Timbo is a Researcher and PI at UCSB where he works on improving real time forecasts and climatological understanding of surface water supplies stored as ice and seasonal snow. His research is on both the physical science of measuring water from space and the linkages between land surface hydrology and how humans manage their demand for water with a focused curiosity on where improved hydrologic knowledge can lead to different decisions in water management. His PhD focused on the long-standing and difficult classification problem of snow and cloud discrimination in multispectral satellite data and the impact of runoff forecast uncertainty on reservoir management. His research is now focused on fusing data from modeling and measuring the physical properties of water with machine learning and computer vision techniques to accurately identify snow, ice, and clouds in multispectral imagery. In addition to research, Timbo received the 2019 Bren School Student Teacher Award for Excellence in Teaching and the campus wide 2017 UCSB Graduate Student Association Excellence in Teaching Award in Science, Technology, Engineering, and Mathematics.