Computer Engineering Student | Embedded Systems & High-Performance Computing | May 2026 Graduate
📥 Download ResumeI am a Computer Engineering student specializing in the intersection of low-level software and hardware. My work is driven by a passion for building reliable, high-performance systems that solve tangible problems.
My experience centers on embedded systems programming (C/C++, ARM), analog circuit design, and performance-critical parallel computing (CUDA). I contribute to an NIH-funded research project developing a computational model of the human left atrium, where I optimize simulations and build interactive tools to bridge complex data with real-world understanding.
I am seeking roles in embedded software and systems integration, with a particular interest in applying these skills to advance mission-critical technology in national defense and aerospace.
Feel free to explore my work below and get in touch if you'd like to collaborate or learn more about any of my projects.
Project Overview: A real-time cardiac simulation that recreates the electrophysiological properties of the human left atrium. Using GPU-accelerated physics with over 13,000 interconnected nodes, the model reproduces dangerous arrhythmias like atrial flutter and micro-reentry that physicians treat with catheter ablation. Through an interactive interface, users can trigger ectopic beats, adjust tissue properties, and perform virtual ablations to test treatment strategies—creating both a procedural planning tool for cardiologists and a training platform for medical students.
My Contribution: My role evolved from optimizing CUDA kernels for computational performance to architecting and building the interactive GUI that makes this complexity accessible to medical professionals. I developed the visualization system that translates 13,000+ node interactions into intuitive real-time graphics, implemented the user controls for triggering ectopic beats and performing virtual ablations, and optimized the rendering pipeline to maintain smooth performance during intensive simulations. The result bridges the gap between computational research and practical clinical application—turning a high-performance physics simulation into a tool physicians actually want to use.
Highlights: NIH-funded research (Grant #1R15HL179671-01); NVIDIA GTC 2026 poster (PDF); 1st Place Poster — SIAM Texas/Louisiana Chapter 2024; 1st Place Undergraduate Poster — Tarleton REID Conference 2025.
Project Overview: A CUDA-accelerated N-body simulation modeling the interaction between microplastics and okra polymers to study microplastic removal from water. This research aimed to reduce expensive and time-consuming lab experiments by providing a computational testbed for rapidly evaluating coagulation and stirring dynamics.
My Contribution: I was directly involved in building this entire simulation from the ground up. I programmed the real-time 3D visualization using OpenGL, developed the physics engine for stirring dynamics, and worked closely with the chemistry team to translate their mathematical models into working simulation code. The coagulation algorithms were derived from chemical equations provided by the chemistry researchers, which I implemented to accurately represent how okra polymers bind with microplastics. By creating this tool, we enabled rapid iteration on experimental parameters without the need for physical lab setups, significantly accelerating the research timeline.
Highlights: 1st Place Graduate Poster – Tarleton REID Conference 2025.
Project Overview: An end-to-end analog pink-noise generator using a reverse-biased BC337-16 transistor for the noise source, passive RC shelves to shape the -3 dB/octave spectrum, and a TL072 gain stage with adjustable output powered by +/-9 V batteries.
My Contribution: Working with a partner, I helped design the circuit schematic from component selection through final topology. I wrote Python scripts to validate our theoretical model against expected performance characteristics and simulated the circuit in LTSpice to verify frequency response before building. I was hands-on in the physical construction, assembling the circuit on breadboard and performing bench measurements to confirm it met our design specifications. The project page includes full schematics, spectral analysis, audio samples, bill of materials, and build photos documenting the entire design process.
Coming Soon! A secure embedded IoT sensor node implementing a hardware-rooted chain of trust for environmental data. Built around the STM32H573I-DK's Arm Cortex-M33 core, the system leverages its dedicated TrustZone secure world and cryptographic accelerators to authenticate readings from a Bosch BME280 temperature, humidity, and pressure sensor. The firmware creates cryptographically signed data packets at the point of measurement, which are then logged to local storage. This project demonstrates the practical implementation of hardware security modules (HSM), secure boot principles, and trusted execution environments—serving as a foundational prototype for secure telemetry in defense, industrial monitoring, and critical infrastructure applications.
Coming Soon! An autonomous drone system for the DARPA Lift Challenge as part of Tarleton's Society of Aerospace Engineers (SAE) Team. This project focuses on innovative approaches to aerial load-lifting, control algorithms, and autonomous navigation.
B.S. in Computer Science (Concentration: Computer Engineering), Minor in Mathematics • Expected May 2026
GPA: 3.94/4.00 (Institutional) • Cumulative: 3.75/4.00
Relevant Coursework: Embedded Systems, GPU/Parallel Computing, Digital Logic, Data Structures & Algorithms
Authors: Bryant Wyatt, Gavin McIntosh, Avery Campbell, Milanie Little, Brandon Wyatt, Mason Bane, Leah Rogers, Kyla Moore, Conner Homrighaus, Charles Puelz
This publication presents the development and clinical application of a real-time interactive digital twin of the human left atrium, enabling physicians and medical students to study and simulate dangerous arrhythmias like atrial flutter and micro-reentry, with applications in catheter ablation planning and medical education.
Authors: Bryant Wyatt, Mason Bane
This paper presents methods for accelerating undergraduate research computations using GPU parallel processing, demonstrating significant performance improvements in scientific computing applications through CUDA implementation.
Feel free to reach out to me: