Summary
Koorosh Aslansefat is Research Associate of Computer Science and a member of the Dependable Intelligent System Group (DEIS) in the University of Hull. He received the M.Sc. degree in control engineering from Shahid Beheshti University, Tehran, Iran, in 2014. He got a fellowship with Grant No. 723764 for the EU H2020 project entitled: GO0D MAN (aGent Oriented Zero Defect Multi-stage mANufacturing) from 2016 to 2018. In 2018, he got a Studentship Award from EDF Energy R&D UK to do a PhD at the University of Hull and have an industrial collaboration with EDF Energy for a project entitled: DREAM (Data-driven Reliability-centred Evolutionary Automated Maintenance for Offshore Wind Farms). In his PhD career, he managed to get the IET Leslie H. Paddle Award for being an Outstanding Researcher for his work on Real-time dependability evaluation and the DREAM project.
In 2021, he became a Research Associate and as a named researcher got a fellowship with Grant No. 101017258 for another EU H2020 project entitled: (SESAME) Safe MultiRobot Systems. In this position, he managed to get an Post-Doctoral Enrichment Award from the Alan Turing Institute for his innovative research on safety evaluation of machine learning known as SafeML. Koorosh Aslansefat is internationally renowned for innovative research on engineering of dependable systems that includes real-time dependability analysis of complex systems and safety assurance of machine learning algorithms. His main research interests are in artificial intelligence safety and explainability, Markov modelling, performance assessment, optimization, stochastic modelling, and runtime dependability evaluation.
Book Chapter
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
Journal Article
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
Research interests
• 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
Lead investigator
Project
Funder
Grant
Started
Status
Project
The Alan Turing Institute - Post-Doctoral Enrichment Awards
Funder
The Alan Turing Institute
Grant
£2,000.00
Started
1 March 2022
Status
Complete
Postgraduate supervision
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.