Greater Seattle Area
Machine learning scientist experienced with traditional and deep ML architectures, predictive and generative models, and uncertainty quantification. Currently an Applied Scientist at Amazon AGI working on LLM agents and GenAI at scale. Also have experience with code development, cheminformatics, and developing ML models to both predict chemical properties and generate structures. I enjoy collaborating with others to drive scientific and business impact. https://scholar.google.com/citations?hl=en&user=qg2LmbgAAAAJ
Generative AI – LLMs for Alexa/AIDo
• Designed virtual libraries using generative modeling and reinforcement learning. • Delivered models to predict ADME properties and accelerate DMTA cycles. • Evaluated uncertainty metrics to prioritize compounds during active learning iterations. • Let efforts to democratize GenAI tools across modeling department.
Thesis Title: Enabling Accurate and High-Throughput Kinetics Predictions via Message Passing Neural Networks Sample projects: • Developed and tuned a graph convolutional network (GCN) to accurately predict reaction barrier heights • Created new kinetics dataset with high-quality CCSD(T)-F12 calculations • Created new Python software to automate kinetic model construction and refinement • Substantially improved estimates for thermodynamic and kinetic parameters within RMG-database • Created kinetic model to understand pyrolysis of a surrogate mixture of heavy oil
• Trained logistic regression and LightGBM models to predict income • Used SHAP values to analyze how unfairness mitigation impacts model structure • Contributed to the open source fairlearn repo
• Led team in training a linear latent variable regression model to predict species concentration from spectra • Analyzed the model’s physical interpretability and created internal tooling for reproducibility