Allahyar Montazeri

Co-Chair of the Future Mobility Grand Challenge Research Committee

Allahyar Montazeri is currently an Associate Professor at the School of Engineering at Lancaster University. Before this, he was appointed as Assistant Professor in Control and Electronics Engineering with the Engineering Department at Lancaster University. Between 2010 and 2011, he joined the Fraunhofer Institute, Germany as a research fellow, and then carried on his research with Fraunhofer IDMT and Control Engineering Group at Ilmenau University, Germany during the years 2011 to 2013.

He has been a visiting research scholar with the Control Engineering Group at ETH Zurich, Switzerland, and the Chemical Engineering Group at the Norwegian University of Science and Technology (NTNU), Trondheim, Norway. Dr Montazeri is the recipient of the European Research Consortium on Informatics and Mathematics (ERCIM) and Humboldt Research awards in 2010 and 2011, respectively. He is also a Fellow of the Higher Education Academy and serves on IFAC Technical Committees ’Adaptive and Learning Systems’ and ’Modelling, Identification, and Signal Processing’.

He is currently the Associate Editor of the journal Frontiers in Robotics and AI, Science Progress, and an editorial board member of Automation MDPI and Computational Intelligence in Electrical Engineering. He has been an international program committee member of several IEEE affiliated conferences and has organized various invited sessions and special issues for journals such as Electronics MDPI and IFAC MIM 2019 and 2022 conferences. His research is funded by different councils and industries in the UK such as the Engineering and Physical Research Council (EPSRC), Sellafield Ltd, National Nuclear Laboratory (NNL), and Nuclear Decommissioning Authority (NDA).

His research interests cover a wide range of areas on control theory and digital signal processing. Particularly he is interested in Adaptive Signal Processing and Control, Nonlinear Robust Control, Linear and Nonlinear System Identification, Estimation Theory, and Machine Learning techniques for optimization with applications in Robotics and Autonomous Systems as well as Active Noise and Vibration Control Systems.