Abstract: Both Machine Learning (ML) and edge computing are making profound impact in several domains, however when they combine, they can bring a more intriguing user experience. The intersection of both has a range of applications in several areas such as automotive (e.g. autonomous cars), healthcare (e.g. remote monitoring using wearable/implanted devices, real-time patient data analysis, remote emergency surgeries), manufacturing (e.g. predictive maintenance), retail (e.g. VR/AR-aided shopping experience) and connected homes (e.g. temperature control, smart doorbells, access control, smart lighting). In this talk, we shall explore how ML-assisted mobile edge computing are creating new possibilities and what are the associated challenges? A brief overview of cutting-edge research directions is also provided.

Biography: Muhammad Asif Khan received B.Sc. degree in Telecommunication Engineering from University of Engineering and Technology Peshawar, Pakistan (2009) and M.Sc. in Telecommunication Engineering from University of Engineering and Technology Taxila, Pakistan (2013). He received the Ph.D. degree in Electrical Engineering from Qatar University in 2020. He was a Researcher Assistant at Qatar University (2014-2015) and at Qatar Mobility Innovation Center (2016-2017). He is currently working as a postdoctoral researcher at Qatar University. He is a Senior Member of IEEE and Member of IET. He has served as a TPC member of several international conferences including IEEE ICC 2021, IEEE CNCC 2021, IEEE BigData2020, IEEE ICASSP2020, IEEE SusTech2020 and IEEE SusTech2021. His current research interests include mobile edge computing, deep learning and distributed optimization. For more detailed information, please visit his homepage: http://www.asifk.me.