Mohammad Kazemi, PhD, FHEA, SMIEEE, is a Research Associate in Edge AI in the Department of Computing at Imperial College London and a member of the Adaptive Emergent Systems Engineering (AESE) Lab. He received his B.Sc. and M.Sc. degrees in Electrical Engineering from K.N. Toosi University of Technology and his Ph.D. in Electrical Engineering from Amirkabir University of Technology. Before joining Imperial's Department of Computing, he was a Marie Skłodowska-Curie Research Fellow in Imperial's Department of Electrical and Electronic Engineering and a Postdoctoral Researcher at Bilkent University. His research lies at the intersection of machine learning, information theory, wireless communications, and signal processing, with particular interests in Edge AI, distributed and federated learning, massive random access, communication-efficient learning, and coding-theoretic approaches to reliable AI. He has authored more than 50 peer-reviewed publications. He is an IEEE Senior Member and a Fellow of the Higher Education Academy. His distinctions include a Marie Skłodowska-Curie Actions Postdoctoral Fellowship, European Commission Seals of Excellence, and an IEEE IST Best Paper Award.
Mohammad Kazemi, PhD, FHEA, SMIEEE, is a Research Associate in Edge AI in the Department of Computing at Imperial College London and a member of the Adaptive Emergent Systems Engineering (AESE) Lab. He received his B.Sc. and M.Sc. degrees in Electrical Engineering from K.N. Toosi University of Technology and his Ph.D. in Electrical Engineering from Amirkabir University of Technology. Before joining Imperial's Department of Computing, he was a Marie Skłodowska-Curie Research Fellow in Imperial's Department of Electrical and Electronic Engineering and a Postdoctoral Researcher at Bilkent University. His research lies at the intersection of machine learning, information theory, wireless communications, and signal processing, with particular interests in Edge AI, distributed and federated learning, massive random access, communication-efficient learning, and coding-theoretic approaches to reliable AI. He has authored more than 50 peer-reviewed publications. He is an IEEE Senior Member and a Fellow of the Higher Education Academy. His distinctions include a Marie Skłodowska-Curie Actions Postdoctoral Fellowship, European Commission Seals of Excellence, and an IEEE IST Best Paper Award.