Koorosh Aslansefat

Dr Koorosh Aslansefat

Lecturer/Assistant Professor

Faculty and Department

  • Faculty of Science and Engineering
  • School of Computer Science

Qualifications

  • MSc
  • PhD / DPhil (University of Hull)

Summary

Koorosh Aslansefat is an Assistant Professor of Computer Science and a member of the Dependable Intelligent System Group (DEIS) in the University of Hull. He received 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 the safety evaluation of machine learning known as SafeML. Koorosh Aslansefat is internationally renowned for innovative research on the 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.

MSc Level:

Real-Time Dependable Systems

BSc Level:

Safety Critical Systems

Python Programming

In Preparation:

Responsible AI and Ethics

Recent outputs

View more outputs

Journal Article

Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations

Aslansefat, K., Hashemian, M., Walker, M., Akram, M. N., Sorokos, I., & Papadopoulos, Y. (2023). Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations. IEEE Software, https://doi.org/10.1109/MS.2023.3321282

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

Co-investigator

Project

Funder

Grant

Started

Status

Project

iCASE PhD studentship with QinetiQ (via Newcastle University)

Funder

EPSRC Engineering & Physical Sciences Research Council

Grant

£98,000.00

Started

1 January 2024

Status

Ongoing

Project

iCASE PhD Studentship with QinetiQ

Funder

EPSRC Engineering & Physical Sciences Research Council

Grant

£42,000.00

Started

1 January 2024

Status

Ongoing

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.

Awards and prizes

IET Leslie H. Paddle Award for an Outstanding Researcher

2020

Koorosh has won a prestigious award and “IET Leslie H. Paddle Scholarship of £5,000 from the Institution of Engineering and Technology (IET). The award is in recognition of his outstanding research on his thesis with the title DREAM: Data-driven Reliability-centered Evolutionary and Automated Maintenance for Offshore Wind Farms, where is and is exploring intelligent data-driven optimization of asset maintenance in Offshore Wind Farms.

Top Peer Review Awards in Computer Science, Engineering, and Cross-Field from Publons

2019

Scholarship role

Studentship Award from EDF Energy R&D UK for project entitled: DREAM (Data-driven Reliability-centred Evolutionary Automated Maintenance for Offshore Wind Farms)

2018

Fellowship, Grant No. 723764 for the H2020 project entitled: GO0D MAN (aGent Oriented Zero Defect Multi-stage mANufacturing)

2017

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