I am currently pursuing my M.Sc. in Mechanical Engineering at the IDEA Lab , University of Alberta , under the supervision of Dr. Milad Nazarahari and Dr. Albert Vette . The objective of my current research is to apply principles of optimal control, state estimation, neuroscience, and reinforcement learning to optimize learning during robotic training.

Honors and Awards

• University of Alberta Education Abroad Award
• RWTH Aachen – University of Alberta Junior Research Fellowship
• CREATE PD Scholarship for Distinguished Honorary CREATE Students
• Ranked 1st in the Mechanical Engineering Department (GPA: 4/4), University of Alberta
• Graduate Students’ Association (GSA) Travel Award
• University of Ottawa Admission with Financial Support (Declined)
• University of Windsor International Admission (Declined)
• Concordia University International Admission (Declined)
• Ranked Top 20% in the Mechanical Engineering, K. N. Toosi University of Technology

Education

M.Sc. in Mechanical Engineering University of Alberta 2023 – Present · Edmonton, Canada GPA: 4.0 / 4.0 Skills: Robotics, System Identification, Optimal Control, Reinforcement Learning, ViT, LLM

B.Sc. in Mechanical Engineering K. N. Toosi University of Technology 2017 – 2022 · Tehran, Iran GPA: 3.59 / 4.0 Skills: Quadrotor, Reinforcement Learning, Control Systems, Machine Learning

Profossional Experience

Research Assistant IDEA Lab, University of Alberta Optimizing Human-in-the-Loop Robotic Training using System Identification and Optimal Control 2023–Present	Internship IGMR Institute, RWTH Aachen ROS 2 Trajectory Tracking Controller for the Paragrip Parallel Robot Summer 2025
Research Assistant NCBL Lab, University of Alberta Adaptive Robot Game Personalization via Emotion Recognition and Intelligent Decision-Making 2023–2025	Research Assistant ARAS Lab, K. N. Toosi University Reinforcement Learning–Based Quadrotor Control and Optimization 2021–2023

Technical Skills

• Programming Languages: Python, C++, C#, MATLAB/Simulink, PLC Programming
• Robotic Systems: Kinarm, Paragrip Robot, Quanser 2D Planar Robot, Parrot Mambo Drone
• Control Algorithms: PID, Optimal Control, Model Predictive Control (MPC), Resolved-Rate Motion Control, Deep Learning-Based Control, Reinforcement Learning
• Machine Learning Algorithms: Reinforcement Learning (Q-Learning, DDPG, PPO), Computer Vision (CNNs, ViT, YOLO), Sequential Models (RNN, LSTM, GRU), and Classical Machine Learning Methods
• System Identification: Kalman Filter Variants (KF), Moving Horizon Estimation (MHE), Expectation-Maximization (EM)
• Tools & Technologies: ROS, Git, LaTeX, VS Code, Unity, Linux (Ubuntu), Arduino
• Engineering Software: SolidWorks, AutoCAD, MSC.ADAMS