Research
RESEARCH
Statistical AI and Time Series Forecasting
I work on application-driven theory, meaning that most of my research focuses on designing theoretically principled statistical and machine-learning models that directly address real-world problems. Much of this work is guided by the belief that effective algorithms often emerge from simple, aesthetically elegant ideas.
Research Themes
Spatiotemporal Forecasting
Forecasting complex spatial and temporal processes in climate, epidemiology, and environmental systems.
Health AI
Statistical and AI methods for digital health, epidemiology, and healthcare decision-making.
Statistical Machine Learning
Statistically principled learning methods for inference, prediction, and uncertainty quantification.
Generative AI
Generative models and modern AI methods for scientific and application-driven problems.