Dual-PhD academic · Sydney, Australia
Research that makes
intelligence useful.
I’m Dr Naimat Ullah Khan, a lecturer and researcher working across artificial intelligence, data analytics, cybersecurity and intelligent industrial systems.
Dr Naimat Ullah KhanTwo doctorates · One academic journey
Artificial Intelligence ◆Data Analytics ◆Cybersecurity ◆Industrial IoT ◆Cloud Computing ◆Recommender Systems ◆Artificial Intelligence ◆Data Analytics ◆Cybersecurity ◆Industrial IoT ◆Cloud Computing ◆Recommender Systems ◆
01 About
Research with purpose. Teaching with clarity. Technology that earns people’s trust.
My research sits at the intersection of machine learning, industrial IoT, data-intensive systems and cybersecurity. I focus on trustworthy methods for anomaly detection, generative modelling, recommender systems and explainable AI.
In the classroom, I turn difficult technical ideas into structured, practical learning. I teach, coordinate units, design assessments and supervise emerging researchers across Australia’s higher-education sector.
Academic journey
Two doctorates. One connected research story.
My academic path connects communication systems, machine learning and computer systems, giving me a broad foundation for research that moves between data, infrastructure and human decisions.
PhD, Computer SystemsUniversity of Technology Sydney
PhD, Communication & Information SystemsShanghai University
02 Research
Intelligent systems for consequential problems.
Four connected research directions, one practical aim: making data-driven systems more robust, explainable and useful.
Causal Sensor-Graph Learning
A causal graph and contrastive-learning framework that detects industrial anomalies while revealing the sensor relationships behind each decision.
Generative Anomaly Detection
Robust WGAN-based approaches, including EWAD-IIoT and EO-WGAN, for learning from scarce, noisy and imbalanced industrial fault data.
Intelligent Edge Systems
Privacy-aware collaborative filtering and edge caching research for responsive next-generation mobile and distributed systems.
Urban Intelligence
Mining location-based social data to understand mobility, visitor behaviour and changing patterns across smart urban environments.
03 Selected work
Journal researchScientific Reports · 2025
A novel ensemble Wasserstein GAN framework for effective anomaly detection in industrial internet of things environments
Nature · Scientific ReportsDOI: 10.1038/s41598-025-07533-1↗Journal researchJournal of Computational Design and Engineering · 2025
A robust anomaly detector for imbalanced industrial internet of things data
Oxford Academic · JCDEDOI: 10.1093/jcde/qwaf085↗Journal researchIEEE Sensors Journal · 2025
A Robust Anomaly Detection Framework in Industrial Internet of Things
IEEE XploreDOI: 10.1109/JSEN.2025.3607873↗04 Teaching
Preparing students for problems that don’t come with answer keys.
My teaching combines clear foundations, authentic data, current tools and assessment that rewards genuine understanding.
Explore tutorials ↗Lecturer
Sydney International School of Technology and Commerce
Teaching data visualisation, advanced analytics, cloud computing and research methods through practical, industry-aligned learning.
View SISTC staff profileCasual Academic
University of Technology Sydney
Teaching information security, management and programming while connecting current research with the student experience.
View UTS teaching profileLecturer & Unit Coordinator
Victorian Institute of Technology
Leading units, assessment design and student support across data science, big data, cybersecurity and business analytics.
Research · Teaching · Collaboration
Let’s turn a strong question into meaningful work.
I’m open to academic, research and collaborative opportunities in AI, data analytics, cybersecurity and intelligent systems.