Qualifications
- MSc (University of Nottingham)
- PhD / DPhil (University of Hull)
441105 - Algorithms and Data Structures
500083 - Advanced Programming
551456 - Software Engineering Design Patterns
551462 - Design, Develop, Deploy
700105 - Games Achitecture and Concurrency
700109 - Advanced Computational Science
Conference Proceeding
A Model-based RCM Analysis Method
Mian, Z., Jia, S., Shi, X., Tang, C., Chen, J., & Gao, Y. (2020). A Model-based RCM Analysis Method. In IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C) (301-307). https://doi.org/10.1109/QRS-C51114.2020.00059
Journal Article
Computation Tree Logic Model Checking of Multi-Agent Systems Based on Fuzzy Epistemic Interpreted Systems
Li, X., Ma, Z., Mian, Z., Liu, Z., Huang, R., & He, N. (2024). Computation Tree Logic Model Checking of Multi-Agent Systems Based on Fuzzy Epistemic Interpreted Systems. Computers, Materials & Continua, 78(3), 4129-4152. https://doi.org/10.32604/cmc.2024.047168
A literature review of fault diagnosis based on ensemble learning
Mian, Z., Deng, X., Dong, X., Tian, Y., Cao, T., Chen, K., & Jaber, T. A. (2024). A literature review of fault diagnosis based on ensemble learning. Engineering applications of artificial intelligence, 127, Article 107357. https://doi.org/10.1016/j.engappai.2023.107357
Research interests
My research interests lie in the field of trustworthy AI, machine learning, system dependability analysis, reliability centred maintenance (RCM) and intelligent maintenance. I am also interested in model-driving engineering (MDE), automated model transformations and its validation and verification.
Most recently I have been interested in establishing trustworthiness assessment framwork for the design, development and deployment of AI-based systems. This involves the assessment of AI bias, robustness, safety, fairness, explainability, ethicality and etc.
I am also interested in integrating state-of-the-art AI, IoT, Big Data, Blockchain and Digital Twins into the model-based dependability analysis field to obtain an intelligent dependability analysis and maintenance of systems.
Dr. Mian is actively looking for potential Ph.D. students, for informal inquiries, please contact him at z.mian2@hull.ac.uk.
Lead investigator
Project
Funder
Grant
Started
Status
Project
Use of Foundational AI in teaching and assessment – A practical case study in software engineering education
Funder
Council of Professors and Heads of Computing (CPHC)
Grant
£4,980.00
Started
1 February 2024
Status
Ongoing
Postgraduate supervision
Live PhD positions with SCHOLARSHIP:
[1] SafeML-based Confidence Generation and Explainability for UAV-based Anomality Detection of Blades Surface in Offshore Wind Turbines, https://auracdt.hull.ac.uk/research-projects/safeml-based-confidence-generation-and-explainability/
[2] A Holistic Predictive Maintenance Framework for Offshore Wind Turbines Components, https://auracdt.hull.ac.uk/research-projects/a-holistic-predictive-maintenance-framework-for-offshore-wind-turbines-components/
[3] An RCM-based Intelligent Maintenance Framework for Offshore Wind Turbines, https://auracdt.hull.ac.uk/research-projects/an-rcm-based-intelligent-maintenance-framework-for-offshore-wind-turbines/
Live PhD positions WITHOUT scholarship:
[1] Integrating AIs into model-based intelligent maintenance: https://www.hull.ac.uk/study/postgraduate/research/phd/non-funded/integrating-ais-into-model-based-intelligent-maintenance