At Middlesex University (2011-Present)
At other universities (Glasgow Caledonian University and Swansea University, 2007-2010)
Teaching asisistant at University of New South Wales (2003-2006)
To Prospective Research Students
You are welcome to contact me if you want to pursue an MPhil/PhD degree in digital twin, machine learning for communications, wireless systems for critical applications and networking at Middlesex University, London, UK. You should be highly motivated with strong background in mathematics, signal processing and/or digital communications.
Current MPhil/PhD students:
Research assistant/Post-doc RF supervision
- Full access to papers can be found via this link to the MDX's repository
Nguyen, Truong-Thang and Dang, Hung V. and Nguyen, Huan X. (2022) Efficient framework for structural reliability analysis based on adaptive ensemble learning paired with subset simulation. Structures , 45 . pp. 1738-1750. ISSN 2352-0124
Niu, Hehao and Lin, Zhi and Chu, Zheng and Zhu, Zhengyu and Xiao, Pei and Nguyen, Huan X. and Lee, Inkyu and Al-Dhahir, Naofal (2022) Joint beamforming design for secure RIS-assisted IoT networks. IEEE Internet of Things Journal . ISSN 2327-4662 (Published online first)
Mihai, Stefan and Yaqoob, Mahnoor and Hung, Dang Viet and Davis, William and Towakel, Praveer and Raza, Mohsin and Karamanoglu, Mehmet and Barn, Balbir and Shetve, Dattaprasad and Prasad, Raja and Venkataraman, Hrishikesh and Trestian, Ramona and Nguyen, Huan X. (2022) Digital twins: a survey on enabling technologies, challenges, trends and future prospects. IEEE Communications Surveys and Tutorials . ISSN 1553-877X (Published online first)
Yaqoob, Mahnoor and Trestian, Ramona and Nguyen, Huan X. (2022) Data-driven network performance prediction for B5G networks: a graph neural network approach. In: IEEE 9th International Conference on Communications and Electronics, 27- 29 Jul 2022, Nha Trang City, Vietnam.
Dang, Hung V. and Tatipamula, Mallik and Nguyen, Huan X. (2022) Cloud-based digital twinning for structural health monitoring using deep learning. IEEE Transactions on Industrial Informatics , 18 (6). pp. 3820-3830. ISSN 1551-3203
Funded projects/grants (in the UK):
Membership of Professional Bodies