Prof. Fakhri Karray
IEEE Fellow
University of Waterloo, Canada

Fakhri Karray is the founding co-director of the University of Waterloo Artificial Intelligence Institute and is the Loblaws Research Chair in Artificial Intelligence in the Department of electrical and computer engineering at the University of Waterloo, Canada. He is also a Professor of Machine Learning and the former Provost at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), a graduate-level, research-based artificial intelligence (AI) university, in Abu Dhabi, UAE. Fakhri’s research interests are in the areas of operational AI, cognitive machines, natural human-machine interaction, and autonomous and intelligent systems. Applications of his research include virtual care systems, cognitive and self-aware machines/robots/vehicles, predictive analytics in supply chain management and intelligent transportation systems. He serves as Associate Editor and member of the editorial board of major publications in smart systems and information fusion.

His most recent textbook in foundational machine learning “Elements of Dimensionality Reduction and Manifold Learning” was published by Springer Nature in February 2023. He was honored in 2021 by the IEEE Vehicular Technology Society (VTS) with the IEEE VTS Best Land Transportation Paper Award for his pioneering work on improving traffic flow prediction with weather Information in connected cars using deep learning and AI. His recent work on federated learning in communication systems earned him and his co-authors the 2022 IEEE Communication Society’s MeditCom Conference Best Paper Award. Fakhri is a Fellow of the IEEE, a Fellow of the Canadian Academy of Engineering, a Fellow of the Engineering Institute of Canada. He served as a Distinguished Lecturer for the IEEE and a Kavli Frontiers of Science Fellow. Fakhri received the Ing. Dip degree in electrical engineering from the School of Engineering of the University of Tunis,Tunisia and the Ph.D. degree from the University of Illinois Urbana-Champaign, USA.

Prof. Keith W. Ross
IEEE Fellow & ACM Fellow
NYU Abu Dhabi, UAE

Keith Ross is a Professor of Computer Science at NYU Abu Dhabi. He is an ACM Fellow and an IEEE Fellow. He is also the recipient of several prestigious best paper awards, and his work has been featured in the mainstream press, including New York Times, NPR, Bloomberg Television, Huffington Post, Fast Company, Ars Technia, and the New Scientist.

He joined NYU Abu Dhabi in September 2023. Previously he was the Dean of Computer Science, Data Science, and Engineering at NYU Shanghai (10 years) and the Leonard J. Shustek Professor at NYU Tandon (10 years). Before that he was a professor at University of Pennsylvania (13 years) and a professor at Eurecom Institute (5 years). He received a Ph.D. in Computer and Control Engineering from The University of Michigan.

His current research interests are in AI, specifically reinforcement learning and deep learning. He has also worked in Internet privacy, peer-to-peer networking, Internet measurement, stochastic modeling of computer networks, queuing theory, and Markov decision processes. For the past several years he has been teaching courses on AI, Machine Learning, and Reinforcement Learning.

He is co-author (with James F. Kurose) of the popular textbook, Computer Networking: A Top-Down Approach Featuring the Internet, published by Pearson (first edition in 2000, eighth edition 2020). It is the most popular textbook on computer networking, both nationally and internationally, and has been translated into fourteen languages. Professor Ross is also the author of the research monograph, Multiservice Loss Models for Broadband Communication Networks, published by Springer in 1995. In 1999, he co-founded and led Wimba, which developed voice and video applications for online learning. He was the Wimba CEO and CTO from 1999 to 2001. Wimba was acquired by Blackboard in 2010.

Prof. Xianghua Xie
Swansea University, UK

Professor Xianghua Xie is currently leading a research team on Computer Vision and Machine Learning (http://csvision.swan.ac.uk) in the Department of Computer Science, Swansea University. He was a recipient of an RCUK Academic Fellowship (tenure-track research focused lectureship) between September 2007 and March 2012. He was appointed as a Senior Lecturer from October 2012, then an Associate Professor in April 2013, and a full Professor from March 2019. Prior to his position at Swansea, He was a Research Associate at the Computer Vision Group, Department of Computer Science, University of Bristol, where he completed both his PhD (2006) and MSc (2002) degrees.

Professor Xie has strong research interests in the areas of Pattern Recognition and Machine Intelligence and their applications to real-world problems. He has been an investigator on several research projects funded by external bodies, such as EPSRC, Leverhulme, NISCHR, and WORD. Among his research works, those of significant importance include detecting abnormal patterns in complex visual and medical data, assisted diagnosis using automated image analysis, fully automated volumetric image segmentation, registration, and motion analysis, machine understanding of human action, efficient deep learning, and deep learning on irregular domains. By 2020, he has published over 150 fully refereed research publications and (co-)edited several conference proceedings. He is an associate editor of IET Computer Vision and an editorial member of a number of other international journals and has chaired and co-chaired several international conferences, e.g. BMVC2015 and BMVC2019.