About Me
Hello! I'm a passionate robotics and high-performance computing graduate exploring the frontier between intelligent algorithms, real-time systems, and scalable design.
I'm currently finishing my Master's in Applied Science at Queen's University. My journey includes applying data analysis tools like Excel, programming design and testing using ROS and ROS2 for robotics, and experience working with data center-sized systems.
With a background in electrical engineering and hands-on experience in Python, C, and C++, I aim to combine technical depth and creative problem-solving to develop high-impact solutions in robotics and high-performance computing.
Affiliations
Education
My Educational Journey and Skill Development
Donald A. Wilson Secondary School
2014 - 2018
Queen's University
2018 - 2023
Bachelor of Applied Science in Electrical Engineering, 2023
Queen's University
2023 - Present
Master's of Applied Science in Electrical Engineering, Winter 2026 Projected
Skills
My Preferred Technologies and Tools
Python
C
C++
ROS & ROS2
Inkscape
Camtasia
Projects
Some of my recent work
Research & Publications
Work that I've published or contributed to.
SHARP: Supercomputing for High-speed Avoidance and Reactive Planning in Robots
September 2025
This paper presents SHARP (Supercomputing for High-speed Avoidance and Reactive Planning), a proof-of-concept study demonstrating how high-performance computing (HPC) can enable millisecond-scale responsiveness in robotic control. While modern robots face increasing demands for reactivity in human–robot shared workspaces, onboard processors are constrained by size, power, and cost. Offloading to HPC offers massive parallelism for trajectory planning, but its feasibility for real-time robotics remains uncertain due to network latency and jitter. We evaluate SHARP in a stress-test scenario where a 7-DOF manipulator must dodge high-speed foam projectiles. Using a parallelized multi-goal A* search implemented with MPI on both local and remote HPC clusters, the system achieves mean planning latencies of 22.9 ms (local) and 30.0 ms (remote, ~300 km away), with avoidance success rates of 84\% and 88\%, respectively. These results show that when round-trip latency remains within the tens-of-milliseconds regime, HPC-side computation is no longer the bottleneck, enabling avoidance well below human reaction times. The SHARP results motivate hybrid control architectures: low-level reflexes remain onboard for safety, while bursty, high-throughput planning tasks are offloaded to HPC for scalability. By reporting per-stage timing and success rates, this study provides a reproducible template for assessing real-time feasibility of HPC-driven robotics. Collectively, SHARP reframes HPC offloading as a viable pathway toward dependable, reactive robots in dynamic environments.
Read PublicationGet in Touch
Feel free to reach out for collaborations or just to say hi.