Enthusiastic, curious, and with multidisciplinary interests. I want to help bridging the gap between science, engineering and the society.
I currently work as VP of Weather Forecasting @ Arable Labs. My primary duties are to initiate and lead data science and algorithm development at the company. My knowledge of hydrology, meteorology, and agricultural science, as well as machine learning and statistical techniques, are invaluable in turning raw numbers into insights for the customers. In the near term, my specific responsibilities include meteorological downscaling and statisical crop forecasting
Previously I worked at NOAA's Geophysical Fluid Dynamics Laboratory for the recently created South Central Climate Science Center. I am interested in challenging the empirical statistical downscaling (ESD) assumptions (e.g. time-invariance, strong relationship), and in communicating the ESD advantages and limitations to the users.
My research also involves the use novel machine learning methods, like Support Vector Machines (SVM) and Bayesian Neural Networks (BNN) to downscale the coarse resolution Numerical Weather Model outputs. My research interests include statistical downscaling, machine learning methods, climate extremes, hydrology and forecasting at different spatial and temporal scales.
Before moving to Princeton, I worked at UBC' Climate Prediction Group, an internationally renowned research group specializing in the prediction of seasonal climate variability using machine learning methods. The group is directed by Prof. William W. Hsieh.