Applied machine learning in medical imaging: prostate and breast cancer detection, personal diagnosis from clinical and image (MRI) data, improve generalization of models. Applied machine learning in earth sciences: data assimilation in ocean models, nowcasting (precipitation and hail), short-term forecast of air pollution, loop current and eddy detection and analysis. Climate change: predicting future distribution of invasive insect pests considering climate change projections. Scientific Machine Learning: Physics Informed Neural Networks (PINNs) to improve parameterizations in Ocean Models, etc.
Education:
Ph.D., Computation Science, Florida State University, 2015 M.S., Computation Science, Florida State University, 2013
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