Currently, I work as a Multi-Agent Control Research Assistant at Aspire Research Center, where I engineer smart energy planning software and build high-fidelity simulation platforms. I also intern at Sandia National Laboratories, deploying cutting-edge autonomous sensing and control algorithms on micro quadcopters. My teaching role at Utah State University has allowed me to mentor students and develop automation tools that enhance learning and research.

Utah State University

• Master of Science in Electrical Engineering
  Focus: Control Systems and Autonomy | GPA: 3.95
  Expected May 2025, Logan, Utah

• Bachelor of Science in Mechanical Engineering
  Minor: Computer Science | GPA: 3.93
  May 2023, Logan, Utah

Aspire Research Center | Multi-Agent Control Research Assistant
May 2022 – Present

• Engineered smart energy planning software using Python and MATLAB, reducing monthly energy costs by up to 50%.
• Designed a high-fidelity physics simulation platform with the EnergyPlus library for detailed thermal analysis and rapid prototyping.
• Created data visualizations using Matplotlib and Seaborn to support multi-disciplinary research teams and industry sponsors.
• Generated simulation datasets for regression modeling with MATLAB’s System-Identification Toolbox.
• Formulated MPC algorithms to schedule distributed energy resources and reduce peak power consumption by up to 50%.
• Implemented XGBoost models to forecast power consumption for optimized load scheduling.
• Developed optimal charging schedules for electric bus fleets using the Gurobi optimization library.
• Migrated city bus simulation libraries from ROS 1 to ROS 2 in C++, enhancing system compatibility and reducing latency.

Sandia National Laboratories | Autonomous Sensing and Control Intern
May 2024 – Present

• Spearheaded the transition from simulation to real-world application for multi-agent reinforcement learning algorithms on micro quadcopters.
• Integrated Vicon motion capture with ROS2 and BitCraze’s Crazyflie 2.X platform to enable autonomous coordination and planning.
• Authored technical reports summarizing key outcomes that helped secure project funding.
• Developed RL models in PyTorch for pursuer-evader challenges, surpassing baseline performance metrics.
• Created trajectory datasets to train diffusion-based multi-agent control models, ensuring high data quality.
• Improved agent policies through competitive self-play and integrated an IMU Simulink model into Rate Table simulations for enhanced dynamics analysis.

Utah State University | Teaching Assistant
Jan 2022 – May 2023

• Led lab sessions for 30 students on sensor interfacing with ultrasonic sensors and thermocouples.
• Assisted in designing and debugging LabView VIs for sensor data collection.
• Developed automated grading scripts in Python and Bash for student C++ projects.
• Coordinated material procurement for student projects and guided final project designs.

Replica Mars Rover
Technologies: C++, C#, SolidWorks, Microcontrollers

• Designed and implemented inverse kinematic controls for a 6 DOF robotic arm, enabling efficient real-time operations.
• Enhanced micro-controller software to reduce latency by 30% via serial communication integration with ROS.
• Collaborated on a novel 6-axis robotic arm design with worm-gears and linear rail actuation using SolidWorks.

Power Consumption Forecasting
Technologies: PyTorch, Pandas, Neural Networks, Scikit-Learn

• Preprocessed a smart meter dataset of 5,000 homes to optimize neural network inputs using Pandas.
• Architected a deep residual network as a baseline model for comparison.
• Developed a CONV-LSTM model that outperformed an XGBoost baseline while reducing training time by 90%.

Epsilon Point Controller
Technologies: Nonlinear Controls, Python, Numpy, ROS2, Gazebo

• Engineered a controller for differential drive robots to track nonlinear and time-varying trajectories.
• Optimized the Turtlebot3 control system with LQR techniques to respect velocity constraints.
• Achieved convergence to target trajectories within 2 seconds of receiving new signals.

UAV RRT Path Planner*
Technologies: ROS2, Python, Controls

• Developed a Python-based UAV simulator with advanced frame transformations and force simulations.
• Implemented the RRT* algorithm to enhance 2-D pathfinding capabilities over the standard RRT.
• Conducted extensive Monte Carlo Analysis to validate the planner’s efficiency and reliability.

Certifications:

• Engineer in Training (EIT)
• Reinforcement Learning Specialization (University of Alberta)

Languages:

• Python, C++, MATLAB, Simulink, C#, ARM-Assembly, Java

Technologies & Frameworks:

• Linux, ROS/ROS2, Git, PyTorch, Numpy, Pandas, Gurobi, Eigen, SolidWorks, Docker