- Doctoral researcher and hydrology specialist at the University of Michigan’s School for Environment and Sustainability (SEAS), working with Prof. Drew Gronewold. Brings a background in civil and environmental engineering and nine years of combined research and professional experience in hydrology, water resources, and environmental modeling.

- Expertise in machine learning and deep learning for hydrological systems, including time-series analysis, streamflow forecasting, reservoir release simulation, and dam operation modeling. Research experience includes developing and evaluating predictive models across 441 reservoirs in the contiguous United States.

- Author or coauthor of seven peer-reviewed journal articles and an active reviewer for international scientific journals, with 184 verified peer reviews. Reviewing experience includes Water Resources Research, Journal of Hydrology, Hydrology and Earth System Sciences, Journal of Hydroinformatics, Environmental Modelling & Software, Science of the Total Environment, and Journal of Water Process Engineering.

- Committed to integrating artificial intelligence with hydrological science to improve forecasting, reservoir operations, climate resilience, and sustainable water resources management. Values scientific rigor, continuous learning, and collaboration across disciplines and cultural contexts, with a focus on translating research advances into practical tools for environmental decision-making.