Introduction

AI/ML engineer working on RAG systems, applied reinforcement learning, and NLP.

AI/ML engineer. I build RAG systems, train reinforcement-learning agents, and ship NLP.

A bias I'll admit to: most of what ships as "AI" is retrieval with a good system prompt, and benchmark leaderboards are mostly marketing. What actually matters is whether a system stays reliable on real inputs and honest about what it doesn't know. That is the bar I hold my work to.

I'm an AI & ML Associate at Impact Solutions Lab, an AAM Foundation initiative, working on the Program and Curriculum vertical. Before this I interned here and on a couple of ML and analytics teams. Somewhere in between, I trained an agent to hunt people through a dungeon and wrote a paper about it.

Talk to my AI twinA retrieval-augmented chatbot grounded in a curated corpus, with its retrieval trace on display.Play my RL gameHunter Wumpus: a trained agent hunts you through a dungeon. The subject of a published paper.Read the projectsRAG systems, applied reinforcement learning, and NLP, with the code and write-ups behind them.

What I work on

  • Retrieval-augmented generation: chatbots and document-intelligence systems that answer from real sources, with citations instead of guesses. The AI twin on this site is one; a mental-health assistant and a hackathon-built document RAG are others.
  • Reinforcement learning: agents that behave, not just score. Hunter Wumpus is the flagship, a PPO agent that hunts the player from memory and the subject of a published paper.
  • NLP and classical ML: attention models, forecasting, and the unglamorous end-to-end work from data cleaning to a model that actually ships.
  • The glue around it: full-stack apps in React, Node, and FastAPI, plus automation on GitHub Actions and the dashboards that make results legible.

Right now

Full-time at Impact Solutions Lab, I build AI systems for an education nonprofit. Two of them are live: a Text-to-SQL pipeline that lets staff query thousands of community survey responses in plain English, and a per-student learning chatbot grounded in each student's own class recordings (WhisperX transcription into a pgvector RAG store). The internal tooling I built as an intern, the Drive-to-YouTube pipeline and the exam portal used by 300+ students, still runs in production.

About this site

This site is my documentation. Everything here is versioned, tested in production, and occasionally hunted by a Wumpus. Read the project write-ups, put my AI twin to the test, or play the reinforcement-learning game the paper is about.

Download my résumé (PDF)

AI Twin

A glass-box RAG chatbot, grounded in a curated corpus.