Research Groups


Research in the department is carried out by a number of different groups, each with its own focus. The groups are involved in numerous national and international projects supported by various funding bodies, and publish regularly in internationally recognized conferences, journals and edited books. Brief descriptions of each group as well as a link to the group's web page can be found by following the links below.

Applied Statistics and Modelling

  • Fuzzy statistics
  • Data mining and econometrics methods
  • Survey statistics
  • Treatment effect evaluation

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Decision Support & Operations Research

  • Modeling language development for mathematical optimization problems
  • Combinatorial optimization and graph theory
  • Heuristic and meta-heuristic methods
  • Decision support: models, methods and applications

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Digitalization and Information Systems Group

  • Design, Implementation and Analysis of Enterprise Information Systems
  • Enterprise Modeling and Metamodeling
  • Blockchains
  • Visualization and Device-Iess Interaction

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Document, Image and Video Analysis

  • Multimodal Information Processing
  • Machine Learning & Deep Learning
  • Structural Pattern Recognition, Graph Matching
  • (Historical) Document Analysis, Recognition and Processing
  • Human Machine Interaction
  • Visualization

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eXascale Infolab

  • Big Data
  • Social Data
  • Data Science Infrastructures
  • Crowdsourcing

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Foundations of Dependable Systems

  • complementation of Büchi automata
  • omega-automata with blind counters
  • security in electronic voting
  • algorithms for ecological graphs
  • intrusion detection in smart grids

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Human Computer Interaction

  • Multimodal Human Machine Interaction
  • Data Visualization
  • Interactive AI
  • Human-Building Interaction

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Soft and Cognitive Computing

  • Fuzzy Sets and Systems
  • Computational Intelligence
  • Soft, Perceptual and Granular Computing

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Software Engineering

  • Privacy-preserving IoT services using trusted execution environments
  • Leveraging distributed consensus models to improve trust in IoT

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