Hey

I do a little bit of everything.

I am currently a Master of Science in Computer Science and Engineering candidate at the University of Michigan, with a focus on machine learning and quantitative finance. I hold a Bachelor of Science degree in Computer Science and a minor in Mathematics from the University of Southern California, Viterbi School of Engineering.

During my time as a Software Development Engineer Intern at Amazon Web Services, I honed my skills in Java development and gained valuable experience in distributed computing. Working with the DynamoDB Restore team, I automated seed table creation and population before testing, which saved 2 developer days per region build. I also developed an ACID-compliant transaction system to handle the complexities of distributed computing, ensuring support for rollbacks and incorporating deadlock detection mechanisms.

In a previous internship at Carl Zeiss Meditec, I utilized my full-stack development skills, combining Angular and C# to create a web application that granted external scientists access to a new perimetry test prototype, enabling continuous refinement of the test algorithm.

My research experience includes applying advanced quantitative techniques such as Principal Component Analysis, GARCH, and ARIMA to construct portfolios that accurately model the performance of target stocks for tax loss harvesting while direct indexing. I presented this research at the prestigious ACM International Conference on AI in Finance 2022. Additionally, I have conducted research in computer vision, using PyTorch and Pandas to create a novel Convolutional Neural Network optimized for target detection in cluttered, infrared environments.

In a personal project, I developed AlphaMax, a high-performance stock trading agent using Proximal Policy Optimization and Optuna hyperparameter tuning. The agent consistently outperforms the S&P 500 Index, and its performance is visualized through innovative Plotly Dash visualizations. I have also explored Geometric Brownian Motion modeling, Kalman Filtering, and Fourier-Based Spectral Estimation to refine trading signal generation.

My technical expertise includes C++ and Python, with a focus on TensorFlow for machine learning, as well as OCaml and Java. I am also proficient in financial modeling and data visualization. With a strong foundation in mathematics and computer science, combined with my passion for writing efficient, high-quality code, I am well-equipped to tackle the challenges faced by quantitative finance firms.

When I'm not developing, I'm probably cooking, lifting, or drinking milk.