I'm a recent graduate with a Master's degree in Computational Engineering from Mississippi State University with a passion for all things technically challenging. With a strong foundation in research and problem-solving in the area of big data and machine learning, I’m making my transition into professional software development through personal projects and continuous learning of state-of-the-art techniques in both areas and more.

• 🎓 Academic background in Applied Mathematics and Scientific Computing
• 💡 Interested in Deep Learning, Scientific Computing, Data Science, Computer Vision, High-Performance Computing (HPC), and Machine Learning in general
• 🌱 Currently learning about Vector Databases, RAG Pipelines, and the newest MLOps techniques
• 💞️ I’m looking to collaborate on any and all cool projects so I can keep developing myself as a programmer, mathematician, and ML engineer while getting better at DevOps

My Github looks a little sparse because for a long time, I've been bad about using git in smaller projects meant only for myself. Also, as my focus became more mathematically-oriented, the code for many projects often included mostly simple scripts and quick calculations. I'm getting better with DevOps now and intend to push many old projects when time permits. Some recent projects you see on my profile are those that began some time ago, but were never organized into a true code repository.

A Python module implementing "CAP-VSTNet: Content Affinity Preserved Versatile Style Transfer" that allows for modular implementation of style transfer as an image augmentation in deep learning pipelines, with a fully Pytorch-based framework for image and video training and inference.

A modular, multi-mode turret platform combining motion detection, deep learning object detection, real-time control, and physical actuation to detect and deter pets (e.g. cats) from tampering with hazardous houseplants using a blast of water fired from an automated turret system.

An efficient, interactive GUI framework for reviewing and categorizing image annotations and more, supporting single-label and multi-label classification with a results (slideshow) viewer for quick inspection and optional generation of more advanced views

(early stage) a modular refactoring of a core component of my thesis project: high-level wrappers for a wide range of image-based augmentations, allowing for built-in hyperparameter optimization to automatically tune augmentation application probabilities and parameter values

(early stage) tool for reformatting poorly-cropped webtoon-style pages (continuous vertical comic strips) into more intelligently stacked images, with the ability to export the new images to cbz, cbr, and pdf files.

Languages: Python | Java | C | C++ | Matlab | R | C# | Javascript | Julia | Bash | SQL | Fortran

Frameworks & Libraries: PyTorch | scikit-learn | Tensorflow | CUDA | OpenCV | Jax | MxNet | Raytune | XGBoost | Numpy/Scipy | Pandas | Polars | Matplotlib/Seaborn | Plotly

Tools & Platforms: Git | Docker | CircleCI | Github Actions | git-lfs | Neptune | Tensorboard | Robot Operating System (ROS)