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.