Soroush Zare

Soroush Zare

PhD Candidate @ University of Virginia | AI • BCI • Robotics

About

I design brain-machine interfaces and adaptive control strategies using EEG, reinforcement learning, and robotics. My mission is to revolutionize neurorehabilitation and intelligent assistive technologies. I am currently a member of the WEARLab at the University of Virginia, where we focus on wearable robotics and intelligent rehabilitation systems.

Research Focus

  • 🧠 Brain-Computer Interfaces (BCI): EEG signal processing and neural decoding
  • 🤖 Artificial Intelligence: Deep learning, reinforcement learning, and transformer models
  • 💪 Wearable Robotics: Soft exoskeletons and rehabilitation systems
  • 🏥 Neurorehabilitation: Adaptive control strategies for motor recovery

Research Experience

🔬 Research Assistant @ University of Virginia

Charlottesville, VA • Jan. 2023 - Present

Key Responsibilities

  • Designing and developing soft upper limb rehabilitation exoskeleton
  • Contributed to the design and control of wearable soft rehabilitation robots using soft materials and 3D printing techniques
  • Developed transformer-based deep learning pipelines to decode EEG motor imagery for real-time control of upper-limb exoskeletons
  • Focused on non-invasive EEG signal acquisition, pre-processing, and classification to interpret motor intent and autonomic patterns

Research Focus

  • Collaborating in interdisciplinary teams to integrate high-resolution EEG technologies with real-time motor function support systems
  • Innovating non-invasive EEG sensor technology to reduce setup complexity and enhance user comfort in real-world applications
  • Exploring cortical correlates of motor intent and autonomic modulation to support closed-loop neuro-rehabilitation systems
  • Contributing to the development of deep learning models for early detection of neurodegenerative diseases

🔬 Research Assistant @ York University

Toronto, Canada • Sept. 2022 - Jan. 2023

Lead Project: Smart Grasping Using Deep Reinforcement Learning

  • Developed and simulated robotic grasping mechanisms using UR5 robotic arm in ROS (Robot Operating System)
  • Utilized Gazebo for real-time simulation and testing of robotic control algorithms
  • Implemented deep reinforcement learning techniques for intelligent robotic manipulation

🔬 Research Assistant @ University of Tehran

Tehran, Iran • Sept. 2018 - Sept. 2022

Member of Human and Robot Interaction Laboratory (TaarLab)

  • Control Cable-Driven Parallel Robot (CDPR) Using Deep Reinforcement Learning
  • Construct 3-D model of Objects Using CDPR
  • AI-based Object Tracking Using CDPR

Research Areas

  • System Identification of Suspended Under-constrained Cable-driven Robot
  • Control of Suspended Under-constrained Cable-driven Robot Creating 3D Graphical Model of Objects
  • Control of Suspended Under-constrained Cable-driven Robot Using Reinforcement Learning

Projects

🤖 Linux for Robotics: Obstacle Avoidance Demo

Real-time autonomous robot navigation using Bash scripting, ROS 2, and Gazebo simulation. Built for the Linux for Robotics Certificate by The Construct.

Watch Demo Video Verify Certificate

🧠 EEGDiffFormer: Transformer-based EEG Decoder

Project Overview

Developed a state-of-the-art transformer-based architecture for EEG motor imagery classification, achieving superior performance in intent validation and robotic adaptation.

Key Features

  • Multi-head attention mechanisms
  • Real-time signal processing
  • Adaptive learning algorithms
  • Seamless robotic integration

Impact

This technology enables more accurate brain-computer interfaces for neurorehabilitation applications, improving the precision of motor intent decoding.

💪 NeuroMotion: EEG-Driven Soft Exoskeleton

Project Overview

Designed and implemented a soft exoskeleton system that adapts to user movements using reinforcement learning algorithms and real-time EEG signal processing.

Key Features

  • Soft robotic actuators
  • Adaptive control strategies
  • User intention prediction
  • Personalized rehabilitation protocols

Impact

Provides personalized assistance for individuals with motor impairments, enhancing rehabilitation outcomes through intelligent adaptive control.

🚴 VR-Bike EEG Study: Neuroplasticity Enhancement

Project Overview

Integrated EEG monitoring with virtual reality cycling to study and enhance neuroplasticity in rehabilitation settings.

Key Features

  • Real-time EEG feedback
  • Immersive VR environment
  • Adaptive difficulty levels
  • Comprehensive data analysis

Impact

Demonstrates the potential of combined VR and BCI technologies for accelerating motor recovery and improving patient engagement.

🤖 Smart Grasping (UR5): Deep RL in Robotics

Project Overview

Developed intelligent grasping algorithms using deep reinforcement learning for the UR5 robotic arm in Gazebo simulation environment.

Key Features

  • Deep Q-learning implementation
  • 3D object recognition
  • Adaptive grasping strategies
  • Real-time control optimization

Impact

Advances robotic manipulation capabilities for industrial and healthcare applications, improving efficiency and safety in automated systems.

Publications

Awards

Professional Leadership & Services

🎓 Leadership & Service

  • Vice President - Graduate Student Board, Mechanical and Aerospace Engineering, UVA, 2024
    • Spearheading initiatives to enhance community engagement and academic support among graduate students.
    • Facilitating the integration of innovative academic programs and technologies to enhance student learning and research capabilities.
    • Collaborating with department heads and faculty to address student concerns and improve graduate student life.
    • Leading strategic planning sessions to identify and implement improvements within the department.
    • Promoting a culture of excellence and inclusivity within the graduate student community.
  • Recruitment Chair - Graduate Student Board, Mechanical and Aerospace Engineering, UVA, 2023
    • Organized and executed comprehensive three-day visits for prospective students, including tours of key research facilities such as Link Lab.
    • Coordinated bi-weekly meetings to discuss and plan recruitment strategies and student engagement activities.
    • Managed poster demonstrations that highlighted current research projects and student achievements to visitors and faculty.
    • Developed effective communication strategies to ensure smooth logistics, fostering a welcoming and informative environment for all attendees.
    • Addressed immediate logistical challenges during events, ensuring all planned activities proceeded without interruption.
  • Student Member - Institute of Electrical and Electronics Engineers (IEEE)

📝 Reviews

Contact

Interested in collaboration or have questions about my research? Let's connect!

Email
cyj7tf@virginia.edu
LinkedIn
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