I'm a computational engineer building and deploying full-stack ML and LLM-based systems. I hold a PhD from the University of Texas at Austin, where I developed a strong foundation in numerical methods, stochastic modeling, and scientific computing. My work spans feature engineering, multi-objective optimization, regime detection, stochastic modeling, RAG, and multimodal document intelligence.
I've shipped production trading systems, LLM-backed research agents, market intelligence tools, and cloud-native analytics applications. I care about the full lifecycle from data pipelines and model development through deployment, evaluation, and monitoring.
I built production ML systems and trading infrastructure for quantitative strategies. That included volatility arbitrage intraday options trading on AWS Lambda, Chronos embeddings with LoRA tuning and AutoGluon-backed interday options trading on Modal, and NSGA-II-backed sector ETF portfolio construction with regime-adaptive rebalancing on AWS Lambda.
I also engineered supporting production workflows around these systems, using GitHub CI orchestration, Docker containerization, EventBridge scheduling, and React dashboards for monitoring and operations.
On the LLM side, I engineered a Groq-powered FX and macro RSS feed-driven intraday FX trading agent on AWS Lambda, built a Groq plus FAISS RAG agent for swing trading commodity- and rate-sensitive stocks on Modal, and deployed a curated-news-backed LLM-triggered rebalance workflow for a quantum computing and AI thematic index.
I also built a trading signal discovery workflow by engineering fast Pandas processing over large historical options chain datasets and designing a natural-language signal-mining workflow over backtest data. For market intelligence research, I engineered a PDF Q&A system using Groq vision model embeddings to unify text and image content, with domain-aware CLIP tuning, dense retrieval, and a two-pass LLM planning and evaluation workflow.
I joined during the early integration phase of a robotic surgical navigation platform combining imaging and robotic actuation. My work focused on implementing TCP/UDP communication protocols for robotic arm motion control, contributing to end-to-end packaging with Poetry, and supporting deployment through Azure tooling. The team delivered a successful live system demonstration used in Series A fundraising.
I adapted physics-informed modeling originally developed for fusion experiment data to financial time series. The resulting production pipeline combined sparse regression for PDE construction, signal generation, and automated execution. I also implemented a CAGR-maximizing Bayesian TPE optimizer for swing-trading thresholds and deployed the system on AWS Lambda.
ML & Optimization
Model development, feature engineering, and optimization across classical ML, probabilistic 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 & Web
Production APIs and app backends connecting market data, broker integrations, feeds, and interactive analytics interfaces.
NLP & LLMs
RAG systems and LLM tooling with observability, vector retrieval, and evaluation for production use.
Beyond the code
