Hello!
I'm Caleb Penley,
a computer science student with a deep interest in researching and designing AI systems inspired by human cognition.
Get in touch: penleyc@icloud.com
Currently attending The Pennsylvania State University pursuing a degree in computer science, I have a keen interest in the intersection between machine learning and neuroscience. I'm currently focused on experimenting with system and architectural design that more-closely mimic the efficient adaptability seen in biological neural networks.
Growing up in a small rural town in northwestern PA, I was surprisingly drawn to the complexity of computation, eventually reverse-engineering everything from engines to computer motherboards.
While expanding my knowledge through experimentation, I have discovered I am fascinated by a variety of disciplines. When I’m not immersed in theory or coding a custom tool, I enjoy reflecting on life's big questions, going on hikes, and socializing with friends and family.
- Python
- C
- Rust
- Java
- Tech Ops
- Machine Learning
- Digital Systems
- Automation
- Teamwork
- Communication
- Coordination
- Mentoring
A full-stack asynchronous dashboard with a TCP-backend for live projector status and management. Secured by UUID-based authentication and automated email alerts for critical errors or projector failures.
Original AI assistant with a Flask REST API for CI/CD, media playback, and real-time weather updates. Scaled to support a server-client design using multi-threaded sockets, leveraging Microsoft Azure for speech recognition and synthesis
A personal research project exploring fine-tuning of a 1B-parameter Llama model to perform textual style transfer using a limited dataset. Examined the effects supervised fine-tuning (SFT) and input/output variations have on model quality and accuracy.
A mdadm-like tool in C for RAID managment, focusing on fail-safe Just a Bunch of Disks (JBOD) operations through rigorous testing. Implemented a caching system to optimize I/O and socket-based communication for scalability and improved performance in a networked environment.
A convolutional VAE in PyTorch from scratch to efficently learn compressed representations from images. Trained on the CIFAR-10 dataset, optimizing for low reconstruction loss while achieving diverse generative sampling.
A personal project exploring the capacity of a lightweight LLM like TinyLlama to perform structured textual reasoning through deductive prediction.
A Java-based academic scheduling system using SQL, supporting dynamic student course enrollment and admin-level schedule control.
Implemented and trained Low-Rank Adaptation modules on a Flux.1 Dev to experiment with efficient model fine-tuning and performance improvements on domain-specific input.
A dynamic movie listings web app using TMDB API for movie data, backed by Firebase and Google Cloud Platform (GCP) functions for reliable showtime access.
Source Code
A simple yet clean and intuitive website designed to showcase pricing, events, and gallery for my dad’s local paintball business.
Source Code
