Deutsch Intern
Center for Artificial Intelligence and Data Science


CAIDAS is JMU's interdisciplinary research center in the field of Artificial Intelligence and Data Science, where research questions in Machine Learning, Data Science, Image and Text Analysis, AI Systems, Ethics/Legal/Societal Acceptance, and Economy and Transfer are to be answered in particular within the four central application pillars: AI for Life Science, Human-Centered AI, AI in Digital Humanities, Economics/Law and AI. The methods for these applications are also researched at CAIDAS in the underlying area Foundations of AI and Data Science.

AI for Life Sciences

Application and development of AI techniques to improve research and understanding in the field of life sciences, including healthcare, biology, and geography.

  • Ecosystems
  • Super Resolution
  • Quantitative Single-Cell Biology
  • Environmental Science
  • Medical Data Analsysis


Human-Centered AI

Focus on developing AI systems that effectively collaborate with humans, including studies on human-AI interaction, explainability of AI decisions, and AI integration in society.

  • Human-AI Interaction
  • Computational Social Sciences
  • AI in Software Engineering
  • Democratizing Language Technology
  • Recommender Systems


AI in Digital Humanities

Application of AI techniques to study and enhance various aspects of human culture and history, such as literature, music, language, historical documents and other cultural heritage.

  • Computational Literary Studies
  • AI in Musicology
  • Geolingual Studies
  • Multilingual NLP
  • Digitization of Cultural Heritage


Economics/Law and AI

Application of AI in business, industry and law with a focus on improving efficiency, sustainability and decision-making.

  • AI Adoption in Organizations
  • Smart Cities &  Urban Mobility
  • Smart Industry & Logistics
  • Future Energy Systems
  • AI in Law
  • Fraud Detection
  • Remote Sensing


Foundations of AI and Data Science

CAIDAS also conducts research on the fundamentals on Machine Learning and Data Science, developing methods and techniques that can be used in all of the application pillars. The main fundamental research areas are Deep Learning, Representation Learning, Reinforcement Learning, Statistical Learning, Machine Learning for Complex Networks, Computer Vision, Natural Language Processing, Pattern Recognition.


Principal Investigators

Radu Timofte

Computer Vision

Andreas Hotho

Data Science

Dominic Grün

Computational Biology of Spatial Biomedical Systems

Christof Weiß

Computational Humanities

Goran Glavaš

Natural Language

Gunther Gust

Business Informatics & Artificial
Intelligence in the Company

Hannes Taubenböck

Global Urbanization and
Remote Sensing

Ingo Scholtes

Machine Learning for
Complex Networks

Carlo D'Eramo

Reinforcement Learning and Computational Decision Making

Frank Puppe

Artificial Intelligence and Knowledge Systems

Marc Latoschik

Human-Computer Interaction

Fotis Jannidis

Computational Philology and Modern German Literary History

Carolin Biewer

English Linguistics

Christoph Flath

Information Management

Frédéric Thiesse

Business Informatics and Systems Development

Eric Hilgendorf

Information and Computer Science Law

Leon Bungert

Mathematics of
Machine Learning

Damien Garreau

Theory of
Machine Learning

Carolin Wienrich

Psychology of Intelligent Interactive Systems

Katharina Breininger

Pattern Recognition


AI in Computational and Theoretical Biology


Artificial Intelligence
and Data Science


Applied Super-Resolution


Digital Media Processing


Artificial Intelligence for the Molecular Sciences


Quantum Dynamics and Artificial Intelligence

open positions

Further appointment procedures are in preparation.