- PhD (University of Hull)
Koorosh Aslansefat was born in Tehran, Iran, in 1989. He received the B.Sc. degree in marine electronic and communication engineering from Chabahar Maritime University, Chabahar, Iran, in 2011, and the M.Sc. degree in control engineering from Shahid Beheshti University, Tehran, Iran, in 2014. He is currently pursuing the Ph.D. degree with the University of Hull, Hull, U.K., working on data-driven reliability-centered evolutionary and automated maintenance for offshore wind farms. His main research interests are in artificial intelligence safety and explainability, Markov modeling, performance assessment, optimization, and stochastic modeling.
A conceptual framework to incorporate complex basic events in HiP-HOPS
Kabir, S., Aslansefat, K., Sorokos, I., Papadopoulos, Y., & Gheraibia, Y. (2019). A conceptual framework to incorporate complex basic events in HiP-HOPS. In Y. Papadopoulos, K. Aslansefat, P. Katsaros, & M. Bozzano (Eds.), Model-Based Safety and Assessment. IMBSA 2019 (109-124). Cham: Springer Verlag. https://doi.org/10.1007/978-3-030-32872-6_8
Towards Improving Confidence in Autonomous Vehicle Software: A Study on Traffic Sign Recognition Systems
Aslansefat, K., Kabir, S., Abdullatif, A., Vasudevan Nair, V., & Papadopoulos, Y. (in press). Towards Improving Confidence in Autonomous Vehicle Software: A Study on Traffic Sign Recognition Systems. Computer,
SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures
Aslansefat, K., Sorokos, I., Whiting, D., Tavakoli Kolagari, R., & Papadopoulos, Y. (2020). SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures. Lecture notes in computer science, 12297, 197-211. https://doi.org/10.1007/978-3-030-58920-2_13
A Hybrid Modular Approach for Dynamic Fault Tree Analysis
Kabir, S., Aslansefat, K., Sorokos, I., Papadopoulos, Y., & Konur, S. (2020). A Hybrid Modular Approach for Dynamic Fault Tree Analysis. IEEE Access, 8, 97175-97188. https://doi.org/10.1109/ACCESS.2020.2996643
A runtime safety analysis concept for open adaptive systems
Kabir, S., Sorokos, I., Aslansefat, K., Papadopoulos, Y., Gheraibia, Y., Reich, J., …Wei, R. (2019). A runtime safety analysis concept for open adaptive systems. Lecture notes in computer science, 11842, 332-346. https://doi.org/10.1007/978-3-030-32872-6_22
• Artificial Intelligence Safety and Explainability
• Performance Assessment
• Data-driven Fault Detection, Diagnosis, and Prognosis
• Dependability Evaluation and Improvement
• Optimization and Evolutionary Algorithms
• Probabilistic Modelling (In particular Markov Modelling)
• Offshore Wind Turbines
• Safety Evaluation of Multi-Robot Systems
SafeML: Exploring techniques for safety monitoring of machine learning classifiers.
SafeDrones: Reliability/Safety Modelling and Evaluation of Multicopters (Multi-rotor Drones) and Electric powered Vertical TakeOff and Landing (eVTOL) Aircrafts.