Overview

Research in Bioinformatics, Medical Informatics and Systems Biology is rooted in the development of novel models, algorithms, and software tools dedicated to answering pertinent questions in the life sciences. Current and past research projects in Tübingen have dealt with developing solutions to challenging questions arising out of the diverse fields of phylogenetics, evolutionary of protein structures, structural bioinformatics, computational drug discovery, immuno-informatics, genomics, microbiome analysis, and gene expression analysis. Diverse and complex, these fields contain an immense opportunity for pushing the realm of knowledge and thereby solving contemporary problems in medicine, which require solutions to streamline the development of personalised cancer immune-therapies or close the gap in our understanding of microbiome-host interactions during an infection.

The Tübingen researchers in Bioinformatics, Medical Informatics and Systems Biology are also members in the following interdisciplinary centers:

• Institute for Bioinformatics and Medical Informatics (IBMI)
• Center for Quantitative Biology (QBiC)
• German Center for Infection Research (DZIF) 

Overview

Research in the department of Machine Learning strives to contribute to the understanding of the following basic principles:

• What are the prerequisites for learning to take place and which guiding principles exist?
• What guarantees can we give for the work performed by Machine Learning Algorithms?
• How can we quantify the uncertainty in our predictions?

We also work on concrete applications of Machine Learning such as Computer Vision, Robotics, Medicine and Biology. Our research therefore also contributes to the fact that machine learning can play a more central role in the process of gaining scientific knowledge in a wide range of disciplines.

Selected Current Research Projects

Collaborative Projects

• 2019–2026: Cluster of Excellence: Machine Learning: New Perspectives for Science
• 2018–2022: Competence Center for Artificial Intelligence and Machine Learning
• International Max Planck Research School for Intelligent Systems (IMPRS-IS) (et al. with MPI-IS)

Individual Projects

• 2023–2028: ERC Consolidator Grant: DeepCoMechTome
• 2022–2028: Project founded by Carl Zeiss Foundation: Certification and Foundations of Safe Machine Learning Systems in Healthcare
• 2020–2025: ERC Starting Grant: Learning Generative 3D Scene Models for Training and Validating Intelligent Systems (LEGO-3D)
• 2019–2024: ERC Starting Grant: Deeply Explainable Intelligent Machines (DEXIM)
• 2019–2023: DFG project: Tübingen Machine Learning Cloud
• 2018–2023: ERC Starting Grant: Probabilistic Automated Numerical Analysis in Machine Learning and Artificial Intelligence (PANAMA)

Overview

The Software and Systems Engineering group is actively engaged in research that deals with the core practical and technical aspects of Computer Science from first principles to practical approaches. Applied projects are focused mainly on the development of large-scale software architectures, the analyses and transformation of structured data, algorithms for automatic evidence generation and optimization as well as the development of solutions for complex web-based distributed systems. Technical approaches are more focused on the analyses and optimization of complex embedded systems, and communication networks and computational architectures, all of which rely heavily on the application of machine learning algorithms.

Practical Computer Science

• Declarative Computer Languages for the Analyses and Transformation of Structured Data
• New Paradigms for Data-Intensive Programming
• Efficient Construction of Large Software Systems
• Definition, Analyses, and Verification of Software Systems
• Algorithms for Calculation of Mathematically Conclusive logic

Technical Computer Science

• Design and Verification of Secure Embedded Systems
• Timing - and Power-Analyses of Embedded Systems
• Architecture Design: From the Systems Level to Tapeout
• Neural Interfaces and Brain Signal Decoding
• Design, Optimization, and Application of Communication Networks
• Software-Defined Networking, 5G and the Internet of Things

Overview

Theoretical computer sciences performs research on the very foundations of the field of computer science. It tries to answer fundamental questions such as:

• Which functions can be calculated by a computer in principle, and which ones cannot be computed?
• Which problems can be solved by efficient algorithms?
• How can we compare different algorithms, and in which sense are some algorithms "better" than others?
• Can computers "learn" to perform certain tasks, and if yes, which ones and how?

Moreover, the field of theoretical computer science develops formal frameworks that can be used by other branches of computer science to describe and analyze complex systems.

Selected Current Research Projects

Collaborative Projects

• 2019–2026: Cluster of Excellence: Machine Learning: New Perspectives for Science

Individual Projects

• 2018–2021: DFG projekt: Dualität und Schaltkreiskomplexität
• 2018–2020: DFG project: Neue Modelle und Methoden zum effektiven orthogonalen Layout von Graphen
• 2018–2019: DFG project: Jenseits von Planarität: Eine Verallgemeinerung des Konzepts "Planarität im Graphenzeichnen"

Overview

The Department of Computer Science in Tübingen is home to a unique interdisciplinary research group which has a strong focus on visual cognition, multi-sensor and sensor-motors processing and their interactions with the goal of making connections to the abstract underpinnings of human cognitive mechanisms and neural encoding processes. In order to achieve these research goals, which drive the understanding of psychophysics and cognitive neurosciences, sophisticated techniques in advanced image analysis, robotics, intelligent software systems, and computer graphics are employed.

• Photo realistic 3D-Acquisition
• Mobile Robots
• Self-Learning Avatars in Virtual Reality Environments
• Hand Eye Tracking
• Computer Models of Human Vision
• Generative, Recurrent, and Self-organized Artificial Neural Networks

The department is also involved in the interfaculty Cognitive Science Center at the University of Tübingen. The CSC pursues the goal, together with the humanities and natural sciences, of gaining a deeper understanding of cognition. For cognition generates behavior, language, and thereby our culture. It is indispensably grounded in physics, biology, and neurobiology and can be understood through modeling using machine learning, mathematics, and statistics.