Marwan Krunz, PhD

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Biography
Dr. Krunz is a Regents Professor at the University of Arizona and the Edward & Maria Keonjian Endowed Chair in Electrical and Computer Engineering. He also holds a joint appointment as a Professor of Computer Science and is a member of the UA Cancer Center. He is the Deputy Center Director and Site Director of WISPER, an NSF/industry funded consortium of 3 universities and 12+ companies. WISPER’s industry-focused research aims to provide solutions for secure and AI-enabled NextGen wireless systems. Previously, Dr. Krunz directed two graduated NSF/industry centers: the Broadband Wireless Access and Applications Center (2013-2024) and ConnectionOne (2008-2013). Both centers focused on wireless systems and circuits, with engagement of tens of companies and government labs. Dr. Krunz holds courtesy appointments at University Technology Sydney and the University of Jordan. He previously held the Kenneth VonBehren Endowed Professorship in electrical and computer engineering. Dr. Krunz’s research is in the fields of AI, wireless communications, and security, with recent focus on applying machine learning techniques in telecommuncations and computational biology. He has published more than 350 journal articles and peer-reviewed conference papers, and is a named inventor on 13 patents. His latest h-index is 64. He is an IEEE Fellow, an Arizona Engineering Faculty Fellow, and an IEEE Communications Society Distinguished Lecturer (2013-2015). He received the NSF CAREER award. He served as the Editor-in-Chief for the IEEE Transactions on Mobile Computing. He also served as editor for numerous IEEE journals. He was the TPC chair for INFOCOM’04, SECON’05, WoWMoM’06, and Hot Interconnects 9. He was the general-co-chair for WiOpt 2023, the general vice-chair for WiOpt 2016, and general co-chair for WiSec’12. Dr. Krunz served as chief scientist/technologist for two startup companies that focus on AI and wireless technologies.
Cancer Focus
Dr. Krunz is applying AI/machine learning techniques in the study of single-cell cancer images and matching these images with gene expression profiles obtained from single-cell RNA sequencing techniques. Utilizing novel generative advesarial networks (GANs) and diffusion models, he is also developing AI-based technologies for enriching sparse gene expression profiles and devising ML tools for classifying relevant genes and predicting their role within heterogenous cell subpopulations under different stimuli and therapeutic treatments.