As climate prediction is one of the most computationally complex problems in science, my primary research interest lies in climate computing at the intersection of Earth system modeling, generative machine learning, and data assimilation. I am particularly interested in adapting models to leverage new computational techniques and exploring the limits of predictability of climate simulations, along with their development and biases. Currently, I am investigating machine learning techniques to accelerate high-resolution climate modeling and maximize the information extracted from physics-based simulations. I am a member of the Predictability of Weather and Climate Group in AOPP, under the supervision of Tim Palmer, Antje Weisheimer, and Yee Whye Teh.
Prior to joining the Intelligent Earth CDT, I graduated with a B.S.E. in Computer Science, along with certificates in Applied & Computational Mathematics and Statistics & Machine Learning, from Princeton University. During my undergraduate studies, I took climate physics courses in Princeton's Program in Atmospheric and Oceanic Sciences and interned at Oak Ridge National Laboratory, NOAA's National Weather Service, Scripps Institution of Oceanography, the Max Planck Institute for Meteorology, and the National Center for Atmospheric Research.
