CS @ Purdue (Honors), building ML systems with real-world applications.
// 01 . log
B.S. Computer Science, Purdue University, John Martinson Honors College. Expected May 2030.
ML research, applied ML, and quant backtesting. Focused on building predictive models and systematic strategies grounded in real-world data.
I test things on real data before I trust them. Six projects so far, each one backtested, benchmarked, or peer reviewed in some form.
Working on volatility-based trading strategies and looking for research or internship opportunities in ML, quant, and SWE
// 02 . research
Original research evaluating whether machine learning can mitigate cognitive bias in fantasy football projections. Position-specific XGBoost models were trained on ten years of NFL data with over 1,900 engineered features, improving ranking accuracy, especially for quarterbacks and tight ends, by reducing narrative-driven bias.
view paper ↗// 03 . builds
The functional implementation of my published research: a tool that generates fantasy football rankings from position-specific XGBoost models and compares them directly against ESPN's expert projections to surface undervalued and overvalued players. Includes a full evaluation pipeline that scores both against final season results to see who was actually right.
A point-in-time backtesting framework for U.S. equities. A dual-filter strategy identifies price breakouts via 50/200-day moving average spreads, then normalizes signal strength using Garman-Klass volatility estimators and allocates capital inversely to risk. Built as six pipeline stages, from raw ticker partitioning through a daily-rebalanced execution simulator with liquidity guardrails.
A deployed NBA analytics platform with live scores, standings, rosters, and boxscores, plus a machine-learning core that predicts player performance for the upcoming season. A toggle switches between fast stat lookups and an AI search mode: a LangChain and Ollama RAG pipeline that answers natural-language basketball questions over a Wikipedia-derived knowledge base.
An NLP system that extracts three semantic frames, capital stock, commercial transaction, and business, from financial text. Named entities are corrected with a custom gazetteer, frames are matched against lexical units using Wu-Palmer and Leacock-Chodorow WordNet similarity scoring, and frame elements like buyer, seller, and monetary amount are pulled from surrounding context.
A full-stack web app that predicts likely diseases from user-reported symptoms, built with a small team. I led the project, trained the XGBoost classifier behind the prediction engine, and built the Flask backend along with a health-history tracker that lets logged-in users review past diagnoses over time, all behind secure, hashed authentication.
A command-line password manager written in C from the ground up. Master-password authentication gates a vault encrypted with a repeating-key XOR cipher, stored in custom binary structs via raw file I/O. Includes duplicate detection, strength scoring across length and character variety, and randomized 20-character password generation.
// 04 . stack
// 05 . contact
Open to research collaborations, internships, and interesting problems.