Teaching Assistant
Arizona State University- Teaching assistant for EEE 480 – Feedback Systems in Fall 2026
- Teaching assistant for EEE 304 – Signals and Systems II in Spring 2025
- Grader for EEE 511 – Artificial Neural Computation in Fall 2025
About Me
I am a PhD student in the School of Electrical, Computer and Energy Engineering at Arizona State University, where I work under the supervision of Prof. Jennie Si.
My research focuses on bridging the gap between classical control and reinforcement learning, with broader interests in control, reinforcement learning, real-time embedded systems, and optimization. I am particularly interested in applying these methods to challenging problems in aerospace and motorsports.
Prior to joining ASU, I spent a year as a placement student with the Oracle Red Bull Racing Formula 1 Team, working in the Energy Recovery System Control group. At ASU, I was also part of the Data Acquisition team for Sun Devil Motorsports, ASU's Formula SAE team. Alongside my research, I am currently a teaching assistant for EEE 480 – Feedback Systems and have previously served as a teaching assistant for EEE 304 – Signals and Systems II and as a grader for EEE 511 – Artificial Neural Computation.
Experience
August 2025 – Present
Data Acquisition Engineer
Sun Devil Motorsports- Designed and implemented the embedded software for the Inertial Measurement Unit (IMU) for the Formula SAE car on an ESP32-S3 microcontroller
- Implemented an asynchronous sensor fusion algorithm to estimate vehicle state using IMU and GPS data
Energy Recovery System Control – Student Placement
Oracle Red Bull Racing- Developed and tested Hardware-in-the-Loop (HIL) models for the Energy Recovery System using Simulink Real-Time and HDL Coder
- Developed embedded software for NXP S32K1 and S32K3 microcontrollers in C
- Developed standalone MATLAB applications for automated testing of electric powertrain components
- Supported the battery production testing software
- Worked with protocols including CAN, SPI, I2C, TCP, UDP, Modbus and SCPI
Research and Development Engineer
dox Technologies Inc.- Led the research on lithium-ion battery modeling and battery management algorithms
- Created a research database with over 40 state-of-the-art papers studying State-of-Charge and State-of-Health estimation, power and energy limits, and battery test planning
- Implemented a lithium-ion battery simulation library in Python for cloud simulations with less than 2% Mean Absolute Error
Projects
Trajectory Optimization and MPC for Fuel-Optimal Rocket Landing
Developed a hierarchical guidance framework for the fuel-optimal landing of a reusable rocket. A nonlinear trajectory optimization problem was solved using successive convexification to generate a fuel-optimal reference trajectory, which was then tracked in real time using Model Predictive Control. The framework was implemented in Python with CVXPY and validated in simulation.
GPS-Aided 2D Inertial Navigation System for Formula SAE Car
Developed a GPS-aided Inertial Navigation System for vehicle state estimation, designed as a foundation for the ASU Formula SAE car. The system fuses high-rate IMU measurements with lower-rate GPS data asynchronously, using a bank of Linear and Extended Kalman Filters to estimate vehicle position, velocity, acceleration, heading, and gyroscope bias.
Electric Powertrain Electronic Control Unit
Modeled an electric vehicle powertrain and developed its Electronic Control Unit (ECU) software in Simulink. The control architecture integrates vehicle state control, battery management, and Field-Oriented Control of the electric motor.
Model Predictive Control for F1TENTH Autonomous Car
Developed a Model Predictive Controller for autonomous racing with an F1TENTH RC car. The controller follows a reference racing line to navigate the track, with the control algorithm implemented as a ROS 2 node and tested in the F1TENTH Gym simulation environment. The project also integrates a Docker-based ROS 2 simulation stack and data logging using Foxglove.
Education
Ph.D. in Electrical Engineering
Arizona State University
2026 – PresentGPA: 4.00 / 4.00
M.S. in Electrical Engineering
Arizona State University
2025 – 2026GPA: 4.00 / 4.00
B.E. in Electrical Engineering
Lebanese American University
2019 – 2024GPA: 3.99 / 4.00
Courses
Arizona State University
Graduate- Optimal Control
- Adaptive Control
- Reinforcement Learning in Robotics
- Real-Time Embedded Systems
- Convex Optimization
- Data-Driven Dynamical Systems Analysis and Control
- AI-Based Decision Making in Dynamic Systems
- Sensors and Machine Learning
- Probability and Random Processes
- Multidimensional Signal Processing
Lebanese American University
Undergraduate- Intelligent Engineering Algorithms
- Control Systems
- Embedded Systems
- Microprocessors
- Energy Storage Systems
- Simulation of Electronic Circuits
- Objects and Data Abstraction
- Computer Networks
- Power Systems
- Smart Grids
- Communication Systems
- Electromechanics
- Electromagnetic Waves
- Electromagnetic Fields
- Signals and Systems
- Electrical Circuits
- Differential Equations
- Calculus
- Discrete Structures
- Probability and Statistics
Independent Coursework
Online & Open Courses- Advanced Robotics – UC Berkeley
- F1TENTH Autonomous Racing – University of Pennsylvania