I grew up in a small town in North East Texas called Daingerfield with a population of around 2000 people, and am currently a senior at the Massachusetts Institute of Technology studying Mathematics and Computer Science. I discovered my passion for mathematics and computer science when I was around sixteen.
Seattle, Washington, United States
Built CostCompass AI, a generative AI platform for infrastructure cost optimization that analyzed AWS usage and pricing data to deliver real-time anomaly detection, forecasting, and optimizations across AWS accounts and services
Pioneered MCP + agent integration patterns, solving a fundamental deployment challenge adopted across Amazon AI initiatives; became the point of contact for MCP deployments across teams.
Released the tool Amazon-wide, collaborating with an L8 principal engineer and presenting research org-wide; positioned to scale into a full service for executives, engineers, and financial analysts.
Enabled success of LoadLens, an AI-powered load test report generator presented to CEO Andy Jassy, by applying the MCP + agent design pattern to unblock development and ensure production functionality.
Overcame AWS Lambda runtime constraints (cold starts, dependency packaging, subprocess limits) by embedding MCP servers directly within Lambda and bridging them with environment variables, establishing a new serverless deployment pattern.
Engineered enterprise-ready infra: auto-scaling ECS Fargate clusters, authentication via CloudFront + Lambda@Edge, multi-account IAM role assumption, and observability with CloudWatch.
2024 — 2024
Remote
Developed a retrieval-augmented chatbot surfacing warehouse-specific information, improving customer engagement and lead conversion for Warehouse Exchange.
Integrated with HubSpot CRM to optimize contact capture and retention workflows.
Built FastAPI + Firebase backend for real-time chatbot handling, reducing latency and improving reliability.
Implemented an analytics dashboard to track chat frequency, query types, and engagement peaks, giving sales teams actionable insights.
Shipped a React/Next.js chatbot UI optimized for mobile/desktop and a mock chatbot tool for internal behavior testing.
2024 — 2024
Cambridge, Massachusetts, United States
Engineered NLP + graph ML pipelines to build a physician–scientist knowledge graph from
OpenAlex/Dimensions.ai, stored in CockroachDB.
Implemented an unsupervised author disambiguation system inspired by IEEE Big Data 2019 (HGCN embedding): constructed heterogeneous networks with CoAuthor/CoTitle/CoVenue relations, meta-path random walks, and graph-enhanced clustering.
Built custom PyTorch Geometric (GCNConv) models over multi-relation graphs; trained with a skip-gram contrastive objective using alias sampling for positives/negatives.
Integrated text embeddings (Word2Vec/Doc2Vec, NLTK preprocessing) with structural features, improving representation quality.
Validated disambiguation with HAC, Louvain, KMeans clustering and PCA/t-SNE visualization; delivered an incremental update pipeline for streaming publications.
Minneapolis, Minnesota, United States
● Worked closely with senior members of DGV’s trading & research team to enhance existing trading strategies and improve investment outcomes for the firm’s investor base with assets in excess of $1.6 billion
● Engineered portfolio construction software in MATLAB, designed for extensibility and ease of modification, allowing users to input a set of security tickers and automatically generate optimized portfolios utilizing statistical optimization techniques
● Applied LSTM deep learning networks to forecast risk-adjusted returns to construct portfolios that often outperformed more traditional Markowitz based portfolio selection criteria
● Designed and back-tested multiple investment strategies based on the construction of optimal portfolios over a 15-year period; notably some strategies outperformed the S&P 500 Total Return Index in metrics such as Sharpe ratio and annualized risk vs returns
● Developed software using Python and Pandas to process historical options data from the Chicago Board of Options Exchange, reducing the dataset from 2.7 billion to 1 million relevant data points for further analysis while also optimizing for speed and accuracy
● Presented software architecture, research findings, and portfolio strategies to trading, research, and management departments, facilitating data-driven decision-making
Boston, Massachusetts, United States
● Developed smart contracts using technologies such as Solidity, OpenZepplin, Hardhat, and Curve.fi interface on the Ethereum blockchain
● Ensured correct smart contract behavior by writing test suites with Ethers.js and Mocha.js libraries
● Optimized the exchange rates of liquidity pools through mathematical analysis and simulation testing
● Developed simulation suites to simulate exchanges on Curve.fi decentralized exchange
Education
Massachusetts Institute of Technology