Matthew Maciesowicz.
Software-Focused Computer Engineer
I’m a Computer Engineer passionate about building clean, modern, and efficient web applications. I mainly work with Next.js, React and TypeScript on the frontend and have experience with backend technologies like Node.js, Express, and MySQL.
About Me
About Me
I’m a Computer Engineer building real, user-facing applications. I work primarily with TypeScript, React, and Next.js. I enjoy building systems that not only look good but serve a purpose that people can interact with and find value in. I’m passionate about building clean, modern, and efficient web applications and exploring how AI can be integrated into real-world applications.
What I Build
I’ve built full-stack applications ranging from real-time web platforms to search engines and custom game systems. My work includes designing RESTful APIs, integrating AI components, and optimizing performance from the frontend down to the infrastructure level.
How I Work
I’ve worked both independently and in team environments using Agile workflows, contributing to frontend development, UI/UX design, and system integration. I care about writing clean, maintainable code and building interfaces that feel intuitive without unnecessary complexity.
Current Focus
Recently, I’ve been working with PyTorch to build and train deep learning models, focusing on how they can be integrated into real-world applications. I have also been exploring building mobile apps with React Native and learning more about cloud infrastructure and DevOps practices to better understand how to deploy and scale applications effectively.
Outside of Code
Outside of development, I spend time playing guitar, sports (badminton and soccer), and board games. I’m generally drawn to things that involve problem-solving or competition.
Skills
Skills
Frontend
- Next.js
- React
- TypeScript
- JavaScript
- Tailwind CSS
- HTML5
- CSS3
Backend
- Node.js
- Express.js
- Next.js API Routes
- MySQL
DevOps
- Git
- GitHub
- AWS
- Linux
- Ubuntu
- Docker
- NPM
AI & Others
- Python
- PyTorch
- C/C++
- XML
- Android
- Java
Projects
Projects
UofTrade Marketplace.
A Second-Hand Marketplace Web Application
A responsive React/Next.js-based marketplace for buying and selling items, akin to Kijiji or Facebook Marketplace. Includes features like browsing, searching, filtering, image uploads, and post creation. The project was developed collaboratively in an Agile environment, utilizing Jira for task management and Docker for containerization.
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Shtei: Chess Variant & AI Engine.
Play and Compete Online in a New Chess Variant
A web application featuring a chess variant called "Shtei" that I co-created with AI opponents and online multiplayer functionality. The project integrates a modified Stockfish engine for AI gameplay and includes a custom Socket-based backend for online matches.
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Etymology Dictionary Android App.
Offline Word Etymology Search App
An Android application that allows users to search and find the etymologies/word origins. Clicking the search bar on the home screen opens a search menu that allows the user to type in a word to search. While the user is typing, words matching the current prompt appear below in a list in real-time by querying the SQL database in the backend. Once the user selects a term, a new menu appears with the selected term along with its corresponding etymology. The app works offline, making it ideal for remote locations!
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GIS Mapping Application.
Explore and navigate your city!
A graphical mapping application that allows users to navigate their city via roads, paths, and subway, show points of interest (such as restaurants, banks, shops, etc.), and search for directions from point A to point B. With emphasis towards cyclists, this map also shows bike lanes/bike paths, including type of bike lane (such as whether or not there is a barriered bike lane), custom points of interest (such as bicycle-share/rental dropoff/pickup locations, drinking fountain locations, washrooms etc.), and sunrise/sunset times for the day.
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Acne Detection & Severity Classification App.
Deep Learning Model for Skin Analysis
A deep learning application trained using PyTorch to detect and classify acne severity from facial images. The model leverages a Region-Based Convolutional Neural Network (R-CNN) for box detection and severity classification, achieving 80% accuracy. The application uses the "acne04" dataset and manually labeled images collected with a Python-based web scraper.
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Mimir Search Engine.
The Internet at Your Fingertips
A Python-based search engine designed to index and retrieve documents efficiently, similar to Google Search. Implements an inverted index algorithm for keyword ranking and result highlighting, deployed on an AWS EC2 instance for scalability.
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