Danielle Maddix Robinson
Danielle Maddix Robinson is a Senior Applied Scientist in the Machine Learning Forecasting Group within AWS AI Labs. She graduated with her PhD in Computational and Mathematical Engineering from the Institute of Computational and Mathematical Engineering (ICME) at Stanford University. She was advised by Professor Margot Gerritsen and developed robust numerical methods to remove spurious temporal oscillations in the degenerate Generalized Porous Medium Equation. She is passionate about the underlying numerical analysis, linear algebra and optimization methods behind numerical PDEs and applying these techniques to deep learning. She joined AWS in 2018 shortly after graduating, and has been working on developing statistical and deep learning models for time series forecasting. In this past year, she has been leading the research initiative on developing models for physics-constrained machine learning for scientific computing on the DeepEarth team. In particular, she has researched how to apply ideas from numerical methods, e.g., finite volume schemes, to improve the accuracy of black-box ML models for ODEs and PDEs with applications to epidemiology, aerodynamics, ocean and climate models.