Berlinghieri, Renato, Brian L. Trippe, David R. Burt, Ryan Giordano, Kaushik Srinivasan, Tamay Özgökmen, Junfei Xia, and Tamara Broderick. "Gaussian processes at the Helm (holtz): A more fluid model for ocean currents." arXiv preprint arXiv:2302.10364 (2023).  https://doi.org/10.48550/arXiv.2302.10364 

Lu, Yuanming, Donald L. DeAngelis, Junfei Xia, and Jiang Jiang. "Modeling the impact of invasive species litter on conditions affecting its spread and potential regime shift." Ecological Modelling 468 (2022): 109962.  https://doi.org/10.1016/j.ecolmodel.2022.109962 

Zhang, Wei, Ben Kirtman, Leo Siqueira, Amy Clement, and Junfei Xia. "Understanding the signal-to-noise paradox in decadal climate predictability from CMIP5 and an eddying global coupled model." Climate dynamics 56 (2021): 2895-2913.  https://doi.org/10.1007/s00382-020-05621-8

Junfei Xia, Wei Zhang, Alesia C. Ferguson, Kristina D. Mena, Tamay M. Özgökmen, Helena M. Solo-Gabriele, A novel method to evaluate chemical concentrations in muddy and sandy coastal regions before and after oil exposures, Environmental Pollution, Volume 269, 2021, 116102, ISSN 0269-7491. https://doi.org/10.1016/j.envpol.2020.116102.

Junfei Xia, Wei Zhang, Alesia C. Ferguson, Kristina D. Mena, Tamay M. Özgökmen, Helena M. Solo-Gabriele, Use of chemical concentration changes in coastal sediments to compute oil exposure dates, Environmental Pollution, Volume 259, 2020, 113858, ISSN 0269-7491. https://doi.org/10.1016/j.envpol.2019.113858.

Research Interest

• Sea Surface Dynamics & Submesoscale Processes
  Eddies, turbulence, and small‐scale ocean features; development of ML‐based methods for reconstructing velocity fields from drifters; physics-informed ML for ocean current modeling.

• Machine Learning & Deep Learning in Oceanography
  Vision Transformers, CNNs, LSTMs, Bayesian learning, and Gaussian Process Regression applied to trajectory data, velocity field interpolation, and SAR image classification (98% accuracy in small-scale ocean phenomena detection).

• Remote Sensing & Numerical Modeling
  Deep learning–enhanced classification of SAR and satellite imagery; integration of ML with numerical models for submesoscale prediction and ENSO-related sea surface temperature change forecasting.

• Coastal Dynamics & Geomorphology
  Wave shoaling, breaking, and refraction; sandy vs. muddy shoreline evolution; sediment response to oil spill exposure. Early work included a funded project on sand ridge evolution along the Jiangsu Plain Coast (Abandoned Yellow River Delta).

• Marine Energy & Climate Change Impacts
  Fluid modeling for energy applications; long-term habitat and ecosystem forecasting via Individual Based Models (IBMs) under climate change scenarios.

• Interdisciplinary Research
  Bridging physical oceanography, ecology, and machine learning; supporting ecologists in forecasting habitat evolution, and climate scientists in understanding decadal predictability and ENSO impacts.