People of PCHES: Yifan Luo

Yifan Luo will be entering a third year as a PhD student with Dr. Vivek Srikrishnan’s research group at Cornell University. Yifan brings a blend of computational expertise, interdisciplinary curiosity, and personal resilience to the PCHES project. Trained originally in forestry and biotechnology, she now spearheads research into the drivers of water shortages across the Western United States, combining detailed hydrologic modeling with cutting-edge machine learning techniques to inform smarter water use strategies.

Yifan’s current project leverages the UNH Water Balance Model (WBM) to quantify contributions to water shortages from natural (temperature and precipitation) and human drivers (population density, irrigation efficiency, cropland fraction, and sectoral water-use intensities). Recognizing that running comprehensive sensitivity analyses on a process-based model like WBM would be impractical due to computational constraints, she developed a deep learning emulator that captures WBM behavior with high fidelity and far greater efficiency. To move beyond prediction and toward explanation, Yifan applies multiple interpretable machine learning techniques to quantify each input’s contribution to water shortages. A key innovation of her work is combining these results into consensus-strength maps, which highlight where different analytical methods agree on the main drivers. This approach offers a clearer and more reliable picture for decision-makers, showing where findings are more or less certain.

Yifan credits the PCHES project’s collaborative nature for broadening her perspective. Attending project workshops and all-hands meetings exposed her to wide range of expertise in public policy, economics, and atmospheric science—fields she now minors in alongside her hydrology work. This interdisciplinary framework, she believes, is essential to capturing the complex socio-hydrologic systems that impact water availability.

Raised in Shanghai (China), Yifan’s academic journey began at Beijing Forestry University, where she studied biotechnology before transferring to the University of British Columbia in Canada to study forestry. It was there that she first encountered fieldwork—collecting tree cores to determine age and assessing post-fire regeneration—and that experience instilled in her a passion for natural resource science. When the coronavirus pandemic disrupted on-site research, she pivoted to computational work during her master’s at the University of Michigan, modeling the spread of 1,4-dioxane chemical plume in groundwater of Ann Arbor and refining the Great Lakes water balance model. Those projects honed her coding skills and statistical rigor—expertise she brought to Cornell after being invited to join PCHES as a PhD student.

Mentorship has played a pivotal role in shaping Yifan’s journey. She has been profoundly influenced by professors who combine insight, patience, and a genuine enthusiasm for their work. These are qualities she hopes to carry forward in her own path as a researcher. Looking to the future, Yifan remains open to various career trajectories—academic, national laboratories, or industry—so long as she continues conducting research. No matter where she ends up, Yifan plans to keep combining advanced computational tools with practical problem-solving. This approach is a natural progression from her foundational experience in forestry to her current focus on water systems.

Outside the lab, Yifan balances her analytical work with a variety of personal pursuits. She enjoys video and board games, and volunteers at film festivals. An avid urban explorer, she often wanders Ithaca’s streets in fair weather. During the long winter months—still an adjustment from her native subtropical Shanghai—she has taken up skiing and snowboarding at nearby resorts to make the dark season feel brighter. Despite the challenges of northern winters, Yifan appreciates Ithaca’s welcoming atmosphere, though she dreams of residing in California’s Bay Area one day, where the year round sunshine would allow her to play tennis and enjoy the outdoors every day.