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 was the Dean of Computer Science , Data Science and Engineering at NYU Shanghai from 2013 to 2023. Previously he was a professor at University of Pennsylvania and a professor at Eurecom Institute. He holds a PhD from The University of Michigan.

He is co-author 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.

His current research interests are in AI, deep learning, and deep reinforcement learning. He has also worked in Internet privacy, peer-to-peer networking, and the modeling and measurement of computer networks.

He is an ACM Fellow and an IEEE Fellow.

Speech Title "Recent Advances in Deep Reinforcement Learning"

Abstract: Reinforcement learning is about learning to make sequential decisions through interactions with an environment. Traditional tabular reinforcement learning suffered from the curse of dimensionality and therefore was rarely employed in important applications. Recently reinforcement learning has been combined with deep learning to create Deep Reinforcement Learning (DRL), enabling researchers to break through the curse of dimensionality and obtain striking results in a wide range of diverse applications, including learning to play the Atari games from raw pixel inputs, learning to beat the grand masters at Go, learning to control tokamak plasmas for nuclear fusion, learning computationally efficient algorithms for matrix multiplication, making the responses in ChatGPT more human-like and accurate with RLHF, and more recently for helping LLMs to reason. In this talk, I will first briefly review some of the DRL breakthroughs, then discuss our own algorithmic research on DRL for high-dimensional state and action spaces, and its application to robotic locomotion.

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.