Volatility Trading Solutions Developer at Walleye Capital (2025-11 – 2026-02)
- Developed and managed projections for dividends and earnings events to support options pricing, reducing reliance on paid feeds
- Contributed to rho hedging system by incorporating exposure aggregation, normalization across maturities, and execution logic
Quantitative Trading Intern at Belvedere Trading (2025-06 – 2025-08)
- Completed intensive options curriculum covering Greeks, options spreads, volatility skew, level conversions, hedging, and pricing
- Traded in 30+ mock sessions; managed Greeks, quoted widths, and applied level conversions under dynamic market conditions
- Shadowed 10+ professional traders across various desks including SPX, Individual Equities, Grains, Oil, Natural Gas, Rates, Metals, and a fully automated desk to learn quoting strategies and risk systems across asset classes
- Analyzed a trade sequence by investigating parameters such as width, size, and fit aggressiveness to diagnose suboptimal market-taking; proposed and backtested various changes to width mechanisms to increase instant edge by 17%
- Backtested volatility dispersion strategy based on PCA factor model on SMH, preempting mean reversion by 34 days on average
Trading Bootcamp Intern at Valkyrie Trading (2025-05 – 2025-05)
- Developed Python algorithm to compute real-time theoretical prices via Monte Carlo simulation in rabbit race game; executed automated market-taking based on edge thresholds to capture 4% edge on average per trade
- Identified synthetic arbitrage by exploiting price sum discrepancies across 8 binary contracts, capturing consistent edge in simulation
- Priced and traded 20+ complex options strategies (e.g., straddles, butterflies, 1-by-2s) in a card-sum market-making game
Software Development Engineer Intern at Amazon Web Services (Annapurna Labs) (2024-05 – 2024-08)
- Spearheaded integration and performance benchmarking of GSPMD within Annapurna Labs' ML compilation process with PyTorch XLA, debugging issues to facilitate sharding and increase training and inference speed by over 200%
- Enhanced NeuronX Distributed library by refactoring code and adding test cases for new models such as Llama3, reducing time spent in onboarding new models by over 50%
Software Engineer Intern at JPMorgan Chase (2023-01 – 2023-08)
- Increased rate of detection of anomalous spikes in alerts volume by 200% by training machine learning models including DBSCAN, Isolation Forest, LSTM, SARIMA, and Prophet on time series data
- Reduced developers' search time for anomalous data points by 50% by building predictive analytics dashboard with React, Express, Node, Cassandra, and Splunk
- Achieved 90% reduction in data retrieval time by utilizing Apache Kafka for real-time streaming, allowing for continuous model updates and prediction generation
Undergraduate Researcher at Illinois Risk Lab (2022-08 – 2022-12)
- Reviewed literature on representation learning and implemented novel framework from key paper on LSTM model for textual data, achieving 70% reduction in number of potentially important words and phrases
Software Engineer Intern at COUNTRY Financial (2022-05 – 2022-08)
- Decreased manual accounting time for IT infrastructure uptime by 40% by building web application with Angular, Express, and Node to dynamically render tabular view, replacing outdated Excel spreadsheet
- Reduced crop quote generation time by 50% by implementing new quoting interface with React, Bootstrap, Express, Node, and Postgres, reducing reliance on outdated Excel spreadsheet
- Cut cloud resource configuration time by 30% by deploying applications to Azure, utilizing GitLab CI/CD for tests and code quality scans, and managing resources with Ansible
