// available for new opportunities
Electrical Engineer bridging embedded systems, machine learning, and automation — building things that actually work.
01 — About
I'm a 4th-year Electrical Engineering student at McMaster University, graduating May 2026 with a Dean's Honours distinction. My work sits at the intersection of embedded systems, machine learning, and industrial automation.
At General Motors, I built Python automation tools that saved weeks of manual effort, streamlined calibration workflows for EV thermal systems, and delivered diagnostic tooling used by cross-functional teams. At the Ministry of Transportation, I led a $200K retrofitting project and designed roadway lighting systems using AutoCAD and AGI32.
My personal projects push further — from a drowsiness detection system running real-time ML on a $55 Raspberry Pi, to a 3D environment scanner built with an MSP432, stepper motor, and ToF sensor. I build embedded systems that do real things in the real world.
I'm actively seeking full-time roles in electrical engineering, embedded systems, mechatronics, or software engineering starting May 2026.
Raspberry Pi, MSP432, Arduino — I design hardware-software systems from the ground up, from sensor interfaces to real-time processing pipelines.
CNN-based facial recognition, Random Forest classifiers, and computer vision systems deployed at the edge on constrained hardware.
Python scripts that eliminate weeks of manual work. VBA macros that boost productivity 20%. Data tools that cut analysis time in half.
AutoCAD wiring diagrams, arc flash analysis, lighting systems, and power distribution for major transportation infrastructure projects.
02 — Skills
03 — Projects
A fully embedded, cloud-free driver safety system deployed on a Raspberry Pi 4B at just $55 in hardware. Integrates computer vision, clinically validated fatigue metrics (PERCLOS, EAR), and a Random Forest classifier into a real-time pipeline at 7–9 Hz — matching commercial ADAS accuracy at 1/20th the cost. A custom 850nm IR LED array restores face detection from 75% to 95%+ in complete darkness.
A multi-factor embedded security system combining deep learning facial recognition (VGG16 CNN) with physical RFID and PIN code authentication. Achieves 80% recognition confidence while integrating hardware door-lock control — demonstrating end-to-end ML deployment on embedded hardware.
A custom-built spatial scanning system using an MSP432 microcontroller, stepper motor, and Time-of-Flight LiDAR sensor. Collects spatial data via I2C, transmits via UART, and renders a full 3D point cloud in Python. Sensor mount 3D-printed using a custom Autodesk Inventor design. Demonstrated 100% usability in environment modeling.
Developed sensing, planning, and control modules for manual, driver-assist, and fully autonomous driving modes on Linux. Implemented Wall Following and Gap Following navigation algorithms using ROS (Robot Operating System) and Python/C++.
Designed and verified digital systems on FPGA using Verilog RTL and Quartus Prime. Built FSM-based Datapath/Controller architectures, verified with ModelSim custom testbenches, and implemented arithmetic/logic circuits from adders to multiplexers.
Led a 5-member team to develop vehicle dynamics models, solar energy control systems, and data processing tools in MATLAB/Simulink. Achieved a 25% system performance improvement through integrated simulation and adaptive control algorithms.
04 — Experience
05 — Education
06 — Contact
I'm open to full-time roles, internships, and project collaborations in embedded systems, electrical engineering, ML, or software. Reach out — I respond fast.
Send me an email