Soroush Zare

I am a PhD Candidate at the University of Virginia specializing in AI-driven brain-computer interfaces and intelligent robotics. My research focuses on developing cutting-edge technologies that bridge neuroscience and engineering, with applications in neurorehabilitation, assistive robotics, and human-machine interaction. I combine expertise in EEG signal processing, deep learning, and reinforcement learning to create innovative solutions for real-world healthcare challenges. As a member of the WEARLab, I contribute to advancing wearable robotics and intelligent rehabilitation systems that improve quality of life for individuals with mobility impairments.

• Coordination-Task-Separability Index: A sEMG-based Estimation of Robot-Assisted Rehabilitation

  Developed a sEMG-based index that quantifies how separable muscle-coordination patterns are across levels of robot assistance during reaching. Using 11-muscle EMG, older adults showed more task-separated, stereotyped coordination, while younger adults showed overlapping and more variable patterns.

  
• Robust Underactuated Point-feet Bipedal Locomotion Using Deep Reinforcement Learning and a Balance Recovery System

  Developed a multi-agent deep reinforcement learning framework for robust bipedal robot locomotion. The system combines two agents: one for walking/running control and another for balance recovery, enabling stable locomotion even under disturbances.

  
• NeuroFlex: Feasibility of EEG-Based Motor Imagery Control of a Soft Glove for Hand Rehabilitation

  Developed an EEG-based brain-computer interface system for controlling a soft robotic glove. The system uses transformer-based deep learning to decode motor imagery signals from EEG, enabling real-time control of the rehabilitation glove for hand therapy.

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

  Developed an integrated transformer-CNN architecture for classifying EEG motor imagery signals. The model combines convolutional neural networks for feature extraction with transformer attention mechanisms for improved classification accuracy in assistive technology applications.

  
• A Low-Cost Wearable Exoskeleton for Sitting and Standing Assistance

  Designed a low-cost wearable exoskeleton system to assist individuals with mobility impairments during sitting and standing transitions. The system features smooth acceleration/deceleration control and adaptive motion support for enhanced user comfort and safety.

  
• 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
• Kinematic Analysis of an Under-constrained Cable-driven Robot Using Neural Networks
• Reconstructing 3-D Graphical Model Using an Under-Constrained Cable-Driven Parallel Robot
• MIMO Dynamic Control of a Suspended Underactuated Cable Robot Using Genetic Algorithm

• Olsen Graduate Fellowship – University of Virginia (2025)
• Outstanding Graduate Research Award – University of Virginia (2025)
• Best Poster Award – Mechanical and Aerospace Engineering, University of Virginia (2025)
• Best Researcher Award in Engineering – Popular Scientist (2025)
• NSF Student Travel Award – CHASE 2024
• GRADESTAR Fellowship – University of Virginia (2023–2024)
• York Graduate Scholarship – York University (2022–2023)