Imad Gohar

Imad Gohar

  • Lecturer
Department of Smart Computing and Cyber Resilience
  • School of Computing and Artificial Intelligence
Faculty of Engineering and Technology
SDGs Focus

Biography

Mr. Imad Gohar recently completed his PhD work in Computer Vision and Artificial Intelligence at the School of Engineering and Physical Sciences, Heriot-Watt University Malaysia, under the prestigious James Watt PhD Scholarship. His doctoral research centered on developing AI-powered visual inspection systems for wind turbine blades using drone imagery - advancing the digitalization of energy infrastructure through intelligent automation.

Prior to joining ºìÐÓÊÓƵ University, Mr. Imad held academic and research roles at leading institutions including the National University of Sciences and Technology (NUST), Information Technology University (ITU) Lahore, and Capital University of Science and Technology (CUST) Islamabad. At CUST, he led the Samsung Innovative Campus (SIC) program and served as a trainer for NUST’s AI Summer School.

His research interests lie at the intersection of computer vision and artificial intelligence, with applications in smart infrastructure monitoring, remote sensing, industrial automation, video surveillance, object detection, and human activity recognition using visual and inertial sensors.

Mr. Imad has published several research papers, including articles in Q1 and Q2 journals and presentations at international conferences. His academic excellence has been recognized with awards such as the Best Presentation at the Heriot-Watt Postgraduate Conference and a gold medal for outstanding performance during his undergraduate studies.

Mr. Imad is an Associate Fellow of the Higher Education Academy (AFHEA) and is well-versed in Outcome-Based Education (OBE), including the design and alignment of Course Learning Outcomes (CLOs), Program Learning Outcomes (PLOs), and Bloom’s Taxonomy. He welcomes interdisciplinary research collaborations in AI for sustainability, smart infrastructure monitoring, and human-centered sensing technologies.

Academic & Professional Qualifications

  • MS(CS) - Computer Vision - National University of Science and Technology - Pakistan (2019)
  • BS(CS) - Algorithms - Institute of Computer Sciences and Information Technology (ICS/IT) - Pakistan (2016)

Research Interests

  • Computer vision and artificial intelligence, with applications in smart infrastructure monitoring
  • Remote sensing
  • Industrial automation
  • Video surveillance
  • Object detection, and human activity recognition using visual and inertial sensors.

Teaching Areas

  • Computer Vision
  • Artificial Intelligence & Machine Learning
  • Python Programming for AI and Data Science
  • Core Computer Science courses

Notable Publications

  1. Person Re-identification Using Deep Modeling of Temporally Correlated Inertial Motion Patterns
    Focus: Wearable sensor data, inertial signals, human movement modeling
    SDG: #9 - Industry, Innovation and Infrastructure
  2. Two-Stream Deep CNN-RNN Attentive Pooling Architecture for Video-Based Person Re-identification
    Focus: Deep learning for identity tracking in surveillance systems
    SDG: #11 - Sustainable Cities and Communities, #9 - Industry, Innovation and Infrastructure
  3. Slice-Aided Defect Detection in Ultra High-Resolution Wind Turbine Blade Images
    Focus: Novel slice-based inference technique for analyzing large turbine blade images
    SDG: #7 - Affordable and Clean Energy, #9 - Industry, Innovation and Infrastructure, #13 - Climate Action
  4. Automatic Defect Detection in Wind Turbine Blade Images: Model Benchmarks and Re-Annotations
    Focus: Benchmarking defect detection models and enhancing dataset quality
    SDG: #7 - Affordable and Clean Energy, #9 - Industry, Innovation and Infrastructure
  5. Review of State-of-the-Art Surface Defect Detection on Wind Turbine Blades Through Aerial Imagery: Challenges and Recommendations
    Focus: Comprehensive literature review; highlights gaps and future directions
    SDG: #7 - Affordable and Clean Energy, #9 - Industry, Innovation and Infrastructure, #13 - Climate Action
  6. Optimizing Wind Turbine Surface Defect Detection: A Rotated Bounding Box Approach
    Focus: Oriented object detection for better localization of defects on curved surfaces
    SDG: #7 - Affordable and Clean Energy, #9 - Industry, Innovation and Infrastructure

Achievements & Accolades

James-Watt Scholarship for doctoral degree
Gold Medalist (Undergrad)
Best Presentation Award (HWU PGRC 2025)

Professional Associations

Associate Fellow of Higher Education Agency (AFHEA)