Our research is organized into six groups, each addressing fundamental questions at the intersection of theory and application.
The AI & Computational Systems Group focuses on the development of intelligent models, computational methods, and scalable learning systems. Its work includes machine learning, deep learning, optimization, simulation, and the study of complex adaptive systems. The group aims to build reliable and research-driven AI frameworks that can support both theoretical exploration and real-world applications.
The Medical & Health Sciences Group focuses on research at the intersection of healthcare, clinical insight, and computational science. Its role is to help identify meaningful medical problems, support health-related research, and ensure that projects involving medical data or AI remain clinically relevant and scientifically grounded. The group acts as a bridge between technical research and real-world healthcare contexts.
The Data Science & Complex Systems Group studies data-rich and interconnected systems through statistical analysis, modeling, and computational research. Its work includes predictive analytics, system dynamics, network analysis, and large-scale data methodologies. The group supports interdisciplinary projects by providing rigorous analytical frameworks for understanding complex biological, technological, and social systems.
Many of our most significant advances emerge from collaboration across research groups.
Exploring the relationship between signal detection and absence, investigating how information can be encoded in what is not present.
