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M. Shamim Hossain, Highly Cited Researcher, SM'IEEE, Distinguished Member, ACM

Professor

Faculty

علوم الحاسب والمعلومات
Room No. 2115, Building No. 31

introduction/brief CV

                                                                                                      

M. Shamim Hossain is currently a professor at the Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia. He is also an adjunct professor with the School of Electrical Engineering and Computer Science, University of Ottawa, ON, Canada. He received his Ph.D. in Electrical and Computer Engineering from the University of Ottawa, ON, Canada in 2009. His research interests include cloud networking, smart environment (smart city, smart health), AI, deep learning, edge computing, Internet of Things (IoT), multimedia for health care, and multimedia big data. He has authored and co-authored more than 380 publications, including refereed journals (280+ SCI/ISI-indexed papers, 150+ IEEE/ACM Transactions/Journal papers, 23+ ESI Highly Cited Papers, 2 Hot Papers), conference papers, books, and book chapters. Recently, he co-edited a book on “Connected Health in Smart Cities”, published by Springer. He has served as the cochair, general chair, workshop chair, publication chair, and TPC at several IEEE and ACM conferences. He is the chair of the IEEE Special Interest Group on Artificial Intelligence (AI) for Health with the IEEE ComSoc eHealth Technical Committee. He is currently the organizing Co-Chair of the Special Sessions with IEEE I2MTC 2022. He serves as the co-chair of the 2nd IEEE GLOBECOM 2022 Workshop on Edge-AI and IoT for Connected Health. He is the Symposium Chair of Selected Areas in Communications (E-Health) with IEEE GLOBECOM 2024. He is the track chair of the IEEE International Conference on Consumer Electronics (ICCE 2024). He is the Technical Program Co-Chair of ACM Multimedia 2023. Currently, he is the Chair of the Saudi Arabia Section of the Instrumentation and Measurement Society Chapter. He was the recipient of a number of awards, including the Best Conference Paper Award, the 2024 IEEE Communications Society Outstanding Paper Award, the 2016 ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) Nicolas D. Georganas Best Paper Award, the 2019 King Saud University Scientific Excellence Award (Research Quality), and the Research in Excellence Award from the College of Computer and Information Sciences (CCIS), King Saud University (3 times in a row). He is on the editorial board of the IEEE Transactions on Instrumentation and Measurement (TIM), IEEE Transactions on Multimedia (TMM), ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), IEEE Multimedia, IEEE Network, IEEE Wireless Communications, Journal of Network and Computer Applications (Elsevier), International Journal of Multimedia Tools and Applications (Springer), and Games for Health Journal. He has served as a lead guest editor for more than two dozen Special Issues (SIs), including ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), ACM Transactions on Internet Technology, IEEE Transactions on Consumer Electronics, IEEE Communications Magazine, IEEE Network, IEEE Transactions on Information Technology in Biomedicine (currently JBHI), IEEE Transactions on Cloud Computing, International Journal of Multimedia Tools and Applications (Springer), Cluster Computing (Springer), Future Generation Computer Systems (Elsevier), Sensors (MDPI), and International Journal of Distributed Sensor Networks. He is a senior member of the IEEE and a Distinguished Member of the ACM. He is an IEEE Distinguished Lecturer (DL). He is the Highly Cited Researcher in the field of Computer Science – 2022 and 2023 (Web of Science™).

areas of expertise

Artificial Intelligence, deep learning, edge computing, Internet of Things (IoT), Cloud networking, smart environment (smart city, smart health), multimedia for health care

publications
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courses
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course

SWE 510: Machine Learning and Data Science Graduate Course

course

Graduate Course

course

SWE 505 Graduate Course