Danielle Robinson
Principal Scientist at Amazon Web Services (AWS)
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolDanielle Robinson is a Senior Applied Scientist at AWS in Palo Alto with a PhD in Computational and Mathematical Engineering from Stanford and six years of industry experience building high-performance algorithms for ML, data science, and computational fluid dynamics. She combines deep numerical analysis and parallel computing expertise—CUDA, MPI, OpenMP—with production-grade software skills in C/C++, Python, R and MATLAB, honed at NVIDIA, Sandia, Lawrence Berkeley, and Berkeley Lab. At AWS she translates research-grade methods into scalable services and has contributed to notable open-source projects like GluonTS, adding probabilistic models and testable dataset utilities. Her background in teaching advanced MATLAB to thousands and mentoring students underscores a knack for distilling complex numerical ideas into practical tools. Unusually, her work spans both structure-preserving reduced-order models for fluid dynamics and GPU-optimized sparse linear algebra, bridging theoretical rigor and high-performance implementation.
7 years of coding experience
8 years of employment as a software developer
Bachelor of Arts - BA, Applied Mathematics, Bachelor of Arts - BA, Applied Mathematics at University of California, BerkeleyDoctor of Philosophy - PhD, Computational and Applied Mathematics, Doctor of Philosophy - PhD, Computational and Applied Mathematics at Stanford University
Github Skills (10)
forecasting10
mxnet10
machine-learning10
deeplearning-ai10
forecast10
time-series10
deep-learning10
python10
data-science10
artificial-intelligence9
Programming languages (4)
Github contributions (5)
awslabs/gluonts
Aug 2019 - Jan 2023
Probabilistic time series modeling in Python
Role in this project:
Data Scientist
Data ScientistContributions:83 reviews, 30 commits, 73 PRs in 3 years 5 months
Contributions summary:Danielle contributed to the development and improvement of the GluonTS library, focusing on time series modeling and analysis. Their work included reformatting mathematical equations within the `GaussianProcess` class and implementing and documenting a `DeepFactorEstimator` model. Further contributions were made to the artificial dataset module, including the addition of field names and the development of tests and examples.
pythontime-seriesdeep-learningforecastingneural-networks
dcmaddix/dcmaddix.github.io
Feb 2023 - Jul 2026
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
Contributions:537 pushes in 3 years 5 months
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