Bio

Negin Hayatbini joined CW3E as a Data Science Postdoctoral Researcher in May 2020. She earned her Ph.D. in early 2020 from the University of California, Irvine, Department of Civil and Environmental Engineering under the supervision of Distinguished Prof. Soroosh Sorooshain and Prof. Kuolin Hsu.

Her Ph.D. research was focused primarily on the application of Deep Learning and Computer Vision techniques to improve and develop state-of-the-art precipitation estimation and forecast algorithms. She leveraged the big-data available from recent developments in satellite technologies, along with advancements in Machine Learning (ML) algorithms and computational power, to develop analytical and data-driven tools for precipitation characterization. For her doctoral dissertation she developed an effective cloud segmentation and tracking algorithm and designed a feature extractor to train a Neural Network for more accurate quantitative precipitation estimation from multiple sources of information.

In the course of her Ph.D., Negin was awarded a graduate research fellowship at NASA Jet Propulsion Laboratory (JPL) and she has also spent some time at Bay Area Environmental Research Institute/NASA Ames as an intern working on the development of advanced Deep Learning frameworks for near-real-time precipitation retrieval from multispectral satellite imagery.

At CW3E, Negin will focus on designing novel approaches based on Machine Learning algorithms to improve the prediction of extreme weather and water events, with the potential to significantly benefit operations of water reservoirs in California and Western States. Her work will assist the acceleration of efforts in Forecast Informed Reservoir Operations (FIRO) and the AR Program.