Software Engineering Intern

Citadel, Chicago, IL

June 2020 – September 2020

• Built real-time trading data interface in .NET, providing fixed income traders with immediate visibility into bond market flows and reducing latency in decision-making.
• Enhanced compliance SQL database by restructuring queries and schemas, cutting average query execution time and streamlining regulatory audits.

Research Assistant

University of Chicago BSD - Kidney Stone Lab, Chicago, IL

June 2019 – September 2019

• Automated data-processing workflow via Python (JSON to CSV), reducing manual lab work by ~5 hours/week and improving reproducibility for ongoing chemical assays.
• Designed AutoCAD-based anaerobic solution chamber, stabilizing pH measurements post-collection and improving accuracy of long-term kidney stone research data.

University of Illinois at Urbana-Champaign

Master of Computer Science (Expected January 2026)

Bachelor of Science in Computer Science + Economics (August 2025)

GPA: 3.80 / 4.00

Relevant Coursework: Applied Machine Learning, ML for Software Engineering, Linear Algebra, Algorithms and Data Structures, Economic Forecasting, Discrete Structures

MarketEPOLL

• Exploits the low-latency nature of Llama70b on the Groq API to generate trades as soon as market news comes out, most optimally used to trade quarterly earnings.
• Built event-driven data pipeline with RSS and Zapier webhooks, enabling sub-second ingestion and processing of financial news.
• Integrated LangChain + Snowflake vecDB for structured financial analysis, improving accuracy of trading signals and enabling backtesting.
• Technologies: Groq API, OpenAI API, LangChain, Snowflake API, TypeScript

Binomial Tree Pricing of Equity Derivatives

• Developed a binomial pricing engine for American and European options using Markov chains to model equity up/down movements.
• Implemented user-input interface with TwelveData REST API, enabling real-time option pricing on arbitrary tickers and strike prices.
• Visualized pricing paths with a Treelib-based node design, making option valuation interpretable for both technical and non-technical users.
• Technologies: NumPy, SciPy, Matplotlib, Treelib, REST API