Greater Philadelphia
I am an experienced quantitative researcher with a strong background in machine learning, applied statistics, and software engineering. My expertise sits at the intersection of rigorous research, large-scale experimentation, and high-impact technical execution. I have experience building production-grade machine learning research infrastructure including data pipelines, backtesting systems, and evaluation frameworks that operate on large-scale, real-world datasets with a focus on reproducibility, scalability and, reliable deployment. I earned a Master’s in Data Science from the School of Engineering at Harvard University (GPA: 4.00/4.00). My graduate work focused on deep learning, statistical modeling, and applied computational science. I completed my undergraduate studies in Applied Statistics and Finance at the University of Virginia (UVA) where I received a Bachelor of Science degree from the McIntire School of Commerce (GPA: 3.92/4.00). My research interests include modern machine learning, foundation models, reinforcement learning, and scalable AI systems.
- Served as a graduate teaching fellow for CS 182 - Artificial Intelligence taught by professors Ariel Porcaccia and Stephanie Gill during the Fall semester 2022 - Worked on the development of course assignments, held weekly office hours, assisted with exam grading, and helped create course notes throughout the semester - Course topics included search algorithms, constraint satisfaction problems, convex optimization, integer programming, game theory, AI game playing, Bayesian networks, hidden Markov models (HMMs), Markov decision processes (MDPs), reinforcement learning, decision trees, linear classification, and AI for social choice
- GSAM Fixed Income Analyst – Portfolio Construction and Risk Management Team - Researched and quantified empirical techniques in Python for enhancing rate hedging methodologies for high spread fixed income securities - Developed and backtested quantitative diversifying trade identification methodology with R to better optimize overall risk-adjusted returns - Implemented hierarchical clustering framework in R for identifying proxy-asset peer groups for tail-risk analysis in FX markets - Provided in-depth analyses on strategy level risk usage, return distributions, and beta exposures to support strategic risk allocations
- Served as a teaching assistant for Professor Patrick Dennis’ COMM 3721 Quantitative Finance course (Spring 2018) - Held weekly office hours to help students understand course material and assisted with proctoring and grading exams - Course content focused on teaching programmatic implementations of option pricing methodologies including binomial tree modeling, the Black-Scholes Merton equation, and Monte Carlo simulation
Completed a 10-week rotational internship program in GSAM’s fixed income group as a credit analyst on the high yield corporate credit, portfolio construction & risk management, and securitized credit selection desks