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About Me

Information Technology at the University of Cincinnati. I work across the stack — ASP.NET Core and typed React front to back, applied machine learning, and the tooling a live Teeworlds/DDNet community's moderation team runs on. My senior capstone, a mentor matching platform built on real-time matchmaking, won an award at the university's IT Expo.

My work splits across three areas that reinforce each other: shipping typed, production web applications; applied machine learning through the IBM AI Engineering track; and building the tooling that keeps a live game community running day to day.

What I Do

  • Web & Full-Stack

    Production web applications end to end — typed front-ends, real-time back-ends, and the data layer underneath them.

    • TypeScript
    • React
    • Next.js
    • ASP.NET Core

  • AI Engineering

    Certified through IBM's AI Engineering track, with applied work in model training, computer vision and prediction pipelines.

    • PyTorch
    • TensorFlow
    • Keras
    • Scikit-Learn

  • Community & Game Infrastructure

    Moderator and tooling developer for KoG, a Teeworlds/DDNet community — building the systems its moderation team runs on, for a player base of 87,000+ awaiting a platform migration targeting 2027.

    • Python
    • PostgreSQL
    • Docker
    • OAuth 2.0

Certifications

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  • IBM · Coursera

    IBM AI Engineering Professional Certificate

    A six-module track covering machine learning and deep learning end to end — the major Python libraries, neural network architectures, and hands-on lab implementations of each. The through-line was less about any single model than about how to implement them properly and transfer that to new problems.

    VerifyIBM AI Engineering Professional Certificate

  • IBM · Coursera

    Deep Neural Networks with PyTorch

    Part of the IBM AI Engineering track. Practical work in PyTorch alongside Pandas and NumPy, building foundational through intermediate understanding of deep neural networks.

    VerifyDeep Neural Networks with PyTorch

  • IBM · Coursera

    Building Deep Learning Models with TensorFlow

    Supervised architectures — convolutional and recurrent networks — followed by unsupervised ones, including restricted Boltzmann machines for a recommendation system and autoencoders for image reconstruction.

    VerifyBuilding Deep Learning Models with TensorFlow