AI/ML Engineer & Data Scientist
Background
I'm a final-year CS and Economics double-major with hands-on experience deploying ML systems, with an intense focus on delivering business value. I care as much about why a model works as whether it does — and I'm drawn to problems where messy real-world data needs to be shaped into something that actually generalizes.
Beyond the technical, my economics background helps me understand customer needs and communicate technical concepts clearly to non-technical users.
Selected Work
Highlights across internship, school, and personal work — each with a full write-up.
Agentic RAG chatbot deployed on Databricks for CoorsTek's R&D group — connecting engineers to their data via orchestrated AI agents. Built from scratch to first production deployment in 2 months.
Production CV system for automated quality control at CoorsTek — improved defect detection accuracy from ~80% to 99% by fixing data quality issues and overhauling the model development procedure.
Deep learning pipeline for pixel-level analysis of ceramic part geometry from XCT images — enabling automated comparison of green-formed and fired parts against CAD spec without manual measurement.
A web app that tracks your experience and job applications, then uses a local LLM to generate a tailored resume and cover letter for each position — built to ship a real AI pipeline quickly in unfamiliar territory.
Read More →ML model trained on Ames, Iowa housing data to predict a home's after-renovation value — motivated by a contractor's need for fast, data-driven ARV estimates before committing to a job.
Read More →Experience
Three consecutive internships over 14 months at one of the world's largest advanced ceramics manufacturers. Built and deployed four production ML systems: semantic segmentation tools for ceramic part analysis, a graph-based material history system, an agentic RAG chatbot (10× faster data retrieval for R&D engineers), and a production defect detection pipeline (improved accuracy from ~80% to 99%).
Background
Rochester Institute of Technology — Rochester, NY
Double major at RIT combining a rigorous CS foundation — ML, deep learning, NLP, algorithms, systems — with quantitative economics in econometrics, game theory, and financial theory. The combination enables me to build models that are technically sound and economically interpretable, a pairing that has proven uniquely valuable across every project I've worked on.
Academic
Two papers in progress — one currently under peer review.
An economic model of a waste-managing firm that chooses to undergo R&D to increase the efficiency of their waste disposal — examining how AI adoption reshapes the firm's investment and profit maximization problem.
Identifies a relatively novel IPR-based subsidy that aims to bring privately optimal levels of R&D into balance with social optimum, comparing it against traditional input- and output-based subsidy approaches.
Writing
Research papers and essays on AI, machine learning policy, and economics.
Analyzes two promising and one concerning aspect of NVIDIA's IP governance strategy as disclosed in their Form 10-K — examining how their tight hardware-software integration creates durable competitive moats, and where that strategy introduces risk.
Referencing the ACM Code of Ethics, GitHub Copilot raises several concerns based on the way it is trained and used — and why awareness of those concerns matters for responsible use.
Get in Touch
Open to full-time roles in AI/ML engineering and data science starting January 2027.