Sophomore at UMass Amherst, triple-majoring in CS, Applied Math, and Statistics & Data Science. Right now that means HoopIQ — my 63.9%-accurate NBA prediction engine — and I'd genuinely like to hear what you think of the work.
I study Computer Science, Applied Mathematics, and Statistics & Data Science at UMass Amherst (Class of 2028). The three degrees share one purpose: I want to be equally strong at writing the code, proving the math, and reading the data — because the roles I'm aiming for demand all three.
Right now, that plays out in three places. I do undergraduate research in combinatorial optimization at the DREAM Lab, studying when fast approximations of hard database problems can be trusted. I'm building HoopIQ, an NBA prediction system that goes from 25 seasons of raw data to a validated machine-learning model — with every design decision documented and benchmarked. And I do professional AI evaluation work, stress-testing frontier models' reasoning in financial and technical domains.
My goal is a machine learning, data analytics, data science, or quantitative research role — internship or co-op, and I mean both equally. A co-op's longer runway is a feature to me, not a fallback: more time to own real work and ship something that matters. If you're a recruiter, engineer, or researcher, this site is built to show you exactly how I operate. And if you have advice, I'm listening — seriously.
Each card below is a short preview. The full pages go much deeper — the reasoning, the numbers, and what's honestly still unfinished.
Research on package-query evaluation and combinatorial optimization: I design experiments that measure when fast approximations of hard optimization problems can be trusted, and I stress-test published algorithms to find where they break. Full detail on the research page →
I evaluate the outputs of frontier AI models across financial, quantitative, scientific, and technical problems — judging reasoning quality, factual accuracy, and correctness on structured data and multi-step tasks. It is professional critical thinking: my job is to find precisely where a model's reasoning fails, and it has sharpened how I test my own.
Selected to help teach the databases course: SQL, relational modeling, normalization, and transactions — the same foundations my research builds on. If I can explain it clearly to a room of students, I actually understand it.
Researched funding opportunities and wrote grant proposals that supported five social-enterprise initiatives in rural development. My first experience turning research and analysis into documents that had to persuade real decision-makers.
Most portfolios end with "feel free to reach out." I mean it more literally than that. I'm early in my career, and honest input from people ahead of me is worth more than anything I can Google. Whichever of these fits you, the door is open:
You've seen hundreds of applications — I've written one. What would make mine more competitive for ML, data science, or quant roles? What should I build, learn, or fix next? Blunt is better.
Send adviceInternship, co-op, research role, or something adjacent — even if it doesn't look like an obvious fit, I'd rather hear about it and talk it through. There's no downside to a conversation.
Share itQuestions about anything on this site, thoughts on HoopIQ's roadmap, or a quick chat about your own path — I'll take a 15-minute conversation with anyone doing interesting work.
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