Research Areas
The Department of Computer Science at the University of Tübingen is located in a unique research landscape in Germany. Along with the department of Mathematics and Natural Sciences, the department of Computer Science is invoveld in several joint projects with the three Max Planck Institutes for Inteligent Systems (MPI-IS), Biological Cybernetics (MPI-BC) and Developmental Biology (MPI-DB), to perform oustanding and intenationaly recognisable cutting-edge research. Close research connections also exist with the Werner-Reichardt-Center for Integrative Neuroscience (CIN), the Bernstein Center for Computational Neuroscience (BCCN) Tübingen, the Hertie-Institut for Clinical Brain Research and the Leibniz-Institut für Wissensmedien (IWM).
The Department of Computer Science is involved in cross-cutting of state of the art research projects. Starting January 1st 2019 is the Cluster of Excellence "Machine Learning in Science" which was acquired together with the MPI for Intelligent Systems and the Leibniz-Institut für Wissensmedien as part of the Excellence Initiative. Together with the MPI for Intelligent Systems, the Department of Computer Science is one of the driving research partners in the Cyber Valley initiative. This initiative involves bundling the local competencies in industry and research into intelligent and self-learning systems in the Reutlingen-Stuttgart-Tübingen region, as well as visibly developing them internationally.
Other comprehensive research projects involving the Department of Computer Science include:
- Center of Competence for Artificial Intelligence and Machine Learning
- Collaborative Research Center "Robust Vision" (SFB 1233) (with MPI-IS)
- International Max Planck Research School “From Molecules to Organisms" (IMPRS) (et al. with MPI-EB)
- International Max Planck Research School for Intelligent Systems (IMPRS-IS) (et al. with MPI-IS)
Ethical issues regarding machine learning and artificial intelligence are investigated, for example, in the Ethics & Philosophy Lab within the Cluster of Excellence.
Bioinformatics & Medical Informatics
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:
Work Groups
- Dr. Franz Baumdicker: Mathematical and Computational Population Genetics
- Dr. Christian Baumgartner: Machine Learning in Medical Image Analysis
- Prof. Daniel Huson: Algorithims in Bioinformatics
- Prof. Oliver Kohlbacher: Applied Bioinformatics
- Jun.-Prof. Dr. Michael Krone: Big Data Visual Analytics in den Lebenswissenschaften
- Prof. Sven Nahnsen: Biomedical Data Science
- Prof. Kay Nieselt: Integrative Transcriptonomics
- Prof. Nico Pfeifer: Methods in Medical Informatics
- Prof. Manfred Claassen (coopted): Clinical Bioinformatics
- Prof. Dr. Carsten Eickhoff (coopted): E-Health and Medical Data Science
- Prof. Dr.-Ing. Thomas Küstner (coopted): Medical Image and Data Analysis
- Prof. Andrei Lupas (Honorary prof.): Proteinevolution
- Prof. Stephan Ossowski (coopted): Computational Genomics
Selected Current Research Projects
Collaborative Projects
- 2019–2026: Cluster of Excellence: Controlling Microbes to Fight Infections (CMFI)
- 2019–2026: Cluster of Excellence: Machine Learning: New Perspectives for Science
- 2019–2023: DFG-Joint Project: Omics Analysis and Bioinformatics, subproject Z03 of the TransRegio TRR 261 Cellular Mechanisms of Antibiotic Action and Production
- 2019–2020: EU-Network (H2020-SC1-2018-Single-Stage-RTD): CARE: Common Action Against HIV/TB/HCV Across The Regions Of Europe
- 2018–2022: DFG-Competence Center: NGS Competence Center Tübingen (NCCT)
- 2018–2021: BMBF-Konsortium: München-Tübingen Allianz für Datenintegration und zukünftige Medizin (DIFUTURE)
- 2017–2021: EU-Network (H2020-MSCA-ITN-2017): Analytics for Biologics (A4B)
- 2016–2021: BMBF-Joint Project: Semantic Support for Predictive Modeling in Systems Medicine (XplOit), Subgroup of i:DSem
- 2016–2019: BMBF-Joint Project: Personalized Oncology via Semantic Intergration of Data (PersOnS), subgroup of i:DSem
- 2015–2020: BMBF-Network: German Network for Bioinformatics Infrastructure (de.NBI) – Establishment phase: Performance Center CiBi
- the German node of ELIXIR-Networks
- 2015–2019: BMBF-Joint Project: Data Management and Multiscale Computational Modeling, Subproject 3 of the consortium Multiscale HCC
- International Max Planck Research School “From Molecules to Organisms" (IMPRS) (et al. with MPI-EB)
Individual Projects
- 2018–2021: DFG-Project: Visual Analysis of Protein-Ligand Interactions (PROLINT) (with the University of Ulm)
- 2017–2020: DFG-Project: Etiology and Pathogenesis of MRKH syndromes
- 2017–2021: MSIBW-Project: bwHealthCloud
- 2016–2019: NIH-Project: Development of essential software and community resources for modeling in systems biology in Java™
- 2016–2019: DFG-Project: Highly efficient and accurate Algorithms for the Mobile Analysis of Microbes (MAIRA)
Machine Learning
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)
Work Groups
- Prof. Robert Bamler: Data Science and Machine Learning
- Dr. Christian Baumgartner: Machine Learning in Medical Image Analysis
- Prof. Martin Butz: Cognitive Modelling
- Dr. Katharina Eggensperger: Automated Machine Learning for Science
- Dr. Shahram Eivazi (IoC): Autonomous Systems
- Prof. Andreas Geiger: Autonomous Vision
- Dr. Konstantin Genin: Epistemology and Ethics of Machine Learning
- Prof. Matthias Hein: Machine Learning
- Prof. Philipp Hennig: Methods in Machine Learning
- Dr. Nicole Ludwig: Machine Learning in Sustainable Energy Systems
- Prof. Ulrike Luxburg: Theory of Machine Learning
- Prof. Jakob Macke: Machine Learning in Science
- Prof. Georg Martius: Distributed Intelligence
- Prof. Seong Joon Oh: Scalable Trustworthy AI (STAI)
- Prof. Nico Pfeifer: Methods in Medical Informatics
- Prof. Gerard Pons-Moll: Continuous Learning on Multimodal Data Streams
- Dr. Leonel Rozo (IoC): Geometric Learning for Motion and Interaction
- Dr. Claire Vernade: Foundations for Lifelong Reinforcement Learning
- Prof. Bob Williamson: Foundations of Machine Learning Systems
- Prof. Andreas Zell: Cognitive Systems
- Dr. Dan Zhang (IoC): Safe Deep Learning
- Prof. Matthias Bethge (coopted): Computational Neuroscience and Machine Learning
- Prof. Michael J. Black (Honorary prof.): Perceiving Systems
- Prof. Moritz Hardt (Honorary prof.): Social Foundations of Computation
- Prof. Bernhard Schölkopf (Honorary prof., FB Physik): Empirical Inference
Software & Systems Engineering
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
Work Groups
- Jun.-Prof. Dr. Jonathan Brachthäuser: Software Engineering
- Prof. Oliver Bringmann: Embedded Systems
- Prof. Torsten Grust: Database Systems
- Prof. Wolfgang Küchlin: Symbolic Computation
- Prof. Michael Menth: Communication Networks
- Prof. Klaus Ostermann: Programming Languages
- Prof. Thomas Walter: Information Services
- Dr. Shahram Eivazi (IoC): Autonomous Systems
- Prof. Dr. habil. Thomas Kropf (Honorary prof.): Technical Computer Science
Selected Current Research Projects
Collaborative Projects
- 2019–2026: Cluster of Excellence: Machine Learning: New Perspectives for Science
- 2017–2019: DFG network: LEAD Graduate School & Research Network
- 2016–2019: Kooperatives Promotionskolleg gefördert durch das MWK-BW: EAES: Entwurf und Architektur Eingebetteter Systeme
Individual Projects
- 2018–2023: BMBF project: GENIAL! Gemeinsame Elektronik Roadmap für Innovationen der automobilen Wertschöpfungskette
- 2018–2023: Industry project: Industry-on-Campus-Professur zu Data Analytics und Big Data (mit Schufa)
- 2018–2022: DFG project: RESIST II: Resilienzbewertung von Wahrnehmungs- und Planungsansätzen in kooperativ interagierenden Automobilen bei unerwarteten Störungen
- 2018–2021: DFG project: PDGREE: Fein-granulare Analyse der Datenherkunft in ausdrucksstarken Anfragen
- 2017–2020: DFG project: Congestion Management for Packet-Based Communication Networks (CoMa)
- 2017–2020: Baden-Württemberg Stiftung gGmbH: HKONSENS-NHE: Entwicklung eines kontext-sensitiven neural-gesteuerten Hand-Exoskeletts zur Wiederherstellung der Alltagsfähigkeit und Autonomie nach Hirn- und Rückenmarksverletzungen
- 2016–2020: Industrie project: VideoSim: Modellierung und Variation von Umgebungseinflüssen auf Umfeldsensorik (mit Bosch)
- 2017–2019: BMBF project: CONFIRM: Automatisierter Firmware-Entwurf unter Berücksichtigung von Timing- und Power-Budgets für anwendungsspezifische Elektroniksysteme
- 2016–2019: DFG project: ALIEN: Abstraktionen, Sprachen und Implementierungstechniken, die Kluft zwischen Programmier- und Anfragesprachen überwinden
- 2016–2019: Industry project: autoSWIFT: Schnellere Innovationszyklen für Elektroniksysteme entlang der Automobilwertschöpfungskette (mit Infineon)
Theory
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"
Work Groups
- Apl. Prof. Britta Dorn: Computational Social Choice, Parametrisierte Komplexität
- Prof. Matthias Hein: Machine Learning
- Prof. Michael Kaufmann: Algorithms
- Prof. Klaus-Jörn Lange: Komplexitätstheorie, Formale Sprachen
- Jun.-Prof. Dr. Anna Levina (Martius): Selbstorganisation und Optimalität in Neuronalen Netzwerken
- Prof. Ulrike von Luxburg: Theory of Machine Learning
- Dr. Lena Schlipf: Teaching Specialist for Theoretical Computer Science
- Prof. Peter Schroeder-Heister: Logic and Language Theory
- Prof. Dr. Reinhard Kahle (coopted): Philiosophy and History of Science
Vision & Cognition
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.
Work Groups
- Prof. Martin Butz: Cognitive Modelling
- Prof. Volker Franz: Experimental Cognitive Science
- Prof. Andreas Geiger: Autonomous Vision
- Prof. Hendrik Lensch: Computer Graphics
- Jun.-Prof. Dr. Anna Levina (Martius): Selbstorganisation und Optimalität in Neuronalen Netzwerken
- Prof. Zhaoping Li: Sensory and Sensorimotor Systems
- Prof. Gerard Pons-Moll: Continuous Learning on Multimodal Data Streams
- Dr. Leonel Rozo (IoC): Geometric Learning for Motion and Interaction
- Prof. Andreas Schilling: Media Informatics (Visual Computing)
- Prof. Felix Wichmann: Neuronal Information Processing
- Dr. Charley Wu: Human and Machine Cognition
- Prof. Andreas Zell: Cognitive Systems
- Dr. Dan Zhang (IoC): Safe Deep Learning
- Prof. Philipp Berens (coopted): Data Science for Vision Research
- Prof. Michael Franke (coopted): General Linguistics & Pragmatics
- Prof. Martin Giese (coopted): Computational Sensomotorics
- Prof. Bettina Rolke (coopted): Evolutionary Cognition
Selected Current Research Projects
Collaborative Projects
- 2019–2026: Cluster of Excellence: Machine Learning: New Perspectives for Science
- 2017–2020: Collaborative Research Center: Robust Vision (SFB 1233) (mit MPI-IS)
Individual Projects
- 2020–2025: ERC Starting Grant: Learning Generative 3D Scene Models for Training and Validating Intelligent Systems (LEGO-3D)
- 2019–2021: DFG project: Development of the agentive self: Critical components in the emerging ability of action prediction and goal anticipation
- 2019–2021: BMBF project: DeepStereoVision: Effiziente und genaue Tiefenwahrnehmung durch Stereovision mit Deep Learning und FPGAs
- 2018–2021: BMWi project: iBinPick: Entwicklung eines intelligenten bin picking Robotersystems
- 2018–2020: BMBF project: FarmingIOS: Intelligente optische Sensorik zur Früherkennung und Behandlung von Pflanzenkrankheiten
- 2014–2019: DFG project: Die Integration von früher Sehverarbeitung, Salienzmodellen und Blicksteuerung: Experimente, Modellierung und räumliche Statistik