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AI techniques to study and enhance various aspects of human culture and history, such as literature, music, language, historical documents and other cultural heritage.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