Introduction
I am Dr. Sultan Daud Khan, an Associate Professor at the National University of Technology (NUTECH), Pakistan, with extensive experience in academia, research, and consulting. My areas of expertise include computer vision, AI-based precision agriculture, and crowd dynamics analysis. Recognized among the world’s top 2% scientists in Artificial Intelligence for 2023 and 2024 by Stanford University, I strive to advance knowledge and innovation in my field.
Experience
Associate Professor, NUTECH, Pakistan (2019–Present)
Engaged in teaching, research supervision, and curriculum development in Computer Science. I have mentored several PhD and master's students in projects spanning deep learning, drone-based image analysis, and crowd behavior analysis.
Assistant Professor, University of Hail, Saudi Arabia (2018–2019)
Conducted research on crowd management and taught undergraduate and graduate courses.
Post-Doc Researcher, TIC, Makkah Techno Valley, Saudi Arabia (2015–2017)
Focused on automated tools for real-time crowd management in Masjid-Al-Haram.
Qualification
- Academia:
- PhD in Computer Science, University of Milano-Bicocca, Italy (2013–2016)
- MSc in Electronics and Communication Engineering, Hanyang University, South Korea (2008–2010)
- BSc in Computer System Engineering, UET Peshawar, Pakistan (2001–2005)
I have published over 60 peer-reviewed papers in top-tier journals and conferences, such as IEEE Access, Remote Sensing, and the Arabian Journal for Science and Engineering. My research contributions span areas like computer vision, artificial intelligence, and crowd dynamics analysis. I am currently serving as an Associate Editor for the Journal of Real-Time Image Processing and have also served as a Guest Editor for Remote Sensing and Computers, Materials, and Continua. Additionally, I am a regular reviewer for prominent academic journals, including Neurocomputing, Pattern Recognition, and IEEE Transactions on Emerging Topics in Computational Intelligence.
Taught Courses
I have taught a variety of undergraduate and graduate-level courses in the Computer Science Department. Below are the courses I have been involved in teaching:
- Advanced Artificial Intelligence
- Computer Architecture and Organization
- Parallel and Distributed Computing
- Digital Logic Design
- Computer vision
- Advanced Computer Architecture
Research
My research focuses on the intersection of computer vision, deep learning, and artificial intelligence. I am currently working on several projects that apply AI techniques to precision agriculture and crowd dynamics, such as developing frameworks for automated crop classification and congestion detection in high-density crowds. I have received research funding from organizations like the Higher Education Commission (HEC) of Pakistan and private sector entities for my work on AI-based solutions for drone imagery analysis and crowd management systems.
- Ongoing Research Projects:
- AI-based Smart Precision Agriculture for Rural Areas of Pakistan
- Monitoring, tracking, and counting of crowds during Hajj and Umrah
- Publications:
- Khan, S.D., et al. (2024). "Enhanced YOLOv8-Based Model with Context Enrichment Module for Crowd Counting in Complex Drone Imagery." Remote Sensing.
- Khan, S.D., et al. (2023). "Multi-Scale and Context-Aware Framework for Flood Segmentation in Post-Disaster High-Resolution Aerial Images." Remote Sensing.
Projects
- AI-Based Smart Precision Agriculture for Rural Areas of Pakistan (Ongoing): Co-led the development of an intelligent agricultural system that leverages drone imagery and deep learning to classify crops and weeds, enabling farmers to optimize yield and reduce costs. This project is funded by HEC NRPU.
- Crowd Behavior Analysis and Management System for Masjid-Al-Haram (Completed): Designed and implemented a system that uses computer vision algorithms for real-time crowd counting and congestion detection in Masjid-Al-Haram during Hajj and Umrah. This project was funded by the Deanship of Scientific Research, University of Ha’il.
- Multiclass Classification of Objects in Gray-Scale Aerial Imagery (Completed): Led the development of a deep learning framework for accurate object classification in grayscale drone images. The project was funded by a public sector organization and is currently used for remote sensing applications.
- Crowd Counting and Dynamics Understanding (Completed): Worked as a consultant on the development of tools for analyzing crowd density, movement patterns, and congestion. The project involved real-time surveillance systems and was funded by the Ministry of Education, Saudi Arabia.
- Crowd Analytic Tool for Surveillance (Completed): Developed an AI-powered surveillance system to analyze video streams for motion patterns, congestion hotspots, and crowd density. The project was successfully deployed for monitoring high-density public areas and funded by Makkah Techno Valley.
- Flood Segmentation Framework for Disaster Management (Completed): Created a multi-scale context-aware framework for analyzing post-disaster satellite imagery to aid flood recovery efforts.
- HAJJCore: Crowd Estimation and Management System (Completed): Developed an automatic decision-support system to estimate crowd numbers and monitor dynamics in Masjid-Al-Haram using camera feeds. This system provided actionable insights for better crowd management.
