Jeffrey Zhao Email & Phone Number

New York, NY, US

Florida, United States

• Built a financial vector database leveraging text embedding techniques on extracted financial datasets, enabling efficient retrieval for investment modeling, credit risk analysis, and advanced machine learning.
• Implemented RAG with LLMs to automate financial insights extraction, generating high-quality earnings call highlights and analytical reports, boosting investment strategy optimization.
• Deployed LLMs on AWS SageMaker, enhancing scalability for credit scoring, sentiment analysis on market news, and real-time quantitative computations.
• Designed a hybrid coroutine/thread state machine framework to process trading inputs and market queries concurrently, achieving a 3x acceleration in computation speed.
• Engineered a parallel extraction algorithm optimized for multi-core clusters, reducing financial data processing time by 6x for large-scale datasets.
• Debugged and deployed production-quality Python code, leveraging CI/CD pipelines for high reliability and scalability.

Shanghai, China

• Deployed and fine-tuned Qwen and GLM-4 models in a local environment, processing millions of text entries from social media, financial news, and forums. Conducted high-precision sentiment analysis and mapped the.
• Engineered a new Python genetic programming package to replace traditional packages like gplearn, expanding the structure of time series data and operators. The custom-designed algorithm framework addresses the balance.
• Implemented the NSGA-II algorithm for multi-objective genetic programming, overcoming traditional single-objective and linear weighted multi-objective limitations. This algorithm facilitates bi-objective optimization.
• Accelerated the runtime of genetic programming simulations by leveraging PyTorch with NVIDIA CUDA for GPU acceleration and Numba with JIT compilation, outperforming the naive implementation by 85 - 110 times.
• Achieved 19.6% average annualized excess return in backtesting (2022-2024), validating the robustness of multi-objective genetic programming models in financial strategies.

Notre Dame, Indiana, United States

• MOOC (Massive Open Online Course) Data Analysis
• Compiled the anonymized MOOC data from multiple sources in ND system, including homework submission record, video watching log, Q&A participation history, and prior course survey
• Performed the feature engineering to prepare the modeling dataset
• Analyzed the data via multiple data analytics techniques, e.g., ANOVA to evaluate difference between classes; K-mean to cluster unobserved learning styles; and random forest to classify final grade categories. Time.
• Constructed data frames for ARIMA time series modeling
• Evaluated the modeling assumptions by multiple criteria, including stationarity, autocorrelation function (P/ACF), BIC (Bayesian Information Criterion), and MAE (Absolute Error)

Beijing, China

Master Of Science - Ms, Financial Engineering, 3.54/4.0

Activities and Societies: Application Programming for Financial Engineering•Engineered a dynamic programming model for stock trading.

Bachelor Of Science - Bs, 3.94/4