Research
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.
- Super Resolution
- Quantitative Single-Cell Biology
- Environmental Science
- Medical Data Analsysis
- Remote Sensing
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AI techniques to study and enhance various aspects of human culture and history, such as literature, music, language, historical documents and other cultural heritage.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
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
Ingo Scholtes
Machine Learning for
Complex Networks
Dominic Grün
Computational Biology of Spatial Biomedical Systems
Carlo D'Eramo
Reinforcement Learning and Computational Decision Making
Christof Weiß
Computational Humanities
Goran Glavaš
Natural Language
Processing
Gunther Gust
Process and IT integration for
AI in the company
Hannes Taubenböck
Global Urbanization and
Remote Sensing
Radu Timofte
Computer Vision
Andreas Hotho
Data Science
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
N.N.
Theory of
Machine Learning
N.N.
Mathematics of
Machine Learning
N.N.
Pattern Recognition
N.N.
Artificial Intelligence for the Molecular Sciences
Further appointment procedures are in preparation.
