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Massoud khodadadzadeh

DrMassoud khodadadzadeh

Accepting new Phd students

Dr Massoud Khodadadzadeh, a Lecturer in Computer Science at the University of Bedfordshire, holds a PhD in Computer Science from Ulster University. His research journey includes impactful roles such as a Research Associate in Artificial Intelligence for Data Science at The Bath Institute for the Augmented Human, University of Bath, and previously at the Intelligent Systems Research Centre, Ulster University.

With international collaborations, he has contributed to pioneering projects, including a visiting researcher position at the Centre for Complex Systems and Brain Sciences, Florida Atlantic University. His work encompasses cutting-edge developments in machine learning and deep learning methods for various applications, such as Remote Sensing, Brain-Computer Interface, advanced techniques for Inner Speech Classification, and identifying emergent agency in infants.

Qualifications

Academic Qualifications

  • Master
    Project: Electrical Engineering
  • PhD
    Project: Computer Science

External Positions

External Positions

  • Reviewer
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • Reviewer
    Mitacs Accelerate research proposals

Metrics

All time

Scopus
42

Research Interests

  • Artificial Intelligence (AI)
  • Machine Learning
  • Deep Learning
  • Robotics
  • Remote Sensing
  • Brain-Computer Interface (BCI)

Teaching Expertise

  • Concepts and technologies of Artificial Intelligence (AI)
  • Intelligent systems and data mining
  • Data Science
  • Decision support systems
  • Computer Vision

PhD Projects Available

Also willing to take MSc by research

  1. Multimodal AI for Early Detection of Neurodevelopmental Disorders . This project focuses on low-cost, explainable AI methods for analysing infant movement to support earlier identification of neurodevelopmental risk
  2. Explainable AI for Behaviour and Movement Understanding in Safety-critical Contexts. This project investigates how explainable AI can analyse human behaviour and movement from video, motion, or sensor data to identify risk, fatigue, or deviations from safe procedures in regulated environments such as industry, healthcare, and aviation
  3. Robotic Sensing and AI for Predictive Safety Monitoring. This project explores autonomous robotic inspection using mobile or quadruped robots equipped with LiDAR and hyperspectral imaging to detect early signs of structural or material risk in large facilities, supporting predictive maintenance and safety decision-making
  4. Explainable AI for Anomaly Detection in Safety-critical Sensor Systems. This project focuses on interpretable AI models for detecting early faults and unsafe patterns in multi-sensor data from aircraft, drones, and industrial machinery, with potential for real-time and edge deployment