My primary research interests focus on applied data science and predictive analytics, primarily in the context of environmental science and river forecasting. Originally trained as an exploration geophysicist with a solid grounding in both digital signal processing and bottom-up process physics, my interests quickly turned to analysis and modeling of complex systems in hydrology, cryospheric science, and climate, and subsequently to data analytics in general. I am particularly intrigued by integration of process physics and experiential expert knowledge into data-driven analysis and prediction algorithms, especially AI, which I have been working with for over 15 years. My work emphasizes bridging the gap between theory and practice, and typically involves building and managing multi-disciplinary, multi-institutional, and frequently international teams.
