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