United States
I am a rising junior at UC San Diego, pursuing a BSc in Computer Science with a minor in Mathematics. With a solid foundation in data structures, algorithms, and math, I have consistently achieved Provost Honors. My academic journey has fueled my passion for quantitative development and financial engineering, driving me to explore complex financial models and innovative technology solutions. Currently, I am working on a dynamic options pricing web app, leveraging Python, Flask, and ReactJS to implement models like Black-Scholes and the Binomial Tree. This project not only demonstrates my financial market knowledge but also showcases my ability to optimize algorithms for efficient response times and integrate real-time market data for actionable trading insights. My commitment to structured application design is evident through the use of MVC patterns and React Hooks for a responsive user interface. Outside of my academic and project work, I am an Olympic recurve archer competing for the UCSD team. My dedication to archery has honed my focus, precision, and perseverance, culminating in achieving a gold medal at the USA 55th Indoor Nationals. I am eager to connect and chat with professionals and peers about quantitative development, programming, archery or a friendly chat. Feel free to reach out!
CSE Tutor for CSE 151A Spring 2026 - Machine Learning: Learning Algorithms
Conducting interpretable machine learning research under Prof. Sanjoy Dasgupta, focusing on inherently interpretable models and Generalised Additive Models (GAMs) with Transformers.
CSE Tutor for CSE 101 Fall 2025- Design & Analysis of Algorithms
Spearheading seminars on strategy research and QuantConnect tutorials, leading technical and social event planning, and organizing in-house trading competitions.
Quantitative Developer @ UC San Diego's Triton Quantitative Trading Club.
- Built a web-based platform to automate due diligence on early-stage AI startups, enabling VCs to make faster and more informed investment decisions. - Designed an LLM-driven agentic workflow using LangChain and LangGraph to scrape, process, and analyze data from online sources like news sites and social media. - Deployed local LLMs with Ollama to reduce inference costs by 85%, while generating scored reports and qualitative insights across 50+ AI startups. - Improved factual reliability and interpretability by reducing hallucination by 60% through prompt engineering, few-shot examples, and iterative human feedback. - Implemented a medallion-style architecture with a local SQL (SQLite) database to store over 2GB of structured startup data with clear data lineage. Currently exploring Model Context Protocol (MCP) integration to modularize tool usage and decouple tool logic from LLM reasoning for scalable expansion.
Developing low-latency, real-time data ingestion and processing systems for systematic trading venture.