Nicoleta Cristea
Research Assistant Professor
Civil & Environmental Engineering
Pronouns: she/her
- cristn@uw.edu
- (206) 356-0679
- WIL 266
- Faculty Website
Biography
Dr. Nicoleta Cristea is a Research Assistant Professor in the Department of Civil and Environmental Engineering and a Senior Data Science Fellow at the University of Washington eScience Institute. Cristea’s work spans various areas of freshwater research, including snow, evapotranspiration, surface-subsurface processes, and stream heating processes in the context of climate change. Many aspects of her work relate to other fields (e.g., ecology or resource management), as freshwater is a linking discipline essential for people and nature. She is interested in continuing this interdisciplinary approach while leveraging modern data science methods and workflows in collaboration with academic researchers and local communities. As part of the University of Washington eScience Institute, Dr. Cristea hosts the weekly Data Science seminar and co-organizes training and outreach events to increase the adoption of data science tools and methods in geosciences.
Education
- Ph.D., University of Washington, Civil and Environmental Engineering
- M.S. University of Washington, Civil and Environmental Engineering
- B.S. University Politehnica Bucharest, Romania
Current projects
What's in a pixel? Snow water equivalent and subpixel variability at multiple spatial resolutions in mountainous terrain
This project examines models' performances to simulate snow water equivalent at different spatial scales, paying particular attention to their abilities to represent subgrid variability, spatial snow patterns in complex terrain, and snow in forested areas.
GeoSMART - GeoScience MAchine Learning Resources and Training
Through this project, we develop an educational framework to support the research workforce in tackling fundamental geoscience challenges with Machine Learning (ML) tools. We showcase discipline-specific curricula and ML workflows and organize communities of practice that can sustain the future growth of ML cyber training opportunities. Materials available at https://geo-smart.github.io/.
Modeling future streamflows in the Skagit River Basin, WA
This work supports the evaluation of future hydrology under various operational scenarios of the Skagit Hydroelectric Project and informs long-term future water and energy planning.