John G

• Assisted with special events and programs.
• Maintained clean, neat, and operational facilities to serve program needs.
• Communicated with staff members to stay informed about volunteer opportunities and events.
• Used strong interpersonal communication skills to convey information to others.
• Managed approximately 30 incoming calls, emails and faxes per day from customers.

Internship: Data Science / Financial Analyst

Project Title: Crude Oil Price Forecasting via Hybrid EMD-BPNN Model

Developed a high-accuracy predictive system for WTI and Brent crude oil prices to mitigate global economic risks associated with market volatility.

Implemented Empirical Mode Decomposition (EMD) to preprocess non-linear, non-stationary price signals into independent Intrinsic Mode Functions (IMFs).

Integrated a Back Propagation Neural Network (BPNN) to forecast future price trends based on the decomposed frequency components.

Conducted comparative performance analysis against baseline models, including LSSVR and standard ANNs, using rigorous statistical evaluation criteria.

Achieved superior prediction accuracy by effectively capturing the "noise" and underlying trends within complex financial time-series data.

Tech Stack: Python (Anaconda), NumPy, Pandas, Scikit-learn, Matplotlib, and TensorFlow/Keras.

Utilized Google Colab and Jupyter Notebooks for iterative model training and data visualization