Earned a Master's degree in Data Science from Stevens Institute of Technology. I am passionate about developing advanced computer vision applications and machine learning algorithms that extract meaningful insights from complex datasets. My experience includes working with both structured and unstructured data, with particular focus on implementing computer vision solutions and predictive analytics models to solve real-world problems.

• Python: Proficient in TensorFlow, BeautifulSoup, PyTorch, pandas, matplotlib, Keras, scipy, and many more.
• R: Skilled in data analysis and visualization.
• iScout Sport Tracking: Experienced in sports data tracking and analysis.
• Computer Vision Annotation Tool (CVAT): Adept at annotating and preparing datasets for computer vision projects.
• Roboflow: Competent in using Roboflow for dataset management and model training.
• MS Visual Studio: Skilled in coding, debugging, unit testing, and utilizing AI productivity tools.
• RapidMiner AI Studio: Used to streamline data cleaning, visualizations, and auto-Machine Learning on large data sets.
• Dynamic Route Optmization (DRO): Familiarity with dynamic route optimization software used in delivery routing.

• Machine Learning & Deep Learning
• Computer Vision Applications in Sports
• Time Series Analysis
• Sports Analytics & Predictive Modeling
• Data Pipeline Development

Computer vision application designed to correct user form in Olympic weightlifting

• Utilizes YOLOv8 object detection for real-time form assessment
• Frame-by-frame analysis using CVAT.ai for annotation
• Provides automated coaching feedback based on detected movements
• Tech stack: Python, YOLOv8, CVAT.ai

Neural network trained on WTA Grand Slam match statistics and betting data

• Batch normalization and L2 regularization
• Custom feature engineering for player statistics
• Cross-validation implementation
• Interactive GUI for prediction inputs
• Tech stack: Python, TensorFlow, scikit-learn

Auto-Regressive and Moving Average models for sports statistics

• Implementation in R
• Focus on sports statistics and financial data
• Stock price analysis using statistical methods

Collection of sports data projects:

• Player analysis for WTA athletes
• MiLB data pipeline and analysis
• NBA outcome prediction model
• Web scraping and data cleaning implementations

• IBM – Deep Learning with TensorFlow
• IBM – Computer Vision and Image Processing Essentials
• IBM – Introduction to Intelligent Virtual Agents with IBM watsonx Assistant
• Microsoft – Computer Vision with Microsoft Azure
• Columbia University – Visual Perception
• Edge Impulse – Computer Vision with Embedded Machine Learning