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 feature engineering, optimization, stochastic modeling, and scientific computing — the toolkit I now turn on quantitative finance, LLM pipelines, and multimodal document intelligence.
In practice, that means systematic trading systems running in production — some driven by classical ML and stochastic models, others steered by staged LLM inference — alongside multimodal LLM agents 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, prompt optimization, deployment, evaluation, and the unglamorous infrastructure that keeps any of it from breaking at 3 a.m.
LLM-backed systems. Deployed multi-asset interday options strategies on pre-trained models: an LLM pipeline that mines trading signals against backtested PnL, surfacing entry/exit rules above 1.5 Sharpe, with TabPFN classifiers gauging trade profitability on LoRA-adapted Amazon Chronos embeddings over technical indicators. Also shipped an intraday FX system with LLM-driven directional inference — a staged pipeline that synthesizes news and price action into market themes and a volatility narrative, then conditions a final prompt into per-pair long/short/no-trade signals — and a DSPy workflow comparing GEPA and MIPRO for prompt optimization against historical trade outcomes.
ML-backed systems. Built intraday options strategies on Bayesian anomaly-detection signals, with stochastic-volatility calibration via constrained optimization and FFT options pricing, generating 5–10% weekly returns on peak capital at risk. Deployed a live equity portfolio with genetic-algorithm optimization and regime-adaptive rebalancing: a regressor→optimizer workflow (forward-looking rolling α/β targets → per-ticker predictions → bi-objective NSGA-II → Pareto-optimal weights) paired with HMM regime detection and Jensen–Shannon rebalancing triggers, reaching 5%+ annualized alpha-to-benchmark on a multi-year backtest.
LLM agents & infrastructure. Built a Qwen2.5-VL multimodal PDF Q&A system with contrastive-embedding fine-tuning and Qdrant indexing, fronted by a FastAPI + Next.js + Streamlit stack. Ran it all on a serverless-first AWS stack (Lambda, EventBridge, DynamoDB, S3, CloudFormation) and Modal, with GitHub Actions CI/CD pushing digest-pinned ECR images across multiple bots, DST-aware EventBridge scheduling, Lambda tuned for ML inference with S3-backed HuggingFace/Torch caches, and React/static dashboards over DynamoDB/S3 served via FastAPI behind Lambda Function URLs.
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, data wrangling, and web scraping and feed ingestion for research tooling.
APIs & Dashboards
Production APIs and app backends connecting market data, broker integrations, feeds, and interactive analytics interfaces.
LLM & RAG
LLM pipelines and prompt optimization for trading inference, plus vector retrieval and multimodal document intelligence.
Beyond the code
