Fourth year Computer Science student at the University of New Brunswick with hands-on experience in data engineering, machine learning, and analytics. Recently completed an analytics internship at MPAC, where I built production ML models and optimized ETL pipelines processing millions of records.

Currently building portfolio projects demonstrating end-to-end ML systems, modern AI architectures, and scalable data pipelines.

Expected Graduation: August 2026 | Location: Fredericton, NB

Programming: Python, SQL, R, Java

ML & AI: TensorFlow, scikit-learn, PyTorch, LangChain, LangGraph, Supervised/Unsupervised Learning, Ensemble Methods, Time Series Forecasting

Data Engineering: Apache Airflow, Apache Kafka, PySpark, ETL/ELT Pipelines, Data Modeling

Databases: PostgreSQL, MySQL, SQLite, MongoDB

Cloud & DevOps: AWS, Azure, Docker, Git

Analytics & Visualization: Pandas, NumPy, Power BI, Matplotlib, Seaborn, Plotly, Streamlit

Analytics Intern | Municipal Property Assessment Corporation (MPAC) | Ottawa, ON |May 2025 – Aug. 2025

• Built resource allocation prediction model achieving 23% improvement using Random Forest on 48M+ records
• Optimized ETL pipelines processing 5M+ records with automated validation at 96% data quality
• Created SQL/BI dashboards reducing manual reporting time and improving decision lead time by 3 days

Teaching Assistant | University of New Brunswick | Fredericton, NB | Sept. 2024 – Dec. 2024

• Mentored 180+ engineering students on Python data workflows
• Authored EDA/visualization tutorials raising assignment scores by 20%

Building production-grade data pipeline with Airflow orchestration for automated NBA data processing and game outcome predictions.

• Tech: Python, Airflow, PostgreSQL, Docker, scikit-learn, XGBoost, FastAPI, Streamlit
• Features: Automated ETL, ensemble ML models, interactive dashboard, REST API

A multi-agent system where AI agents work together to analyze data and answer questions in plain English. Watch agents collaborate in real-time as they break down queries, fetch data, run analysis, and create visualizations.

• Tech: LangChain, LangGraph, Chroma, Groq (Llama 3.1), FastAPI, React, D3.js, PostgreSQL
• Features: Real-time agent visualization, RAG for context-aware queries, interactive dashboards

• 🔨 Building production-grade ML and data engineering portfolio projects
• 📚 Learning advanced LLM architectures and multi-agent systems
• 💼 Seeking Data Engineering, ML Engineering, or Data Science opportunities for Winter or Summer 2026