I build AI tools that help people work better — from agentic research platforms to context-aware session analyzers. I spend time understanding the problem deeply before I ever write a line of code.
I'm a grad student at Drexel finishing up my MS in Machine Learning Engineering, originally from Electrical Engineering.
I tend to spend a lot of time just sitting with a problem before jumping into code. Most of my projects started as personal annoyances — something slowing me down or missing from the tools I was already using. ResearchNu came from drowning in literature reviews. ContextCrunch came from watching my AI sessions silently fall apart with no visibility into why.
Outside of that I'm pretty easy to find on a basketball court or squash court, in the kitchen trying something new, or down a rabbit hole building the next thing.
Agentic AI research platform aggregating 26 live APIs across academic, patent, grant, clinical, and policy domains — returning cited findings, research gaps, and quantified novelty scoring. Built because literature reviews were eating my life.
Free open-source tool that helps Claude, ChatGPT, and Gemini users understand their AI session in real time — tracking context usage, token waste, semantic redundancy, and even CO₂ and water usage per model.
Didn't have time to fill out a March Madness bracket — so I built an AI to do it. 7-factor weighted scoring engine across 63 games, backed by 30 years of NCAA seed data, with a conversational agent covering both the Men's and Women's 2026 Tournaments.
Debugged 45+ PCBs, performed soldering and component diagnostics, and conducted final testing on 1,000+ inclinometers in a clean room environment with ESD certification.
Developed transmission contingency files from EMS models using PSSE, analyzed system overloads and weather events to support operational reliability and strategic planning.
Whether you're curious about my projects, want to collaborate on something, or just want to talk AI — I'm always happy to connect.