I'm an undergrad at Cornell studying Computer Science and Mathematics.
I'm drawn to state-of-the-art problems and fast moving systems where there's always room to innovate. To that end, I find myself especially interested in developing evals and benchmarks, optimizing systems and infrastructure, and applying models to the right use cases.
Previously, I interned at Microsoft as a Software Engineer. On campus, I'm part of Cornell Data Science and Epsilon Nu Tau, and I've worked as an undergraduate researcher benchmarking video models.
I also love to dance Bhangra, play basketball, boulder, and do food challenges!
Reimplemented the famous "Show, Attend and Tell" paper in PyTorch and extended it with adaptive attention (λ-tuned sentinel gating) and VQA. Analyzed the tradeoffs of soft attention mechanisms for vision-language tasks and presented poster.
// Cornell CS 4782: Deep Learning · team of 3 · May 2026
A trading backtester built with a Cornell Data Science team sponsored by Millennium's Systematic Data Platform team. Extended with short-selling functionality and realistic trading costs (ADV caps, borrow costs, market impact). Used to implement and backtest a Customer-Supplier Momentum strategy.
// Cornell Data Science × Millennium · team of 10 · Mar 2026
A RAG system that indexes Cornell Data Science's GitHub repositories to answer deep technical questions about past projects, with a focus on intelligently chunking and storing context from large codebases.
// Cornell Data Science · team of 6 · May 2025
Here's my notepad, where I post reactions or reflections after coming across a tweet, paper, video, book, or some content makes me think. These notes are often free-form and loosely organized.
