Mohamed Akrout
 |
Assistant Professor
Office: Min H. Kao Building |
About me
I am an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee. I received my PhD in Electrical and Computer Engineering in 2024 from the University of Manitoba. I received my BE in Computer Engineering from Ecole Polytechnique de Montreal and Telecom ParisTech, and my MS in Applied Computer from the University of Toronto. After my PhD, I spent a year visiting the Technical University of Munich (TUM) and the University of California San Diego (UCSD). I was awarded the doctoral scholarship and the postdoctoral fellowship from the Natural Sciences and Engineering Research Council of Canada (NSERC), in 2022 and 2024, respectively.
Research interests
My research interests include signal processing for inverse problems and physically consistent antenna design. My research focuses on application of principles and tools in optimization and control to the study of requirement-driven machine decision-making. I am particularly interested in how constraints, arising from system requirements, shape decision-making in artificial intelligence systems. My current research interests include:
Scaling beyond 5G (B5G) communication systems: I study MIMO communication under physical constraints. The major difficulty of this research is that the performance criterion of interest (i.e., the achievable rate) emanates from communication theory while the physical constraints (i.e., antenna size and mutual coupling) arise from antenna theory. In other words, optimizing the achievable rate of MIMO systems while accounting for their physical constraints is a problem that lies at the interface of communication and antenna theories. For this reason, I am interested in combining Shannon and Maxwell theories using circuit-based physically consistent models which connect the physics of antennas with the mathematics of communication systems.
Artificial intelligence for healthcare: I am particularly interested in optimizing the patient-clinician cycle using artificial intelligence, where agents are designed to deliver accurate second-opinion diagnoses for doctors and optimize their precious time to provide a prescription. In this context, I helped creating AIP Labs, a digital health & AI company in Europe focused on dermatology at which I still advise from time to time. By visiting the AIP digital clinic, you can get a dermatology diagnosis, a treatment plan, and a prescription within a few hours or less, all reviewed by both a EU certified dermatologist.
For prospective students
I am looking for highly motivated PhD students with a strong background in mathematics and/or programming. If you are interested in signal processing or wireless communication, and their interplay with the areas of optimization and learning, I encourage you to apply here and send me an email.