معرفی
Xinxuan Zhang serves as an Assistant Research Professor at the University of Connecticut within the School of Civil and Environmental Engineering under the College of Engineering. Her research bridges environmental engineering, climate science, and infrastructure resilience through advanced data-driven methodologies for modeling critical systems under extreme conditions.
Dr. Zhang earned her Ph.D. in Engineering from the University of Connecticut in 2018. Her academic journey has focused on developing computational frameworks that integrate physical models with observational data to address climate-infrastructure challenges.
Her research program centers on two interconnected domains: atmospheric-land surface modeling and infrastructure resilience. She pioneers data assimilation techniques for land surface models, particularly integrating remotely sensed leaf area index and soil moisture to refine hydrological predictions. Concurrently, she develops machine learning frameworks for power outage forecasting during weather events, examining infrastructure vulnerabilities under climate change. Her hydrology work spans baseflow separation, water quality estimation, and precipitation analysis in complex terrain, often employing remote sensing and NWP model integration. This interdisciplinary approach combines environmental physics with data science to quantify climate risks to critical infrastructure.
Analysis of her publication trajectory (2018-2024) reveals evolving research priorities. Early work focused on precipitation estimation in mountainous regions and debris flow prediction, while recent publications emphasize hybrid mechanistic-machine learning models for grid resilience and advanced data assimilation techniques. A consistent thread is the integration of remote sensing data with physical models to improve predictive capabilities, with increasing emphasis on climate change impacts on infrastructure systems. Her most cited work centers on LAI assimilation effects on global water cycles and power outage prediction frameworks.
Dr. Zhang maintains active research collaborations within UConn's environmental engineering ecosystem, likely engaging with institutes like the Connecticut Transportation Institute and Eversource Energy Center. Her work demonstrates strong methodological expertise in numerical modeling, remote sensing, and machine learning applied to real-world climate resilience challenges.
