About

I am a Computer Engineering student at McMaster University. I love problem-solving and have a passion for learning new skills. I am especially interested in computer vision, machine learning, artificial intelligence, statistics, and embedded systems.

I am on co-op as a Data Engineer Intern at MHI RJ Aviation. So far I have developed scripts in Python and Batch using technologies such as Polars, Pandas, and xlsxwriter to automate and optimize data extraction and processing pipelines.

Outside of my studies and co-op I am the President of the McMaster Artificial Intelligence Society. This role allows me to share my enthusiasm for AI/ML, foster a collaborative environment, and advance McMaster's initiatives in the AI space.

When I am not programming I enjoy working out, listening to music, and spending time with my friends.

Experience

MHI RJ Aviation

Data Engineer Intern

May 2024 - Present

Led the development of a suite of Batch and Python scripts that utilize Tkinter, Polars, and xlsxwriter to automate a large daily data extraction and processing pipeline, resulting in a 73% reduction in runtime.
Developed a web application in Python using Dash, Plotly, Polars, and Tailwind that elegantly and dynamically visualizes sales and backlog data for a multi-million dollar warehouse relocation project.
Fine-tuned a time series sales forecasting model using XGBoost resulting in 18% lower RMSE.
Utilized PowerBI to transform and display sales data resulting in automatically-refreshing, maintenance-free dashboards

MacAI

President

May 2024 - Present

Selected by the previous Co-Presidents and MacAI Executive Team to lead the organization.

MacAI

Director of Education

Sept. 2023 - April 2024

Led the Education Team composed of 7 bachelor's and 2 master's students.
Spearheaded the development and presentation of AI/ML workshops to educate students on a variety of topics such as supervised learning, neural networks, and computer vision, and technologies such as Keras, Tensorflow, and Pandas.
Developed CNN and neural network demos in Jupyter Notebook using TensorFlow, Keras, sklearn, and matplotlib to further attendees understanding of concepts such as data preprocessing, model validation, and model fine-tuning.