Planetary Health, Epidemiology & Environmental Systems

This research area drives interdisciplinary studies investigating the complex interdependencies between climate change, the natural environment, and human health. Our team conducts systemic risk assessments encompassing the transmission dynamics of infectious and zoonotic diseases, the impacts of extreme weather events, and the broader effects of environmental stressors on human populations.

We leverage advanced epidemiological modeling, spatial analytics, and high-resolution atmospheric and environmental forecasting simulations. Rooted in the One Health and Planetary Health frameworks, this research is designed to provide actionable insights that support public health decision-making, climate adaptation strategies, and crisis management.

Computational Social Science & Public Policy Analytics

This research area focuses on developing computational methods to analyze large-scale social and economic processes leveraging big data, including administrative records, survey data, and digital traces of user activity. We fuse quantitative research methodologies with advanced data analytics, machine learning, and natural language processing (NLP) to provide rigorous descriptions of complex social phenomena.

Our research centers on data quality, robust measurement design, and statistical inference under uncertainty—directly supporting the design and evaluation of public policies, as well as the analysis of institutional and market behavior.

AI, Data Science & Decision Systems

This research area focuses on the applications of artificial intelligence and data science in high-stakes decision-making systems carrying profound social, legal, and institutional implications. Special emphasis is placed on model explainability and trustworthiness, algorithmic auditing, and forecasting under uncertainty.

The team also investigates the integration of AI within highly regulated frameworks, including healthcare, law, and critical infrastructure. A core component of this area involves deploying decision-making models within large-scale operational systems, such as aviation.

Mathematical Modeling & Biological Simulations

This research area focuses on developing mathematical and computational models of biological and biomedical processes, encompassing cellular dynamics, tissue growth and regeneration, as well as oncology and immunological mechanisms. Our studies leverage multiscale modeling, differential equations, agent-based simulations, and Bayesian inference and calibration methods.

The core focus centers on deciphering biological mechanisms, uncertainty quantification (UQ), and rigorous model validation, serving as a methodological cornerstone for biomedical and clinical research driven by ICM.