Bio

I am interested in the intersection of machine learning and scientific computing for simulating physical systems. I graduated from the University of California, Berkeley with highest honors in Applied Mathematics in 2012. I then graduated from the Institute of Computational and Mathematical Engineering (ICME) at Stanford University with my Masters of Science in 2015 and with my PhD in Computational and Mathematical Engineering in 2018. In my PhD research, I developed novel finite volume averaged-based methods for nonlinear porous media flow. I was advised by Professor Margot Gerritsen. After graduating in 2018, I joined AWS AI Labs at Amazon Web Services as an Applied Scientist, where I worked on developing and researching deep learning models for probabilistic time series forecasting. In 2021, I was promoted to Senior Applied Scientist and have been working on the DeepEarth team, researching physics-constrained machine learning models.

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