University of Tehran · School of Electrical and Computer Engineering
Industrial Automation & Intelligent Information Processing Laboratory
Researching intelligent systems for complex real-world environments. Directed by Prof. Behzad Moshiri.
- Industrial Automation
- Sensor and Data Fusion
- Intelligent Information Processing
- Control Systems
About the laboratory
IAIIPL studies how measurement, information and intelligence combine into systems that act reliably in the physical world.
Fusion first
Multi-sensor and multi-source information fusion is the laboratory's central methodological commitment.
Theory that ships
Methods are validated on real measurement, with the uncertainty and failure modes left visible.
Cross-disciplinary by design
Collaborations span electrical engineering, industrial automation, transportation and biomedical research.
Director
Behzad Moshiri
Professor of Control Systems Engineering
- Professor, School of Electrical and Computer Engineering, University of Tehran
- Director, Industrial Automation & Intelligent Information Processing Laboratory
- Adjunct Professor, Department of Electrical and Computer Engineering, University of Waterloo
- Member, Waterloo Data & AI Institute
Behzad Moshiri received his B.Sc. degree in mechanical engineering from Iran University of Science and Technology (IUST) in 1984, and M.Sc. and Ph.D. degrees in control systems engineering from the University of Manchester Institute of Science and Technology (UMIST), U.K., in 1987 and 1991 respectively. He joined the School of Electrical and Computer Engineering, University of Tehran in 1992, where he is currently Professor of Control Systems Engineering. He directs the Industrial Automation and Intelligent Information Processing Laboratory and is an Adjunct Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, Canada, and a member of the Waterloo Data & AI Institute.
Research
Themes we work on
Method-driven research spanning estimation, fusion, control and applied machine intelligence.
01
Industrial Automation
Instrumentation, supervision and automation of industrial processes, from sensing and measurement through to plant-level decision making.
02
Sensor and Data Fusion
Combining evidence from multiple sensors and information sources so that a system is more accurate and more robust than any single source.
03
Intelligent Information Processing
Machine learning and data-driven inference applied to complex, high-dimensional and often incomplete real-world data.
04
Control Systems
Classical, adaptive and intelligent control for systems that must operate under uncertainty and changing conditions.
05
Robotics and Intelligent Systems
Perception, planning and control for robotic and autonomous systems operating in unstructured environments.
06
Intelligent Transportation
Sensing, estimation and decision making for transportation networks and vehicles.
People
Current members
Ali Mikaeili
M.Sc. Student
AI - Neuroscience
Elahe Radmanesh
PhD Student
Medical Robotics - Control Systems
Erfan Joodi
M.Sc. Student
Sport - Healthcare
Hamed Soltani
M.Sc. Student
Fault Detection - Information Fusion
Mohammad Vaziri
M.Sc. Student
Multimodal Data Fusion - Trustworthy Medical AI
Niloufar Delfan
PhD Student
Medical Imaging - Neurosurgery
Publications
Recent publications
Kept current automatically from open scholarly records, reviewed before it appears here.
2026
A novel interpretable classification of lumbar spinal stenosis using a cascade deep learning approach and T2-weighted MRI
Mohammad Tabarestani, Niloufar Delfan, Mohammad Khoshnevisan, Jaber Hatam Parikhan, Amirmohammad Bahri, Amin Hessam, Mohsen Nabiuni, Behzad Moshiri
Journal of Neurosurgery Spine
2026
CB-OWL-ViT: A Multimodal Cost-Effective Framework for Contagious Disease Monitoring
Mohammad Fatahi, Danial Sadrian Zadeh, Ali Noormohammadi-Asl, Behzad Moshiri, Otman Basir, Ebrahim Navid Sadjadi, Jesús García-Herrero, José M. Molina
Mathematics
2026
Dynamic temporal fusion graph neural network for spatio-temporal air quality inference
Amin Sheikhzadeh, Behzad Moshiri, Ebrahim Ghafar-Zadeh
Engineering Applications of Artificial Intelligence
2026
EEG Source Localization: FEM Forward Modeling and MLP-Based Inverse Modeling
Melisa Daryaeiheravi, Zakieh Hassanzadeh, Ashkan Mahdavian, Fariba Bahrami, Ahmad Kalhor, Behzad Moshiri
Zenodo (CERN European Organization for Nuclear Research)
Contact
Get in touch
For research enquiries, collaboration or supervision, email the laboratory directly.
Address
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran