Ellianna Abrahams

Scientific Computer Vision, Uncertainty Quantification, Cryosphere Sciences

I am a Postdoctoral Fellow at Stanford Data Science, where I am hosted by Prof. Ching-Yao Lai and Prof. Dustin Schroeder. My work focuses on building scientifically contextualized artificial intelligence to tackle challenges with real-world implications, particularly those that concern the impacts of climate change on Earth’s polar regions. Specifically, I use this lens to investigate machine approaches that provide greater scientific interpretability through the integration of prior domain knowledge and achieve more trustworthy and meaningful model outcomes through the inclusion of uncertainty quantification. 

Prior to joining Stanford, I obtained a B.Sc. in Physics  from CUNY City College and was subsequently a Helen Fellow in Computational Sciences at the American Museum of Natural History in NYC. After this, I completed an M.A. in Astro/Physics, an M.A. in Statistics, and a Ph.D. in Astro/Physics from UC Berkeley, where my research focused on scientific computer vision. I am also passionate about algorithmic fairness, few shot learning, and methods for the transparent use of data and software.