Dr. Naimat Ullah Khan completed his first Ph.D. in Communication and Information Systems at Shanghai University in 2024 and is currently completing a second Ph.D. in Computer Science at the University of Technology Sydney (UTS), with a focus on advanced machine learning models for recommender systems. His research spans spatiotemporal modeling, data analytics, deep learning, and anomaly detection in Industrial IoT environments.
Experience:
- University of Technology Sydney, Australia, Faculty of Engineering and IT,. Casual Lecturer. October 2022- Ongoing
- Victorian Institute of Technology, Australia. Casual Lecturer. July 2024- June 2025
- Tracks Institute of Management, Education and IT, Pakistan. Tutor. March 2020- November 2021
- Al-Tehrir Institute of Information Technology, Pakistan. Tutor. August 2016– July 2017.
- Preston University Islamabad, Pakistan. Casual Lecturer. December 2014- August 2016.
Education:
- Ph.D.: Communication and Information Systems, Shanghai University, 2024.
- Ph.D.: Computer Systems, University of Technology Sydney, (Thesis passed, expected graduation July 2025).
Teaching Experience:
- Data Analysis and Visualization
- Big Data Technologies
- Information Security and Management
- Software Engineering
- Programming (Python, Java)
- Research Methodology in Computer Science
- Data Mining
- Cloud Computing (AWS/GCP)
- Management Information Systems
- Capstone Projects
- Postgraduate research supervision
Publications:
- A Novel Approach for Pattern Classification within Imbalanced Datasets in an industrial Internet of Things Environments. Springer Nature: Scientific Report Journal. 2025, Accepted.
- A Novel Ensemble Wasserstein Generative Adversarial Network for Effective Anomaly Detection in Industrial Internet of Oxford: The Journal of Computational Design and Engineering. 2025, Accepted.
- A Robust Framework for Anomaly Detection in Industrial Internet of Things. IEEE Transaction: Sensors Journal. 2025, Under Review.
- Enhanced Group Recommendation System: A Hybrid Context-Aware Approach with Collaborative Filtering for Location-Based Social Networks. International Journal of Knowledge-Based Development. 2025, Accepted.
- Prediction and Classification of User Activities Using Machine Learning Models from Location-Based Social Network Data. Appl. Sci. 2023, 13.
- Location-based social network’s data analysis and spatio-temporal modeling for the mega city of Shanghai, China. ISPRS International Journal of Geo-Information, 9(2), 76.
- A Study of User Activity Patterns and the Effect of Venue Types on City Dynamics Using Location-Based Social Network Data. ISPRS International Journal of Geo-Information, 9(12), 733.
- A review of human pose estimation from single image. In 2018 International Conference on Audio, Language and Image Processing (ICALIP) (pp. 230-236). IEEE.
- Analyzing the spatiotemporal patterns in green spaces for urban studies using location-based social media data. ISPRS International Journal of Geo-Information, 8(11), 506.
- Role of big data in the development of smart city by analyzing the density of residents in shanghai. Electronics, 9(5), 837.
- Spatiotemporal patterns of visitors in urban green parks by mining social media big data based upon WHO reports. IEEE Access, 8, 39197-39211.
- 3D Object classification using a volumetric deep neural network: An efficient Octree Guided Auxiliary Learning approach. IEEE Access, 8, 23802-23816.
- A new hybrid image encryption algorithm based on 2D-CA, FSM-DNA rule generator, and FSBI. IEEE Access, 7, 81333-81350.
- Migration Impact on Remittances Special Focus on Gulf Countries: A Case Study of Pakistan. North American Academic Research, 2(8), 62-80.
- Enhance Requirement Engineering Techniques in Expert System Development (ICEET 2014).