Responsible for solving quantum calibration problems using deep learning, computer vision, and bayesian methods. One of the lead developers of a collaboration achieving first fully automated calibration of semiconducting qubits. Designed orchestration solutions for complex calibration pipelines for the National Physical Laboratory and others.

Tech Lead for team of 5 data scientists and software engineers across NA and Europe. Refactored deep learning training pipeline with Ray-based hyperparameter tuning and created Dash visualizations for model evaluation.

Partnered with business stakeholders to translate objectives into data-driven solutions. Built metallurgically-aligned model predicting copper concentrate quality, reducing out-of-spec plant operation by 30% for a major NA client.

Led development of computer vision solutions. Refactored image validation API for faster performance and optimized GPU utilization by 15x, significantly reducing infrastructure costs.

Implemented SSD-based model for building detection from satellite imagery. Delivered time-sensitive business risk analysis for major cryptocurrency industry player and contributed to stealth-mode startup whitepaper.

Developed intelligent prioritization system for Chile’s largest work accident prevention company, reducing accidents by 3% and increasing expert productivity by 13%. Built predictive models for mining operations.

Developed computer vision solutions and consulted on ML infrastructure decisions. Trained and deployed ResNet101-based multilabel classifier on Kubernetes for product categorization across million+ image dataset.

Developed business model and go-to-market strategy for agricultural technology startup. Successfully pitched to investors and secured selection into Wadhwani Foundation’s Venture FastTrack program.

Delivered tutorials and graded assignments for advanced mathematics courses in algebra and geometry. Taught undergraduate courses including Elliptic Curves, Geometry of Surfaces, and Commutative Algebra.