His research outputs are in the fields of risk assessment, supplier selection, and developing intelligent decision support systems in the healthcare and manufacturing domains in the presence of uncertainty. The main approach is considering systems as cause-and-effect complex models and by data-driven optimization based on swarm intelligence are optimized.
Currently, his main research focuses on developing explainable intelligent decision support systems based on interpretable Machine Learning methods in the healthcare domain under the supervision of Dr. Yingqian Zhang, Dr. Marco S. Nobile, and Dr. Isel Grau Garcia.
One of the basic rules of the universe is that nothing is perfect. Perfection simply doesn't exist... Without imperfection, neither you nor I would exist." - Stephen Hawking
Mohsen Abbaspour Onari currently is a Ph.D. Candidate in Information Systems Group at the Industrial Engineering & Innovation Sciences (IE&IS) at Eindhoven University of Technology (TU/e). He also is a member of the Eindhoven Artificial Intelligence System Institute (EAISI). He held a Bachelor of Industrial Engineering from Islamic Azad University, Iran in 2015 and received an MSc of Industrial Engineering minor Systems Optimization from the Urmia University of Technology, Iran in 2019, respectively. His master thesis title is "Developing fuzzy cognitive maps based on the game theory" to model the complex systems in interactive behavior under the supervision of Prof. Mustafa Jahangoshai Rezaee.
Comparing Interpretable AI Approaches for the Clinical Environment: an Application to COVID-19(2022)
A fuzzy cognitive map based on Nash bargaining game for supplier selection problem: a case study on auto parts industryOperational Research (2022)
Enhancing risk assessment of manufacturing production process integrating failure modes and sequential fuzzy cognitive mapQuality Engineering (2022)
A medical decision support system for predicting the severity level of COVID-19Complex and Intelligent Systems (2021)
Risk assessment in discrete production processes considering uncertainty and reliability: Z-number multi-stage fuzzy cognitive map with fuzzy learning algorithmArtificial Intelligence Review (2021)