At Dell Technologies, I lead predictive modeling for global supply chain demand forecasting, reducing forecast error by 15% and saving $8M in inventory costs. I also architect AWS SageMaker and Kubernetes MLOps pipelines for operational telemetry.
At Southwest Research Institute, I developed numerical solvers for fluid dynamics simulations, improving runtime by 40%, and built aerospace telemetry models that reached 95% anomaly detection recall while reducing false alarms by 30%. I also collaborated with PhD mathematicians on a stochastic PDEs paper published in AIAA Journal.
Earlier, at National Instruments, I implemented linear algebra routines for LabVIEW’s math toolkit and optimized sparse matrix operations. My research at Rice University and Texas Advanced Computing Center included persistent homology, climate-model manifold learning, and parallel matrix multiplication kernels.
