Jake Blackburn

my real name is John fyi.

About Me:

Im an aspiring software engineer interested in Machine Learning and Neuroscience. Through years of self-taught programming and studying in school, I've gained knowledge and experience across a wide range of STEM disciplines including chemistry, genetics, advanced math, and physics. Since graduating from the College of William and Mary with a degree in Computer Science (ML / AI track) and a Biochemistry minor, I'm aiming for graduate studies where I can perform research in AI and computational biology, learning what I can from modern neuroscience to deepen my understanding of intelligence in general and inform experimentation with innovative models.

I captured this image in November 2023, which depicts the Matts Creek wildfire rolling down the side of a mountain just outside of Glasgow, VA. The image has been edited via my own low level Image Editor project. (See Github Repo)

Projects & Skills

Visit my github for more.

  • 📌 Pinned Projects

  • 🧪 Skyrim Alchemy Data Analysis

    Simulation engine for emulating ESV: Skyrim's alchemy mechanic, built w/ Python and deployed to the web w/ Django, Docker, and Railway. Includes Monte Carlo Simulation experiments revealing actionable gameplay insights; a data science approach to optimizing gameplay.

  • 🧮 Rust Machine Learning

    Rust workspace including examples of machine learning algorithms like Linear Regression, the Perceptron, and multi‑layer Neural Networks, all build on a from-scratch tensor type implementing core Linear Algebra operations.

  • 🎨 Perceptual Style Transfer

    Transfer Learning approach to Neural Style Transfer building on previous research, (Johnson et al, 2016) to implement stylizing CNNs with PyTorch. Demo deployed with FastAPI and Docker on Google Cloud.

    • 🙃 Other Skills

    In my coursework and independent projects I've built things with C/C++, Javascript and Java as well as frameworks like React and Next.js and have gained valuable experience with things like Git, Docker and several flavors of Linux. Having used so many different technologies, I've learned Adaptability is essential. Furthermore, I've had lots of work experiences across a variety of jobs which has upgraded my Teamwork, Communication, and Discipline.

This Red-Eyed Tree Frog has been stylized like the famous painting "Wave of Kanagawa" using a 'perceptual style transfer' model.

Education

TLDR:

Graduated Summa Cum Laude from William and Mary with a B.S. in Computer Science with concentration in Machine Learning & Artificial Intelligence with a minor in Biochemistry.
(Major GPA: 3.84, Cumulative GPA: 3.75)

  • 📚 Significant Coursework:

  • Neural Networks for Machine Learning
  • Rigorously covered machine learning and neural network fundamentals using Python tools like Numpy, Matplotlib, and Tensorflow, Culminating in a substantial contribution to a mutli-modal Mark-Recapture Technique for monitoring Ursus Arctos (Brown Bear) population status, health, and behavior for the Kenai National Wildlife Refuge. Technique utilizes Image Classification, Object Detection and Pose Estimation to automatically identify individual brown bears in massive amounts of footage.
  • Software Engineering
  • Worked in programming teams using SCRUM methodology to systematically improve a Qualtrics survey analysis web + cli tool built with Python and Django, also created and delivered a 35 minute presentation / demo on the Weights and Biases MLops tool.
  • Topological Data Analysis
  • Studied mathematical tools including topology, modern algebra, and group theory, for analyzing datasets using persistent homology, and interpreting the topological features of point clouds. Developed experiments using Python and gudhi analyzing effectiveness of persistent homology for reconstructing the homology groups of manifolds such as the 'Mobius Strip'.
Project for "Topological Data Analysis" class: point clouds sampled from the famous manifold, the 'Mobius strip', used to reconstruct the topological features of the underlying manifold using persistent homology.