Dr. Soroush Zare | Engineering | Best Researcher Award

Greadaute Research Assistant University of Virginia United States

📚 Soroush Zare is a dedicated Ph.D. Candidate in Mechanical and Aerospace Engineering at the University of Virginia. With expertise in robotics, soft exoskeletons, and Brain-Computer Interface (BCI) technologies, he specializes in designing advanced systems for rehabilitation and assistive applications. His research integrates AI-driven controls with cutting-edge mechanical design.

Profile

Google Scholar

Education

🎓 University of Virginia (2023–Present)

• Ph.D. in Mechanical and Aerospace Engineering (GPA: 4.0/4.0)
• Focus: EEG-based motor imagery for wearable textile robotics under an NSF-funded project.

🎓 University of Tehran (2018–2021)

• M.S. in Mechanical Engineering (GPA: 3.9/4.0)
• Thesis: Deep Reinforcement Learning Control of Suspended Cable-Driven Robots.

🎓 Shiraz University (2014–2018)

• B.S. in Mechanical Engineering (GPA: 3.6/4.0)
• Thesis: Modeling and analysis of bladeless wind turbines.

Experience

💼 Research Assistant, University of Virginia (2023–Present)

• Developed wearable soft rehabilitation exoskeletons integrating EEG technologies.
• Innovated reinforcement learning frameworks for intuitive robotic control.

💼 Research Assistant, York University (2022–2023)

• Led projects on robotic grasping using deep reinforcement learning.

💼 Research Assistant, University of Tehran (2018–2022)

• Advanced control techniques for Cable-Driven Parallel Robots (CDPRs).

Research Interests

🔬 Robotics, soft exoskeletons, Brain-Computer Interface (BCI) technologies, deep reinforcement learning, EEG-based motor imagery classification, and AI-driven assistive technologies.

Awards

🏆 Honors & Achievements

• NSF Student Travel Award, IEEE/ACM CHASE (2024)
• GRADESTAR Fellowship (2023, 2024)
• Chairperson’s Fellowship (2023)
• Ranked 2nd among solid design students at Shiraz University.

Publications Top Notes: 

📄 Recent Publications

NeuroMotion: EEG-Based Motor Imagery Control of Wearable Exoskeleton (In preparation).

Kinematic analysis of an under-constrained cable-driven robot using neural networks

Wearable upper limb robotics for pervasive health: A review

Experimental study on the control of a suspended cable-driven parallel robot for object tracking purpose

Reconstructing 3-D Graphical Model Using an Under-Constrained Cable-Driven Parallel Robot

Understanding Human Motion Intention from Motor Imagery Eeg Based on Convolutional Neural Network

A Low-Cost Wearable Exoskeleton for Sitting and Standing Assistance

EEG Motor Imagery Classification using Integrated Transformer-CNN for Assistive Technology Control

MIMO Dynamic Control of a Suspended Underactuated Cable Robot Using Genetic Algorithm