Dr Samiya Khan, Lecturer in Computer Science at the University of Southampton, Delhi, specializes in data science, ethical AI, and interdisciplinary research. A PhD holder and HEA Fellow, she has published widely on digital health, green computing, and responsible AI, and serves as a Women in Data Science Ambassador promoting diversity in technology.
Dr Samiya Khan is a Lecturer in Computer Science at University of Southampton, Delhi, India. She brings deep expertise in data science, ethical AI, and interdisciplinary research, underpinned by substantial international experience in both teaching and research. A strong advocate of responsible innovation, Dr Khan’s work explores emerging domains including AI for social good, data feminism, and Indigenous data governance. She has delivered keynote talks and guest lectures at international conferences and leading universities and has authored and edited books with top academic publishers such as Elsevier and Springer.
Prior to joining the University of Southampton Delhi, she served as a Lecturer in Computer Science at the University of Greenwich, United Kingdom. In this role, she played a pivotal role in curriculum development, student mentorship, and the supervision of award-winning undergraduate research projects. She holds a PhD in Computer Science and has contributed to several funded research initiatives at the intersection of artificial intelligence, healthcare analytics, and sustainability.
Dr Khan is a Fellow of HEA and is widely published in peer-reviewed journals and has authored numerous book chapters and conference papers on topics such as data science for digital health, green computing, and responsible AI. She is also an active ambassador for Women in Data Science (WiDS), championing diversity and equity in technology through workshops, community engagement, and outreach events.
Ahmadi-Assalemi, G., Al-Khateeb, H., Makonese, T. L., Benson, V., Khan, S., & Butt, U. J. (2025). Feature-driven anomalous behaviour detection and incident classification model for ICS in water treatment plants. International Journal of Electronic Security and Digital Forensics, 17(1-2), 1–29. [IF: 0.4]
Al-Sabbagh, A., Hamze, K., Khan, S., & Elkhodr, M. (2024). An Enhanced K-Means Clustering Algorithm for Phishing Attack Detections. Electronics, 13(18), 3677.
Elkhodr, M., Khan, S., & Gide, E. (2024). Semantic IoT Middleware for Healthcare: Integrating Blockchain, AI Feedback, and Semantic Annotations for Enhanced Data Security and Interoperability. Future Internet, 16(1), 22.
Khan, S., Naeem, M. K., Hoque, M. T., Refat, N., Rahman, M. A., & Patwary, M. (2023). A Modified Mental State Assessment Tool for Impact Analysis of VR-based Therapeutic Interventions in Patients with Cognitive Impairment. Digital Health, Sage.
Khan, S., Banday, S. A., & Alam, M. (2023). Big Data for Treatment Planning: Pathways and Possibilities for Smart Healthcare Systems. Current Medical Imaging, 19(1), 19–26.
Khan, S., & Alam, M. (2023). Preprocessing framework for scholarly big data management. Multimedia Tools and Applications, Springer.
Banday, S. A., Nahvi, R., Mir, A. H., Khan, S., AlGhamdi, A. S., & Alshamrani, S. S. (2022). Ground Glass Opacity Detection and Segmentation using CT Images: An Image Statistics Framework. IET Image Processing.
Khan, S., Singh, R., Khan, S., & Ngah, A. H. (2023). Unearthing the barriers of Internet of Things adoption in food supply chain: A developing country perspective. Green Technologies and Sustainability, 1(2).
Shakil, K. A., Alam, M., & Khan, S. (2021). A latency-aware max-min algorithm for resource allocation in cloud. International Journal of Electrical and Computer Engineering (IJECE), 11(1), 671–685.
Khan, S., Liu, X., Shakil, K. A., & Alam, M. (2017). A survey on scholarly data: From big data perspective. Information Processing & Management, 53(4), 923–944.
