I'm a computational engineer who designs, ships, and operates full-stack ML and LLM-based systems end-to-end. A PhD at the University of Texas at Austin grounded me in numerical methods, stochastic modeling, and scientific computing — the toolkit I now turn on quantitative finance, retrieval-augmented generation, and multimodal document intelligence.
In practice, that means systematic trading systems running in production, LLM-backed research and discovery tooling, hybrid retrieval pipelines, and the serverless cloud scaffolding around them. I'm comfortable owning research, infrastructure, and production code as a single workflow — feature engineering, model selection, calibration, deployment, evaluation, and the unglamorous infrastructure that keeps any of it from breaking at 3 a.m.
Production trading systems. Shipped several systematic strategies: an interday equity options book (LoRA-tuned Chronos embeddings + TabPFN classifiers), a long-short equity portfolio driven by LightGBM alpha/beta forecasts and genetic-algorithm optimization that backtested to 20%+ CAGR with 10% annualized alpha, an intraday options strategy on anomaly-detection signals with Heston/FFT calibration, and an intraday FX system steered by ensemble LLM inference (Groq) over macro news and rate differentials.
Infrastructure & LLM tooling. Built it all on a serverless-first AWS stack (Lambda, DynamoDB, S3, EventBridge, CloudFormation, Modal) with GitHub Actions CI/CD and digest-pinned ECR images. On the LLM side: a Treasury/macro RAG pipeline benchmarking FAISS, Whoosh, SQLite FTS5, and Postgres tsvector; a Llama-4-Scout multimodal PDF Q&A system with Qdrant indexing; and a Streamlit research tool that mines trading rules backtesting above 1.5 Sharpe.
Research. Foundation time-series benchmarking (Chronos vs. TimesFM, base vs. LoRA-adapted), XGBoost-vs-LightGBM alpha/beta studies with SHAP, and experimentation with HMM volatility-regime detection, Black-Litterman shrinkage, and NSGA-II Pareto optimization across the alpha/beta tradeoff.
I joined during the early integration phase of a surgical navigation platform combining imaging and robotic actuation. My work centered on implementing TCP/UDP communication protocols for robotic arm motion control, supporting end-to-end product packaging and deployment, and helping stage a live demonstration that anchored the company's successful Series A round.
I adapted physics-informed modeling originally developed for fusion experiment data to financial time series. The resulting production pipeline used sparse regression for PDE construction and signal generation, paired with a CAGR-maximizing Bayesian TPE optimizer driving a swing-trading system deployed on AWS Lambda.
ML & Optimization
Model development, feature engineering, and optimization across classical ML, probabilistic models, time-series foundation models, and evolutionary search.
Cloud & MLOps
Serverless-first production systems with CI/CD, infrastructure-as-code, containers, and model deployment workflows.
Programming
Python-first engineering with scientific computing, fast data processing, and frontend work for internal analytics tools.
APIs & Dashboards
Production APIs and app backends connecting market data, broker integrations, feeds, and interactive analytics interfaces.
LLM & RAG
LLM tooling and hybrid retrieval systems combining lexical and vector search for production RAG, plus multimodal document intelligence.
Beyond the code
