Hi, I'm Hamzeh.
Building AI That Works.
I'm a Software Engineer at Cresset Capital in Chicago, where I work across the AI stack—from training teams and building custom solutions to vendor collaboration and compliance monitoring. I recently graduated with honors from Harvard University, where I studied Computer Science and Statistics and wrote my senior thesis on cross-market economic spillovers between the U.S. and Chinese stock markets.
Experience
Bridging the gap between theory and enterprise AI.
Software Engineer
Building AI infrastructure to support business operations across the organization.
- •Designing and implementing AI infrastructure to enable widespread adoption across all business functions.
- •Developing agentic AI systems to automate internal workflows and enhance operational efficiency.
- •Building analytics and monitoring frameworks to track AI usage, performance, and compliance requirements.
- •Evaluating and assessing vendor AI solutions to inform strategic technology decisions.
Finance Intern
Developed internal generative AI tools.
- •Developed the first internal generative AI tool for finance teams using the Azure OpenAI API.
- •Integrated database connections and management, file search, report generation, and data analysis and visualization in the application through a Python user interface.
- •Initial testing with equity-based compensation analyses reports increased efficiency by ~93%.
Data Science Intern
Biotech Hedge Fund ($150M AUM).
- •Built a Python class to run Monte Carlo simulations for portfolio-return and risk estimation and created a dashboard for results visualization.
- •Provided methods to quantify margin of error on long-term investment success probabilities.
Education
Harvard University
A.B. in Computer Science and Statistics (Honors), Minor in Mathematics
May 2025
Cross-Market Signals: Economic Spillovers Across Markets
This thesis investigates the interconnectedness of the U.S. and Chinese stock markets, analyzing how macroeconomic factors from one country affect the equity returns of the other. Using statistical and machine learning models on the Jensen, Kelly, and Pedersen Global Factors dataset, the study finds strong spillover effects from the U.S. to China, but minimal effects in the reverse direction.
Read the Research PaperFeatured Work
A collection of projects exploring the intersection of AI, Finance, and Data Science.
Provides methods for assessing company similarity using specified sub-sections of 10-K filings. Uses cosine similarity of BERT embeddings for similarity.
A comprehensive analysis of Bitcoin (BTC) and Ethereum (ETH) price movements, implementing various time series analysis techniques, machine learning models, and deep learning architectures. The project goes beyond traditional ARIMA modeling to incorporate advanced neural network architectures, feature engineering with external data sources, and pairs trading strategies.
This project focuses on predicting the movements of stocks over time based on various financial and economic indicators. The goal is to determine the most important financial indicators affecting stock prices and develop an accurate predictive model. The analysis includes feature importance analysis, model training with various machine learning algorithms, and performance evaluation across different market conditions.
An explanation of basic computer graphics. Includes discussions on transformations in 2D and 3D space, quaternions, perspective projections, and shadow mapping.
An explanation of how ChatGPT works from the ground up. Topics include basic neural networks, recurrent neural networks, and long short-term memory networks.
This study dives into how specific metrics influence success in baseball, analyzing hitting, pitching, and fielding metrics to understand team performance. The project combines statistical rigor with domain knowledge to provide insights that can influence roster-building decisions, tactical approaches, and broadcasting narratives. The findings bridge the gap between traditional baseball insights and modern predictive techniques.