificial intelligence into the real-world. Both by teaching them to students in schools and universities and by advancing the research in relevant fields of application.
Furthermore, we take an interest in kinetic theory, developing models, numerical methods and software implementations and combining them with the aforementioned techniques.

Melanie Schienle
KIT
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The Schienle team develops novel statistical methods for interpretable machine learning, including networks, treatment effects and prediction uncertainty.

Michael Beigl
KIT
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The Beigl team develops AI-driven analytics and Big Data methods and systems to solve problems in application domains.

Michael Gertz
UNI HD
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The Gertz team focuses on novel models and techniques in support of information extraction, data/text mining, machine learning, and network analysis for heterogeneous data.

Oliver Stegle
DKFZ
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The Stegle group develops and applies statistical approaches and methods based on machine learning for analysing high-dimensional molecular data modalities.

Pascal Friederich
KIT
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After his Ph.D. in physics, Pascal Friederich received a Marie-Sklodowska-Curie Postdoctoral Fellowship at Harvard University and the University of Toronto where he worked on machine learning methods for chemistry. In 2020 Pascal Friederich was appointed assistant professor at the Informatics Department of the Karlsruhe Institute of Technology, leading the AI for Materials Science (AiMat) research group. His research focuses on developing and applying machine learning methods for property prediction, simulation, understanding, and design of molecules and materials. In 2022, Pascal Friederich received the Heinz-Maier-Leibnitz Prize from the German Research Foundation.

Peter Sanders
KIT
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The Sanders team develops basic toolbox algorithms and software libraries for handling large data sets in a scalable way.

Rainer Stiefelhagen
KIT
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The Stiefelhagen team investigates methods to analyse images using weak supervision, for example by jointly analysing medical images and their associated clinical reports.

Ralf Mikut
KIT
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The Mikut team analyses 2D, 3D and 3D+t biomedical images and related data using image analysis and machine learning methods.

Robert Strzodka
UNI HD
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The chair Application Specific Computing focuses on efficient interactions of mathematic, algorithmic and architectural aspects in heterogeneous high performance computing (GPUs, FPGAs, many-core).

Shiva Faeghi
KIT
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Our research focuses on development and application of computational methods to analyze and optimize healthcare processes.

Stefan Riezler
UNI HD
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The Riezler group develops statistical machine learning methods for ambiguous and noisy data, with a focus on natural language processing.

Tamim Asfour
KIT
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The Asfour team develop data-driven methods and algorithms for skill learning, motion generation and prediction in the context of robot-assisted surgery and exoskeletons.

Till Bärnighausen
UNI HD
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Till Bärnighausen is Alexander von Humboldt Professor and Director of the Heidelberg Institute of Global Health (HIGH), Heidelberg University, Germany. He is also Senior Faculty at the Africa Health Research Institute (AHRI) in South Africa and fellow at the Harvard Center for Population and Development Studies.
Till’s research focuses on creating and evaluating global health interventions. His research has been funded by the US National Institutes of Health, European Union, German Research Foundation, Alexander von Humboldt Foundation, Wellcome, Bill & Melinda Gates Foundation, Clinton Health Access Initiative, Else-Kröner-Fresenius Foundation, USAID, UNAIDS, WHO, KfW, World Bank.
Till has previously worked as professor at Harvard T.H. Chan School of Public Health; as medical doctor in Germany, China and South Africa; and as management consultant for McKinsey & Company. Till holds doctoral degrees in international and population health (Harvard) and history of medicine (Heidelberg), and master degrees in health systems management (LSHTM), financial economics (SOAS), and innovation and entrepreneurship (HEC Paris).

Ullrich Köthe
UNI HD
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Ullrich Köthe’s team works on interpretable machine learning methods that help to extract knowledge from data in the natural and life sciences. Our speciality are invertible neural networks.

Vincent Heuveline
UNI HD
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The EMCL focuses on uncertainty quantification (UQ) in scientific computing, high performance and data intensive computing, with main application focuses in medical engineering.