My research interests focus on applied data science and predictive analytics in the context of environmental science and hydrometeorological forecasting. Originally trained as a geophysicist with a solid grounding in both digital signal processing and bottom-up process physics, my attention quickly turned to analysis and modeling of complex systems in hydrology, cryospheric science, and climate, and subsequently to data analytics and machine learning in general, which I have been working with for close to two decades. I am particularly intrigued by the integration of both underlying process physics and experiential human expert knowledge into data-driven quantitative analysis and prediction algorithms, particularly AI, and my work emphasizes bridging the gap between theory and practice. Most of my projects involve building and managing multi-disciplinary, multi-institutional, and frequently international teams.
