I'm an independent consultant with an MS in Applied Mathematics and six years of experience delivering rigorous, production-ready solutions across scientific computing, data engineering, and full-stack development. I work best on complex problems that require both theoretical depth and real-world implementation — from first-principles modeling to cloud deployment.
Bayesian inference, dynamical systems, genetic algorithms, multi-objective optimization, Monte Carlo simulation, and uncertainty quantification. I translate complex, ambiguous problems into rigorous computational models with honest confidence bounds.
Full-stack development in Python, Rust, and TypeScript. Production APIs, microservices, and offline-first web applications. Cloud infrastructure on AWS and GCP managed with Terraform and Kubernetes, with CI/CD automation and observability built in.
Pipeline architecture using Hamilton DAG and Modal Labs, statistical modeling with uncertainty quantification, and large-scale data processing. I turn research notebooks and proof-of-concepts into maintainable production systems.
6 months · 2025
Led technical architecture for a platform that automatically designs fixed-wing drones — selecting optimal components from catalogs and tuning airframe geometry to meet user-specified mission requirements. Powered by a custom evolutionary search algorithm (genetic algorithm); deployed on AWS cloud infrastructure managed as code. Coordinated a distributed engineering team across multiple repositories.
4 months · 2024
Transformed a 3,000-line research script into a production system estimating how many machine learning experts work at each of 400+ organizations, inferred from 250,000+ employee profiles. Redesigned the statistical model to correctly account for correlated AI classifiers and produce honest, statistically valid confidence intervals. Integrated cloud-based distributed computation for large-scale sampling.
2 months · 2025
3D computer modeling of eggplants in a 30-meter solar greenhouse with 4,032 plants. Simulated sunlight distribution through the canopy (Monte Carlo ray tracing), used intelligent parameter search (Bayesian optimization) to tune 8 architectural parameters, and applied numerical integration to average results over the daily solar cycle — achieving a 29% improvement in photosynthesis efficiency.
2 months · 2022–2023
Mathematical framework for allocating advertising budgets across 18+ channels to maximize campaign reach. Built a statistical model for how frequently people engage with each channel, with a probabilistic correction for imperfect demographic targeting, and formulated the whole problem as a mathematical optimization. Authored a 14-page specification derived from first principles.
2023
High-performance portfolio rebalancing library written in Rust, extending the classical Markowitz mean-variance framework — the standard mathematical basis for balancing risk and return — with novel soft risk constraints. Callable from Python. Achieved a 50× speed improvement over the original implementation (from ~5 ms to ~100 μs per solve).
Python · Rust · TypeScript · SQL · Fortran · C++ · Julia
Bayesian inference · dynamical systems · genetic algorithms · Monte Carlo · numerical PDEs · bootstrap resampling · Gaussian processes · constrained nonlinear optimization
AWS (Batch, ECS, Aurora, S3) · GCP · Terraform · Kubernetes · Docker · Cloudflare · GitHub Actions · Modal Labs
Scientific computing · geospatial analysis · agricultural technology · aerospace engineering · financial modeling · data visualization · decision support under uncertainty
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