Qianqian Liu

Dr. Qianqian Liu is an Associate Professor in the Department of Physics and Physical Oceanography, as well as the Center for Marine Science, at the University of North Carolina Wilmington. She graduated from the University of Rhode Island with a PhD degree in Physical Oceanography in 2015. Before joining the academic community in North Carolina, Dr. Liu refined her skills as a postdoctoral fellow at the NOAA Great Lakes Environmental Research Laboratory, which is affiliated with the University of Michigan and Grand Valley State University. Her research is focused on coastal and estuarine hydrodynamics and biophysical processes (particularly how the physical processes influence biological processes) using numerical and machine learning modeling.

Education

Ph.D. in Oceanography, University of Rhode Island
M.S. in Oceanography, University of Rhode Island
B.S. in Marine Science, Ocean University of China

Specialization in Teaching

Course Taught:
PHY475 Physical Oceanography
PHY575 Physical Oceanography
PHY101-00X Elementary College Physics
PHY101-2XX Elementary College Physics Lab
PHY102-2XX Elementary College Physics II Lab

Research Interests

My research focuses on developing models to enhance our understanding and management of complex aquatic systems. My research addresses pressing challenges, including hypoxia (low dissolved oxygen), harmful algal blooms (HABs), and compound flooding in freshwater and coastal systems—issues that are becoming increasingly common and garnering public and policy attention.
To tackle these problems, I develop advanced numerical and machine learning models that simulate hydrodynamic and biogeochemical processes in coastal systems. These tools help reveal how anthropogenic and natural stressors interact to affect ecosystem health and inform strategies for resilience and restoration.
Specific areas of interest include:
Short-term forecast of harmful algal blooms using particle-tracking models
Compound flooding forecasting
Biophysical interactions in aquatic systems
Water quality forecasting through integrated biophysical and machine learning approaches

Professional Service

o Served in review panels for DOE (2023) and NSF (2024)
o Served as a guest editor for the journal “Water: Special Issue: Coastal Water Quality Modelling” in 2024
o Reviewer for the journals of: Journal of Environmental Management, Frontier in Marine Science Estuarine, Coastal and Shelf Science, Science of the Total Environment, Journal of Geophysical Research – Oceans, Environmental Science and Pollution Research, Progress in Oceanography, Toxins, Natural Hazards etc.
o Served as a proposal reviewer for DOE, NSF, and Maryland Sea Grant
o Chaired sessions in the 2024 Ocean Science Meeting in New Orleans titled “Application of Deep Learning for Ocean Data Science: Modeling, Prediction, Data
Analysis, Signal and Image Processing” with two oral sessions and one poster session
o Co-chaired a session (Success Story Session as a scriber) in the Cyberinfrastrure PI conferences in Houston, Texas in September 2023.