I build at the intersection of embedded systems, robotics, and operating systems — firmware to kernel, bare-metal to distributed. M.S. in Computer Engineering from ASU, open source contributor to iotauth, and always happiest close to the metal.
🔐 iotauth/iotauth (Open Source Contribution)
Contributing to SST (Secure Swarm Toolkit) — an award-winning research project (ACM/IEEE Best Paper at CPSWeek '17) that provides locally centralized, globally distributed authentication and authorization for IoT and distributed systems. The framework manages secure key distribution between devices through a local Auth entity, bridging trust between constrained IoT nodes and the broader internet.
🖥️ ME-learning_Embedded (Personal Project)
Exploring bare-metal programming across hardware targets using QEMU. Developing hardware abstraction layers and investigating low-level system behavior — no physical hardware required.
- Robotics & Hardware — Real-time robot control on RP2040 using Lingua Franca reactors (IMU tilt sensing, line tracking, hill-climb, motor control), Bluetooth-driven delivery bots with relay-actuated locking compartments, and servo-based person-tracking camera rigs
- Systems Programming — OS internals on xv6/RISC-V (process scheduling, memory management, system calls in Assembly and C), bare-metal RP2040 peripheral programming via MMIO, interrupt handling, and debounced GPIO — no HALs, no libraries
- Computer Vision & Distributed AI — Face authentication with LBPH classifiers and OpenCV, real-time subject tracking for dynamic camera alignment, and federated learning across edge nodes using PyTorch CNNs with the Flower framework
A person-tracking rig that uses computer vision to dynamically align a camera toward a subject in real time. Includes Python code, 3D model files, and electronics layout.
Solutions to advanced OS assignments implemented in xv6 — covering process scheduling, memory management, and system calls in Assembly and C.
Decentralized machine learning system enabling local model training and secure central aggregation across edge nodes.
A physics-based simulation framework for modeling AI data center server racks. Simulates power consumption, thermal equilibrium (Junction temperature solving), and compute performance of CPUs and GPUs. Implemented independently in both MATLAB/Simulink and Python.
Implemented the LRU Insertion Promotion Vector (IPV) cache replacement policy within the gem5 simulator architecture. Additionally, built a custom deep learning framework from scratch by writing custom CUDA C++ kernels for forward/backward passes and bridging them to Python.
Feel free to reach out if you want to collaborate on robotics, embedded systems, or OS-level projects!
Open to embedded systems & systems software roles — let's build something.