Study at Mohamed bin Zayed University of Artificial Intelligence

Professor Eric Xing is the President of the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), the world's first graduate-level, research-based artificial intelligence (AI) university. In this role, he aims to build and enable a generation of leaders who have the necessary understanding, expertise, and skills to unlock AI's full potential through academic research and industry applications.

Prior to joining MBZUAI, Professor Xing was a Professor of Computer Science at Carnegie Mellon University, and the Founder, Chairman, and Chief Scientist of Petuum Inc., which was recognized as a 2018 World Economic Forum Technology Pioneer that builds standardized artificial intelligence development platform and operating system for broad and general industrial AI applications. Additionally, Prof. Xing is the Founding Director of the Center for Machine Learning and Health, Carnegie Mellon University, and University of Pittsburgh Medical Center. Professor Xing has also spent time as a Visiting Associate Professor at Stanford University, and as Visiting Research Professor at Facebook Inc.

Professor Xing is a recipient of the National Science Foundation (NSF) Career Award, the Alfred P. Sloan Research Fellowship in Computer Science, the United States Air Force Office of Scientific Research Young Investigator Award, the IBM Open Collaborative Research Faculty Award, as well as several best paper awards.

He is a board member of the International Machine Learning Society. He is a Fellow of the Association of Advancement of Artificial Intelligence (AAAI), and an Institute of Electrical and Electronics Engineers (IEEE) Fellow. In the past, he served as the Program Chair (2014) and General Chair (2019) of the International Conference of Machine Learning (ICML).

Professor Xing has authored or contributed to more than 370 cited research papers and reports. His research has been cited more than 35,000 times by leading academics and academic journals. His main research interests are in the development of machine learning and statistical methodology, and large-scale computational system and architecture, for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in artificial, biological, and social systems.

Professor Xing has served in numerous other roles in the past including: associate editor of the Journal of the American Statistical Association (JASA), Annals of Applied Statistics (AOAS), IEEE Journal of Pattern Analysis and Machine Intelligence (PAMI), and the PLoS Journal of Computational Biology; action editor of the Machine Learning Journal (MLJ) and Journal of Machine Learning Research (JMLR); member of the United States Department of Defense Advanced Research Projects Agency (DARPA) Information Science and Technology (ISAT) advisory group.

He holds a PhD in Molecular Biology and Biochemistry from the State University of New Jersey, and a PhD in Computer Science from the University of California, Berkeley. He completed his undergraduate study at Tsinghua University.

Before joining MBZUAI as Provost, Professor Fakhri served as the founding co-director of the University of Waterloo AI Institute. He has held the Loblaws Research Chair in Artificial Intelligence in the department of electrical and computer engineering of the University of Waterloo. He is the co-author of a textbook on applied artificial intelligence: Soft Computing and Intelligent Systems Design (Addison Wesley Publishing), and the co-author of an upcoming textbook on Elements of Dimensionality Reduction and  Manifold Learning (Springer). He has published extensively in the general field of pattern analysis and machine intelligence and has been issued 20 patents (US registered). He is the Associate Editor (AE) of some flagship journals in the field of AI and intelligent systems: the IEEE Transactions on Cybernetics, the IEEE Transactions on Neural Networks and Learning. He has served as the AE for the IEEE Transactions on Mechatronics, the IEEE Computational Intelligence Magazine, and IEEE Access. He also serves on the editorial board of the Elsevier Journal of Information Fusion, International Journal of Robotics and Automation, the Journal of Control and Intelligent Systems, and the Journal of Advances in Artificial Intelligence.

Professor Fakhri’s research interests are in the areas of operational artificial intelligence, cognitive machines, natural human-machine interaction, autonomous and intelligent systems. Applications of his research include cognitive and self-aware machines/robots/vehicles, predictive analytics in supply chain management and intelligent transportation systems. Recent work of Fakhri and his research team on deep learning-based driver behavior recognition and prediction has been featured on The Washington Post, Wired Magazine, Globe and Mail, CBC radio and Canada's Discovery Channel.

Professor Fakhri is the co-founder and Chief Scientist of Yourika.ai, a provider of next generation online learning systems. He is a Fellow of the IEEE, a Fellow of the Canadian Academy of Engineering, a Fellow of the Engineering Institute of Canada and the President of the Association for Image and Machine Intelligence. He served as a Distinguished Lecturer for the IEEE and is a Fellow of the Kavli Frontiers of Science (a major research and symposium program of the US National Academy of Sciences).

Prof. Le Song was an Associate Professor of Computational Science and Engineering and also the Associate Director of Center for Machine Learning at Georgia Institute of Technology in the USA. He holds the PhD in Computer Science from the University of Sydney and National ICT Australia. He taught in different educational institutions in the USA.

Prof. Le Song published more than 160 papers in peer-reviewed top machine learning conferences and journals such as NeurIPS, ICML, ICLR, AISTATS and JMLR over the past 15 years. He was invited as an Area Chair in many  international conferences. Prof. Le Sone is also an active member of several regional and international groups in the field of Machine Learning, Artificial Intelligence and Statistics, such as the board member of the International Conference on Machine Learning.

Prof. Le Song is an experienced educator and researcher in the field of Machine Learning and Artificial Intelligence. He received honors and awards for his research and articles as a Principal Investigator and co-Principal Investigator.

Prof. Le Song’s impressive portfolio includes his several years’ experience in various Institutes such as Georgia Institute of Technology, Google Research,  Carnegie Mellon University and National ICT Australia, he developed machine learning methods and algorithms for complex and dynamic data, machine learning and cross-campus multi-disciplinary research, large scale machine learning package for Internet data, nonparametric probabilistic graphical models for complex social and biological data and analyzed sensor time series data using kernel methods.

Prof. Le Song’s remarkable works won several best paper awards at the ACM Conference on Recommendation System (Recsys) in 2016, Artificial Intelligence and Statistics (AISTATS) in 2016, IEEE International Parallel & Distributed Processing Symposium (IPDPS) in 2015, Neural Information Processing Systems (NeurIPS) in 2013, and International Conference on Machine Learning (ICML) in 2010. He was also the recipient of the National Science Foundation CAREER Award in 2014, and Outstanding Junior Faculty Research Award in 2014 and Lockheed Martin Inspirational Young Faculty Award in 2014.

Professor Ling Shao is the Founding CEO and Chief Scientist of the Inception Institute of Artificial Intelligence (IIAI), Abu Dhabi, UAE. He received the B.Eng. degree in Electronic and Information Engineering from the University of Science and Technology of China (USTC), the M.Sc. degree in Medical Image Analysis and the Ph.D. (D.Phil.) degree in Computer Vision at the Robotics Research Group with Professor Sir Michael Brady from the University of Oxford. Previously, He was Chair Professor (2016-2018) in Computer Vision and Machine Learning and Director of the Artificial Intelligence Laboratory with the University of East Anglia, Norwich, Chair Professor (2014-2016) in Computer Vision and Machine Intelligence and Head of the Computer Vision and Artificial Intelligence Group with Northumbria University, Newcastle upon Tyne, a Senior Lecturer (2009-2014) with the University of Sheffield and a Senior Scientist (2005-2009) with Philips Research, The Netherlands. His research interests include Computer Vision, Deep Learning/Machine Learning, Medical Imaging, and Image/Video Processing. He has authored/co-authored over 400 papers in top conferences/journals such as ICCV, CVPR, ECCV, NeurIPS, TPAMI and IJCV and over 20 EU/US patents.

Ling Shao has been an Associate Editor of IEEE Transactions on Image Processing, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Cybernetics, and several other journals. He has edited four books and several special issues for journals such as IJCV. He has organized a number of international workshops with top conferences including ICCV, ECCV and ACM Multimedia. He was the General Chair for BMVC 2018, has been an Area Chair or Program Committee member for many conferences, including ICCV, CVPR, ECCV and ACM MM, and a reviewer for many leading journals. He was selected as a Highly Cited Researcher by the Web of Science in 2018 and 2019. He is a Fellow of the IAPR, a Fellow of the British Computer Society, a Fellow of the IET, a Senior Member of the IEEE, and a Life Member of the ACM. 

Mérouane Debbah is an Adjunct Professor with the Department of Machine Learning at MBZUAI. He is Chief Researcher at the Technology Innovation Institute in Abu Dhabi. He received the M.Sc. and Ph.D. degrees from the Ecole Normale Supérieure Paris-Saclay, France. He was with Motorola Labs, Saclay, France, from 1999 to 2002, and also with the Vienna Research Center for Telecommunications, Vienna, Austria, until 2003. From 2003 to 2007, he was an Assistant Professor with the Mobile Communications Department, Institut Eurecom, Sophia Antipolis, France. In 2007, he was appointed Full Professor at CentraleSupelec, Gif-sur-Yvette, France. From 2007 to 2014, he was the Director of the Alcatel-Lucent Chair on Flexible Radio. From 2014 to 2021, he was Vice-President of the Huawei France Research Center. He was jointly the director of the Mathematical and Algorithmic Sciences Lab as well as the director of the Lagrange Mathematical and Computing Research Center. Since 2021, and as  Chief Researcher at the Technology Innovation Institute in Abu Dhabi,  he is leading the AI & Telecom Systems center. He has managed 8 EU projects and more than 24 national and international projects. His research interests lie in fundamental mathematics, algorithms, statistics, information, and communication sciences research. He is an IEEE Fellow, a WWRF Fellow, a Eurasip Fellow, an AAIA Fellow, an Institut Louis Bachelier Fellow and a Membre émérite SEE. He was a recipient of the ERC Grant MORE (Advanced Mathematical Tools for Complex Network Engineering) from 2012 to 2017. He was a recipient of the Mario Boella Award in 2005, the IEEE Glavieux Prize Award in 2011, the Qualcomm Innovation Prize Award in 2012, the 2019 IEEE Radio Communications Committee Technical Recognition Award and the 2020 SEE Blondel Medal. He received more than 20 best paper awards, among which the 2007 IEEE GLOBECOM Best Paper Award, the Wi-Opt 2009 Best Paper Award, the 2010 Newcom++ Best Paper Award, the WUN CogCom Best Paper 2012 and 2013 Award, the 2014 WCNC Best Paper Award, the 2015 ICC Best Paper Award, the 2015 IEEE Communications Society Leonard G. Abraham Prize, the 2015 IEEE Communications Society Fred W. Ellersick Prize, the 2016 IEEE Communications Society Best Tutorial Paper Award, the 2016 European Wireless Best Paper Award, the 2017 Eurasip Best Paper Award, the 2018 IEEE Marconi Prize Paper Award, the 2019 IEEE Communications Society Young Author Best Paper Award, the 2021 Eurasip Best Paper Award, the 2021 IEEE Marconi Prize Paper Award as well as the Valuetools 2007, Valuetools 2008, CrownCom 2009, Valuetools 2012, SAM 2014, and 2017 IEEE Sweden VT-COM-IT Joint Chapter best student paper awards. He is an Associate Editor-in-Chief of the journal Random Matrix: Theory and Applications. He was an Associate Area Editor and Senior Area Editor of the IEEE TRANSACTIONS ON SIGNAL PROCESSING from 2011 to 2013 and from 2013 to 2014, respectively. From 2021 to 2022, he serves as an IEEE Signal Processing Society Distinguished Industry Speaker

Dr Najwa Aaraj is an Adjunct Professor in the Department of Machine Learning at MBZUAI.  She is the Chief Researcher at the Cryptography Research Centre at the Technology Innovation Institute (TII). Dr Aaraj leads the research and development of cryptographic technologies, including post-quantum cryptography (PQC) software libraries and hardware implementations, lightweight cryptographic libraries for embedded and RF systems, cryptanalysis, and applied machine learning for cryptographic technologies. She is also Acting Chief Researcher at TII’s Autonomous Robotics Research Centre (ARRC), which is dedicated to breakthrough developments in robotics and autonomy. 

Dr Aaraj earned a PhD with Highest Distinction in Applied Cryptography and Embedded Systems Security from Princeton University (USA). She has extensive expertise in applied cryptography, trusted platforms, security architecture for embedded systems, software exploit detection and prevention systems, and biometrics. She has over 15 years of experience with global firms, working in multiple geographies from Australia to the United States. 

Before joining TII, Dr Aaraj was Senior Vice President at DarkMatter, a cyber-security leader based in the UAE. She was formerly at Booz & Company, where she led consulting engagements in the communication and technology industry for clients globally. She also held positions at IBM T.J. Watson Security Research in New York State,  Intel in Portland, Oregon.

Dr Aaraj has written multiple conference papers, Institute of Electrical and Electronics Engineers (IEEE) and Association for Computing Machinery (ACM) journal papers and book chapters, and received patents on applied cryptography, embedded system security, and machine learning-based protection of Internet of Things (IoT) systems. 

Dr Aaraj is on the advisory board of New York-based NeuTigers, a leading-edge startup revolutionising the next generation of energy/latency-efficient artificial intelligence (AI). She is also a Board Member and adviser to multiple security and Machine Learning startups including Okinawa Graduate Institute of Science and Technology.

Dr Martin Takac was an Associate Professor in the Department of Industrial and Systems Engineering at Lehigh University, where he has been employed since 2014. He received his B.S. (2008) and M.S. (2010) degrees in Mathematics from Comenius University, Slovakia, and Ph.D. (2014) degree in Mathematics from The University of Edinburgh, United Kingdom. He received several awards during this period, including the Best Ph.D. Dissertation Award by the OR Society (2014), Leslie Fox Prize (2nd Prize; 2013) by the Institute for Mathematics and its Applications, and INFORMS Computing Society Best Student Paper Award (runner up; 2012).

Dr Martin’s is current research interests include the design and analysis of algorithms for machine learning, applications of ML, optimization, HPC. Martin received funding from various U.S. National Science Foundation programs, including through a TRIPODS Institute grant awarded to him and his collaborators at Lehigh, Northwestern, and Boston University. He currently serves as an Associate Editor for Mathematical Programming Computation, Journal of Optimization Theory and Applications, and Optimization Methods and Software and is an area chair at machine learning conferences like ICML, NeurIPS, ICLR, and AISTATS.

Fahad Khan is an Associate Professor in the Computer Vision department at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). He previously was a Lead Scientist at the Inception Institute of Artificial Intelligence (IIAI), Abu Dhabi, United Arab Emirates. Before joining the IIAI, he was a Senior Lecturer at Computer Vision Laboratory, Linkoping University, Sweden. He received an MSc degree in Intelligent Systems Design from Chalmers University of Technology, Sweden and a Ph.D. degree in Computer Vision from Autonomous University of Barcelona, Spain. From 2012 to 2014, he was a postdoctoral fellow at Computer Vision Laboratory, Linkoping University, Sweden. From 2014 to 2018, he was a research fellow at Linkoping University, Sweden. In 2016, he received the prestigious starting grant (acceptance rate 15%) worth 350K Euros from the Swedish Research Council. In 2018, he was awarded the Docent title (in Sweden, Docent is often translated as Associate Professor) in computer vision from Linkoping University, Sweden. He has achieved top ranks on various international challenges (Visual Object Tracking VOT: 1^st 2014, 2016 and 2018 and 2^nd in 2015; VOT-TIR: 1^st 2015 and 2016; OpenCV Tracking: 1^st 2015; 1^st PASCAL VOC Segmentation and Action Recognition tasks 2010). He received the best paper award in the computer vision track at IEEE ICPR 2016. He has published over 50 reviewed conference papers, journal articles, and book contributions, with over 6000 citations according to Google Scholar. His research interests include computer vision and machine learning. He serves as a regular program committee member for leading computer vision and artificial intelligence conferences such as IEEE CVPR, ICCV, and ECCV.

Karthik Nandakumar is an Associate Professor in the Computer Vision department at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). Prior to joining MBZUAI, he was a Research Staff Member at IBM Research – Singapore from 2014 to 2020 and a Scientist at Institute for Infocomm Research, A*STAR, Singapore from 2008 to 2014. He received his B.E. degree (2002) from Anna University, Chennai, India, M.S. degrees in Computer Science (2005) and Statistics (2007), and Ph.D. degree in Computer Science (2008) from Michigan State University, and M.Sc. degree in Management of Technology (2012) from National University of Singapore. His primary research interests include computer vision, machine learning, biometric recognition, applied cryptography, and blockchain. Specifically, he is interested in research on deep learning algorithms for biometrics and video surveillance applications as well as various security, privacy, and trust-related issues in machine learning. He has co-authored two books titled Introduction to Biometrics (Springer, 2011) and Handbook of Multibiometrics (Springer, 2006).

Karthik Nandakumar has received a number of awards including the 2008 Fitch H. Beach Outstanding Graduate Research Award from the College of Engineering at Michigan State University, the Best Paper award from the Pattern Recognition journal (2005), the Best Scientific Paper Award (Biometrics Track) at ICPR 2008, and the 2010 IEEE Signal Processing Society Young Author Best Paper Award. He is a Senior Area Editor of IEEE Transactions on Information Forensics and Security (T-IFS). He was also an Associate Editor of T-IFS from 2015 to 2019 and received the 2019 Outstanding Editorial Board Member award. He is also an Associate Editor for Elsevier journal on Pattern Recognition and a Distinguished Industry Speaker for the IEEE Signal Processing Society. In the recent past, he has served as Vice President for Education in the IEEE Biometrics Council and as an elected member of the IEEE Signal Processing Society Technical Committee on Information Forensics and Security. He is a senior member of the IEEE.

Huan Xiong is an Assistant Professor in the Machine Learning department at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). Prior to joining MBZUAI, he was a Research Scientist at the Inception Institute of Artificial Intelligence (IIAI), Abu Dhabi, United Arab Emirates. He received the B.S. and M.S. degrees from the School of Mathematical Sciences, Peking University, China, in 2010 and 2013, respectively, and the Ph.D. degree from the Institute of Mathematics, University of Zurich, Switzerland, in 2016. He was a postdoctoral researcher at the Institute for Advanced Mathematical Research, University of Strasbourg, France, from 2016 to 2018. He hosted two research projects funded by the Swiss National Science Foundation (SNSF) and the French National Centre for Scientific Research (CNRS) respectively. His research interests include machine learning and discrete mathematics.

Dr. Rao Muhammad Anwer is an Assistant Professor in the Computer Vision department at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). Prior to joining MBZUAI, he was at Inception Institute of Artificial Intelligence (IIAI) in Abu Dhabi working as Research Scientist. Before joining IIAI, he was a Post-Doctoral Research Fellow with Aalto University, Finland from 2014-2018. He received the master’s degree in intelligent systems design from the Chalmers University of Technology, Sweden, and the Ph.D. degree in computer vision from the Autonomous University of Barcelona, Spain. His research interests are in object detection, pedestrian detection and action recognition

Hisham Cholakkal is an Assistant Professor in the Computer Vision department at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). Prior to MBZUAI, Hisham worked as a Research Scientist at the Inception Institute of Artificial Intelligence (IIAI) in Abu Dhabi. He received PhD degree in Computer Vision from Nanyang Technological University, Singapore and M.Tech degree in Digital Signal Processing from Indian Institute of Technology Guwahati, India. He has six years of experience at various industrial and academic research labs. Before joining IIAI, he was a Senior Technical Lead in the Computer Vision and Deep learning Research team at Mercedes-Benz R&D, India. He has also worked as a computer vision researcher at BEL-Central Research Lab, India and Advanced Digital Sciences Center, Singapore. His research interests include object detection, instance segmentation, pedestrian detection, human object interaction, human pose estimation, crowd counting, image classification and weakly supervised learning.

Muhammad Haris Khan is an Assistant Professor in the Computer Vision department at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). Prior to MBZUAI, he was a Research Scientist in Inception Institute of Artificial Intelligence (IIAI), UAE. Before joining IIAI, he served as an Assistant Professor at Comsats University Islamabad (CUI), Pakistan for 2 years. Earlier, he was a Post-Doctoral fellow at the University of Nottingham (UoN), UK for 1.5 years. He obtained his PhD in Computer Vision from University of Nottingham (UoN), UK, and Masters from University of Sussex, UK. Prior to starting his PhD, he also served as a Lecturer for 3 years at CUI, Pakistan. He has authored and co-authored several papers in top-ranked computer vision conferences. His overall peer-reviewed research has over 1500 citations. His research interests span active topics in computer vision including face-related tasks, domain adaptation, visual tracking and object recognition. He is also a recipient of International Research Excellence Scholarship for his Doctoral study.

Dr. Hang Dai is an Assistant Professor in the Computer Vision department at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). Prior to joining MBZUAI, Dr. Dai worked as a research scientist at the Inception Institute of Artificial Intelligence (IIAI) in Abu Dhabi. He received his PhD degree from the Department of Computer Science at The University of York, U.K., in 2018. He was an Oversea Research Scholar with the University of York from 2015 to 2018. His research was funded by NHS, Google, Royal Academy of Engineering and the Leverhulme Trust and QIDIS from the National Commissioning Group. He served as a reviewer of ICCV, CVPR, ECCV and program committee member of AAAI. His research interests include geometric deep learning, deepfake detection, 3D computer vision.

Bin Gu is an Assistant Professor in the Machine Learning department at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). Prior to joining MBZUAI, he was a full professor of Nanjing University of Information Science and Technology. He received the B.S. and Ph.D. degrees in computer science from the Nanjing University of Aeronautics and Astronautics, in 2005 and 2011, respectively. He was a Post-Doctoral Fellow with the University of Western Ontario from 2013 to 2015, with the University of Texas at Arlington from 2016 to 2017, and the University of Pittsburgh from 2017 to 2018. His research interests focus on large scaling optimization in machine learning and data mining. He has published 40 more papers, with over 2,000 citations according to Google Scholar.  He served as a program committee member or reviewer for several leading machine learning and data mining conferences and journals such as NIPS, ICML, KDD, AAAI, TPAMI, JMLR, and a senior program committee member of IJCAI 2019 and 2020.

Dr Mohammad Yaqub is an Assistant Professor in the Computer Vision department at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). He is also an honorary research fellow at the Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.

Prior to joining MBZUAI, Dr Yaqub spent 12 years in Oxford, UK. He received his DPhil (PhD) in Biomedical Engineering from the University of Oxford in 2011. During his PhD, he was supervised by Prof Alison Noble, Dr Kassim Javaid and Prof Cyrus Cooper. He worked on developing machine learning solutions to real-world medical problems. After his PhD, he was a research fellow (2011 - 2016) at the Institute of Biomedical Engineering, University of Oxford and a lecturer (2010 - 2020) in the IT Services, University of Oxford.

He worked on several problems including fetal brain structure detection and segmentation in 2D and 3D ultrasound, fusion of 4D echocardiographic images, automatic categorization and quality checking of images from routine fetal anomaly scans. He has co-supervised PhD and master students during his work at Oxford.

Dr Yaqub was also the Vice President of Engineering (2016 - 2020) at Intelligent Ultrasound limited where he led a team to build AI-based medical device products. Dr Yaqub has taken his research ideas from its early days at the University of Oxford all the way to the integration of a medical product (ScanNav) into the ultrasound machines of a world-leading manufacturer.

Dr Yaqub’s research interests include medical image analysis (e.g, ultrasound, MRI and CT), deep learning, machine learning, and medical image registration. He has published over 35 peer-reviewed articles in top conferences and journals such as IEEE TMI, Medical Image Analysis, MICCAI and Ultrasound in Medicine and Biology. He has been granted multiple research grants and two patents. He is co-organizing the 24^th Medical Image Understanding and Analysis (MUIA) in summer 2020 at Oxford University.

Dr. Salman Khan is an Assistant Professor in the Computer Vision department at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). He is highly accomplished within his field, with research interests including computer vision, pattern recognition, and machine learning.

He has been a Senior Scientist with the Inception Institute of Artificial Intelligence since 2018, and an Honorary Lecturer with Australian National University since 2016. Previous roles include working as a Research Scientist with Data61-CSIRO (previously NICTA) from 2016-2018, and Visiting Researcher with National ICT Australia, CRL, in 2015.

In 2019, Dr. Khan was awarded the outstanding reviewer award at IEEE CVPR, won the best paper award at 9th ICPRAM 2020, and won second prize in the NTIRE Image Enhancement Competition alongside CVPR 2019.

Throughout his career he has acted as an investigator on several competitive research grants funded by agencies such as the Khalifa University Research Award in the UAE, Boeing in the US, and the Australian Renewable Energy Agency in Australia. Dr. Khan has also acted as a referee for international grant agencies such as the Australian and European Research Council (ARC and ERC), and IEEE Chair for Computer Society.

He has published over 80 papers in top scientific journals and conferences such as TPAMI, IJCV, CVPR, ICCV, ECCV, NeurIPS and AAAI.

Dr. Khan was a recipient of several prestigious scholarships, including Fulbright and IPRS, and served as a program committee member for several premier conferences including CVPR, ICCV, ECCV, ICRA, IROS, NeurIPS, AAAI and IJCAI.

Dr. Khan received his PhD degree from The University of Western Australia in 2016. His thesis received an honorable mention on the Dean’s List Award, and was nominated for the prestigious ‘Robert Street Prize’ at UWA as well as the ‘John Makepeace Bennett Award’ from CORE, Australia.

Dr Shangsong was an associate professor at the Sun Yat-sen University since 2018. Prior to that he was an research scientist at the King Abdullah University of Science and Technology from January 2018 to December 2018. He received his Ph.D. degree from the University of Amsterdam, The Netherlands, in 2014, supervised by Prof. dr. Maarten de Rijke, the Academician of the Royal Netherlands Academy of Art  and Sciences, in the field of computer science.

Dr Shangsong’s research interests lie in the field of Information Retrieval, Data Mining, Artificial Intelligence, and Machine Learning (especially, Deep Learning). He has published over 60 peer-reviewed papers, most of which are in top-tier venues such as SIGIR, KDD, NeurIPS, WWW, AAAI, IJCAI, WSDM, CIKM, TKDE, TOIS, and TKDD. He is an editor member of the journal of Information Processing & Management since 2016 and a Young Associate Editor-in-Chief of the Journal of Computer Science and Technology since 2021. He is PC member and reviewer in a number of conferences and journals such as SIGIR 2017-2021, KDD 2021, ICML 2021, NeurIPS 2020-2021, WWW 2018-2021, IJCAI 2018-2021 (IJCAI 2021 SPC), AAAI 2019-2021, TKDE, TKDD, and TOIS.

Dr Shangsong has received various awards/honors such as the SIGIR 2017 Outstanding Reviewer Award, Outstanding Contribution for instructing Data Mining course from the International Petroleum Engineers, the Kingdom of Saudi Arabia Section. He has been teaching a number of courses such as Introduction to Data Mining, Machine Learning and Data Mining, and Introduction to Information Retrieval.

Dr. Min Xu is an Affiliated Assistant Professor in Computer Vision Department at MBZUAI. He is also an Assistant Professor at the Computational Biology Department within the School of Computer Science at Carnegie Mellon University, USA. His career has centered on developing AI methods for the analysis of biomedical images and other biomedical data, in particular, Cellular Cryo-Electron Tomography (Cryo-ET) 3D image data. Dr. Xu has published over 70 research papers in prestigious peer-reviewed conferences and journals, such as CVPR, ICCV, ISMB, MICCAI, PNAS, Bioinformatics, PLOS Computational Biology, Structure, and JSB.

Dr. Xu received a B.E. in Computer Science from the Beihang University, M.Sc from the School of Computing at the National University of Singapore, M.A. in Applied Mathematics from the University of Southern California (USC), and Ph.D. in Computational Biology and Bioinformatics from USC. He was a postdoctoral researcher at USC.

Dr. Yuanzhi Li is joining MBZUAI as an Affiliated Assistant Professor in the Computer Vision department. He is an Assistant Professor at CMU, Machine Learning Department. Previously, he was a postdoctoral researcher at Stanford. He did his Ph.D. at Princeton under the advise of Sanjeev Arora (2014-2018) and his undergraduate study at Tsinghua university (2010-2014).

His primary research area is deep learning theory, focusing on (1). Understanding the hierarchical feature learning process in neural networks and how its better than shallow learning methods. (2). How the choice of optimization algorithms affects the training speed of different types of neural networks, and how it influences the generalization of the learned solution. (3). How to use pre-trained neural networks in downstream applications more effectively.

Dr. Zhiqiang was a senior research scientist with Baidu Research at China. He holds the PhD from the school of computer engineering, Nanyang Technological University, Singapore.

Dr. Zhiqiang research interests lie at the intersection of numerical computation, stochastic optimization, and Riemannian optimization. He is also interested in deep learning, clustering, community detection, topic modeling, etc. His recent ongoing works are about faster alternating least-squares for CCA, comprehensively tight analysis of gradient descent for PCA, accelerated inexact power methods, and Riemannian search for eigenvector computation.

Dr. Zhiqiang was serving as a reviewer for several academic activities of IJCAI, AAAI, ICLR, ICML, NeurIPS in various years. He also has industrial experiences about automatic optical inspection (AOI) for TFT-LCD panels and solar wafers, data analytics for airlines and insurances.