Projects
Here are a few projects I’ve worked on from industry roles to academic research and side projects. Each one reflects what I value: practical problem-solving, thoughtful design, and learning by doing.
🔧 Rail Detection with 3D LiDAR
Canadian National Railway, 2024
As a Data Scientist at CN, I built a deep-learning pipeline to detect and localize rail tracks using 3D LiDAR data. The project used XGBoost and Fully Convolutional Networks (FCNs) on rasterized point clouds and contributed to digital twin development, asset monitoring, and predictive maintenance.
🧠 Promotion Optimization Engine
PhD Thesis, 2023–2025
I developed a reinforcement learning model to personalize promotions based on customer engagement and customer lifetime value (CLV). The work combined real-world transactional data, heterogeneous treatment effect analysis, and MILP optimization to benchmark performance. It was tested over a 12-week period with real customer segments.
📊 Marketing Analytics & Order Forecasting
Cook It, 2022
I developed predictive models for weekly order forecasting and customer churn, helping reduce inventory waste and improve planning. Alongside this, I built a Python-based dashboard to visualize key metrics; including average order value (AOV), purchase probability, and promotion response across segments. The tools supported weekly decision-making across growth, retention, and operations teams.
🧭 Travel Recommender System
Side Project
A personal project that uses natural language processing and location data to suggest travel destinations based on user preferences. Built with Python, scikit-learn, and OpenAI embeddings, inspired by a love of exploring and capturing new places.