Meet our team

Ali Jamshidi received his MSc degree from Tehran University in 2011. In 2008, he also obtained his BSc in the field of engineering. His MSc thesis was on “A new model for vulnerability assessment of infrastructure using ANFIS (Case study: LAVAN Island)”. During his studies, he had the opportunity to participate in several practical research programs to assess risk and vulnerability of critical infrastructures in the field of urban/ industrial hazards. He has several teaching experiences at university for graduate students and industrial workshops.

Since January 2014, he has started his PhD project at the Section of Railway Engineering, Faculty of Civil Engineering and Geosciences, TU Delft. His research interests include: Risk analysis, Asset management, Reliability, and Computational Intelligence.

Design of key performance indicators at the whole system performance

This project is defined to develop a new asset and risk management methodology that is able to deal with multiple involved parties so as to guarantee the long-term objectives of railways for maintaining traffic safety and network availability in a cost-effective way. In broadly speaking, the project includes new methods for:

(1) service contracting that aim at influencing or aligning the contractors’ objectives and their way of operating with the long-term objectives of railways,

(2) coordinated planning of maintenance and operations,

(3) Key Performance Indicators (KPIs) for optimal whole system performance.

Ali Jamshidi has been working, specifically, on the last task which is design of the KPIs in order to improve overall performance of the Dutch railway network.


Teaching Experience

    • Risk Analysis in infrastructures
    • Vulnerability assessment in infrastructures
    • Incident Command Systems (ICS)
    • Risk Analysis of Hazardous Material Transportation (HAZMAT)
    • Disaster management

Ali Jamshidi, MSc

PhD Researcher

+31 (0) 15 278 16 36
Building: 23, room 1.54

Edit Ali Jamshidi, MSc's profile

Close editing screen