Study at Mohamed bin Zayed University of Artificial Intelligence

Professor Fakhreddine (Fakhri)  Karray is the Provost of the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), a  first-of-its-kind graduate level, research-based artificial intelligence (AI) university. Before joining MBZUAI, 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 at  the University of Waterloo, Canada. Fakhri’s research interests are in the areas of operational AI, cognitive machines, natural human-machine interaction, 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. 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. He was recently honored by the IEEE Vehicular Technology Society (VTS) for his novel work on improving traffic flow prediction using weather Information in connected cars through deep learning and tools of AI and received the Society’s 2021 Best Land Transportation Paper Award.

Fakhri is the co-author of a textbook on applied artificial intelligence: Soft Computing and Intelligent Systems Design (Pearson Education 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 is the author of 20 US registered patents. He is the Associate Editor (AE) of flagship journals in the field of AI and intelligent systems, including  the IEEE Transactions on Cybernetics, the IEEE Transactions on Neural Networks and Learning Systems and the IEEE Computational Intelligence Magazine. He served as the AE and Guest Editor for the IEEE Transactions on Mechatronics, the IEEE Computational Intelligence Magazine and IEEE Access (special issue on IoMT). He also serves on several editorial boards of AI-related journals and has served as the General Chair/Program Chair for several international conferences in the field of intelligent systems.  Fakhri is the co-founder and Chief Scientist of Yourika.ai, a provider of AI based 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 a Fellow of the Kavli Frontiers of Science (a renowned research and symposium program of the US National Academy of Sciences). He received his PhD from the University of Illinois Urbana-Champaign, USA, and completed his undergraduate engineering degree at the National Engineering School of Tunis, Tunisia.

Prof. Timothy Baldwin is joining MBZUAI as a Professor, Associate Provost for Academic and Student Affairs, and Acting Department Chair of Natural Language Processing.

Prior to joining MBZUAI, Prof. Baldwin spent 17 years at The University of Melbourne, including roles as Melbourne Laureate Professor, Director of the ARC Training Centre in Cognitive Computing for Medical Technologies (in partnership with IBM), Associate Dean Research Training in the Melbourne School of Engineering, and Deputy Head of the Department of Computing and Information Systems. He has previously held visiting positions at Cambridge University, University of Washington, University of Tokyo, Saarland University, NTT Communication Science Laboratories, and National Institute of Informatics. His primary research focus is on natural language processing (NLP), including deep learning, algorithmic fairness, computational social science, and social media analytics.

Tim completed a BSc(CS/Maths) and BA(Linguistics/Japanese) at The University of Melbourne in 1995, and an MEng(CS) and PhD(CS) at the Tokyo Institute of Technology in 1998 and 2001, respectively. Prior to joining The University of Melbourne in 2004, he was a Senior Research Engineer at the Center for the Study of Language and Information, Stanford University (2001-2004). He is the author of over 400 peer-reviewed publications across diverse topics in natural language processing and AI.

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.

Before joining MBZUAI, Professor Abdulmotaleb El Saddik served as a Distinguished University Professor and University Research Chair in the School of Electrical Engineering and Computer Science at the University of Ottawa. He is an internationally recognized scholar who has made strong contributions to the knowledge and understanding of intelligent multimedia computing, communications, and applications. His research focus is on the establishment of digital twins to enhance the quality of life of citizens using artificial intelligence (AI), Internet of Things (IoT), SN, augmented reality (AR)/virtual reality (VR), haptics and 5G to allow people to interact in real-time with one another as well as with their smart digital representations in a secure manner.
 
He is the designated Editor-in-Chief of the ACM Transactions on Multimedia Computing, Communications and Applications (ACM TOMM), Senior Associate Editor of IEEE Multimedia (IEEE MM), and Guest Editor for several transactions and journals. He has co-authored 10 books and more than 600 publications as well as chaired more than 50 conferences and workshops. He has supervised more than 150 researchers. El Saddik has received research grants and contracts totaling more than $20 million. He is the author of the book Haptics Technologies: Bringing Touch to Multimedia.
 
El Saddik is a Fellow of the Royal Society of Canada, Fellow of IEEE, Fellow of the Canadian Academy of Engineering and Fellow of the Engineering Institute of Canada. He is an ACM Distinguished Scientist and has received several awards, including the Friedrich Wilhelm Bessel Award from the German Humboldt Foundation, the IEEE Instrumentation and Measurement Society Technical Achievement Award. He also received IEEE Canada C.C. Gotlieb (Computer) Medal and A.G.L. McNaughton Gold Medal for important contributions to the field of computer engineering and science and the IEEE TCSC Achievement Award for Excellence in Scalable Computing.
 

Sir Michael Brady was Associate Director of the AI Laboratory at MIT from 1980-1985, leaving to take up the newly created Professorship of Information Engineering, University of Oxford, which he held from 1985-2010.  Upon “retirement”, he was appointed Emeritus Professor of Oncological Imaging in the Department of Oncology of the University of Oxford.  He is the author of over 500 articles and 50 patents in computer vision, robotics, medical image analysis, and artificial intelligence, and the author or editor of ten books.  He has been elected a Fellow of the Royal Society, Fellow of the Royal Academy of Engineering, Membre Associé Étranger of the Académie des Sciences, Honorary Fellow of the Institution of Engineering and Technology, Fellow of the Institute of Physics, Fellow of the Academy of Medical Sciences, and Fellow of the British Computer Society.  He was awarded the Royal Academy of Engineering Whittle Medal, the IET Faraday Prize, and the Henry Dale Prize.  Sir Michael has founded successful spin-out companies, primarily in medical image analysis: Volpara Healthcare Technologies; Perspectum Diagnostics; ScreenPoint Medical bv; Optellum; Mirada Medical; and formerly Mirada Solutions; and the Guidance Group (Monitoring, Industrial, and Marine Divisions).  He was a Director of Oxford Instruments plc from 1995-2014.

Professor Michael I. Jordan joins MBZUAI as a Laureate Professor and the first Honorary Program Director of the MBZUAI Laureate Faculty Program (LFP). 

Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. He is also a member of the advisory committee for the Alibaba Academy for Discovery, Adventure, Momentum and Outlook (DAMO), which is dedicated to exploring the unknown through scientific and technological research and innovation.

Jordan has been awarded an Honorary Doctorate from Yale University. He is the 2020 IEEE John von Neumann Medal winner. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Award in 2015 and the ACM/AAAI Allen Newell Award in 2009. He has also received the Ulf Grenander Award in Stochastic Theory and Modeling, David E. Rumelhart Prize, ACM-AAI Allen Newell Award, and has contributed to several renowned published research papers.

He is a Fellow of the Association of Advancement of Artificial Intelligence (AAAI), ASA, CSS, IEEE, IMS, Bayesian Analysis (ISBA) and Society for Industrial & Applied Mathematics (SIAM). He holds a Ph.D. in cognitive science from the University of California in San Diego, a master’s in mathematics, from Arizona State University, and a bachelor’s in science in psychology from Louisiana State University.

A celebrated academic and thought leader, Jordan was one of 16 eminent faculty in artificial intelligence and machine learning to take part in the university's first Executive Program in 2021.

Prof. Mohsen Guizani (IEEE S’85–M’89–SM’99–F’09) brings an impressive portfolio of academic and administrative experience of higher education to MBZUAI. 
Prior to joining MBZUAI, Prof. Guizani has served in multiple administrative positions in the USA and the Gulf region, such as the Founding Associate Vice President for Graduate Studies at QU, Chair of the ECE Department at the University of Idaho, Chair of the Computer Science Department at Western Michigan University and Professor at the University of Missouri. He received his BS (with distinction), MS and PhD degrees from Syracuse University, Syracuse, New York, USA.
Prof. Guizani is a highly experienced educator and researcher in the field of applied machine learning and artificial intelligence, Internet of Things (IoT), intelligent systems, smart city and cybersecurity. He received many honors and multiple awards for his research and teaching and more than 30M US$ of research grants as a Principal Investigator (PI) and Co-principal Investigator (Co-PI). He has published extensively in high impact journals and conferences. He has authored/co-authored 10 books and more than 800 technical papers in top journals and conferences and has been granted more than 10 US Patents. He was elevated to the IEEE Fellow in 2009 for his contribution to “quality of service in broadband and ad hoc wireless networks.” He is a highly cited researcher and was listed as a Clarivate Analytics Highly Cited Researcher in Computer Science in 2019, 2020 and 2021. Prof. Guizani has won several research awards including the “2015 IEEE Communications Society Best Transaction Paper Award” as well four Best Paper Awards from top conferences, such as IEEE ICC and IEEE Globecom. He is also the recipient of the 2017 IEEE Communications Society Wireless Technical Committee (WTC) Recognition Award, the 2018 Ad-Hoc Technical Committee Recognition Award, and the 2019 IEEE Communications and Information Security Technical Recognition (CISTC) Award. 
Prof. Guizani has been very active in organizing highly ranked international conferences. He served as the Editor-in-Chief of IEEE Network and is currently serving on the Editorial Boards of many IEEE Transactions and Magazines. He is a Senior Technical Editor of the IEEE Network and an Advisory Board Editor of the IEEE Internet of Things Journal. He was the Chair of the IEEE Communications Society Wireless Technical Committee and the Chair of the TAOS Technical Committee. He served as the IEEE Computer Society Distinguished Speaker and is currently the IEEE Communication Society Distinguished Lecturer.
Prof. Guizani is the founder of the IEEE International Conference of Wireless Communications and Mobile Computing (IWCMC), the founder of WISE (a non-profit organization for education) and established three new journals. 

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.

Dr. Shehata has over seventeen years of research and development experience in operational Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP). His experience is a balanced combination of theory and practice. While He has developed strong theoretical experience in the foundations of machine learning algorithms, he has also developed comprehensive professional practice experience in building and deploying novel ML models in production.

Dr. Shehata is a highly sought-after speaker and contributor to the research community of AI, EdTech, EDUCAUSE, conferences, and journals. Dr. Shehata was the Learning Analytics LAK - Practitioner Program Chair of the International Conference on Learning Analytics & Knowledge in 2017. Dr. Shehata was nominated for the SOLAR executive committee. He earned his Ph.D. at the University of Waterloo in machine learning and natural language understanding a decade ago where he developed awarding-winning research and authored patents. His research work in the areas of machine learning and artificial intelligence has been recognized and published in top conferences, journals, and patents including IEEE TKDE, Computational Intelligence, Springer KAIS, ACM KDD, IEEE ICDM, IEEE / WIC / ACM WI, Springer ADMA, and SDM. Dr. Shehata is a member and a technical reviewer for Learning Analytics and Knowledge (LAK), IEEE Transactions on Cybernetics, ACM Transactions on Knowledge Discovery from Data (TKDD), IEEE Transactions on Knowledge and Data Engineering (TKDE), Data and Knowledge Engineering, Elsevier, IEEE Transactions on Systems, Man, and Cybernetics, Part B, IEEE Control Systems Society Conference Management System, and IBM Centers for Advanced Studies: CASCON Conference Before joining MBZUAI, Dr. Shehata joined Desire2Learn (D2L), a worldwide leading Learning Management System, where he spent 10 years leading the research and development of machine learning and data mining algorithms in production. He spent years developing and scaling robust software platforms and introduced new methodologies and systems to enable advanced predictive modeling against petabyte-scale data in IP AI-based products. He led the data science and business intelligence teams at D2L to adopt a data-driven platform and culture to make an impactful change in decision-making processes to enable descriptive, predictive, and prescriptive analytics. Dr. Shehata Led the engineering charge on AI and Big data analytics architecture and evangelizes his research and development through journals, conference presentations, demos, and patents where he provided thought leadership. Dr. Shehata is the Co-founder and CTO of YOURIKA company where he leads the engineering, research, and development efforts of artificial intelligence platform that provides a personalized learning experience to students based on measuring their understanding using novel IP deep learning models.

Dr. Shehata’s broad experience allowed him to contribute to research and develop new achievements in the AI discipline; publish world-class manuscripts; author patents, develop research projects with his industrial network connections with Amazon, Microsoft, and Google; and expose the state-of-the-art advances in AI through these projects.

Dr. Shahrukh Hashmi has been at Mayo Clinic for approximately 10 years. He started Mayo Clinic’s first BMT survivorship program. He has served as PI or Co-PI on many industry-sponsored and NIH sponsored trials. His research interests include premature aging and GVHD. He is also involved in stem cell therapeutics particularly in regenerative hematology. He has authored >200 articles in peer-reviewed journals, including in JAMA, Lancet, and in NEJM. He chairs many national or international professional committees/groups including being the chair of the Worldwide Network for Blood and Marrow Transplant’s Nuclear Accident committee (Geneva, Switzerland), founding chair of American Society for Blood and Marrow Transplant Society’s Survivorship SIG (Chicago, Illinois), and co-chair of the Center for International Blood & Marrow Transplant Registry’s (CIBMTR) Health Services Committee (Milwaukee, Wisconsin). He is currently the chair of the Dept. of Hematology/Oncology at the SSMC, Abu Dhabi, UAE; and the chair of SEHA Oncology Council.

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.

Dr. Salman Khan is an Associate 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.

Kun Zhang has joined MBZUAI as Associate Professor in the Machine Learning Department. He also maintains an associate professorship at Carnegie Mellon University. His research interests lie in machine learning and artificial intelligence, especially in causal discovery and causality-based learning. He develops methods for automated causal discovery or causal representation learning from various kinds of data, investigates learning problems including transfer learning, representation learning, and deep learning from a causal view, and studies philosophical foundations of causation and various machine learning tasks. On the application side, he is interested in neuroscience, computer vision, computational finance, and climate analysis.

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. Qirong Ho joined MBZUAI as an Assistant Professor in the Machine Learning Department and the Acting Manager of the Center for Integrative Artificial Intelligence. 

Dr. Qirong Ho is Co-Founder and CTO at Petuum Inc., an AI startup recognized as one of the World Economic Forum's Tech Pioneers. Petuum creates the standardized building blocks for assembling AI affordably and sustainably. Dr. Ho holds a Ph.D from Carnegie Mellon University, and is part of the Technical Committee for the CASL (Composable, Automatic and Scalable ML) open-source consortium. Prior to his work at Petuum, Dr. Ho provided key Artificial Intelligence and Machine Learning infrastructure solutions to technology, banking and telecommunications companies.

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.

Dr. Pengtao Xie joined MBZUAI as an Assistant Professor (Adjunct) in the Department of Machine Learning. is an assistant professor at UC San Diego. He received his PhD from the Machine Learning Department at Carnegie Mellon University in 2018. His research interests are machine learning inspired by humans’ learning skills (especially classroom learning skills), such as learning by progressive examination, interleaving learning, small-group learning, learning by teaching, etc., and their applications in healthcare. He serves as area chair for ICML, CVPR, ICCV, NAACL, etc. His PhD thesis was selected as a top-5 finalist for the AMIA Doctoral Dissertation Award. He is the recipient of the Amazon AWS Faculty Award, Tencent AI-Lab Faculty Award, Tencent WeChat Faculty Award, the Innovator Award presented by the Pittsburgh Business Times, the Siebel Scholars award.

Research interests

Lu’s research focuses on computer vision and sensing, image and video analytics, and deep learning. He has been working on scene text detection and recognition for years, contributing to a number of impactful benchmarking datasets as well as innovative detection and recognition techniques. In recent years, Lu has been studying how to tackle data collection and data annotation challenges in deep network training.

Bio

Lu is currently an associate professor (tenured) in the School of Computer Science and Engineering, Nanyang Technological University (NTU). He has held an adjunct position at MBZUAI since late 2021. Before joining NTU in 2017, he took a number of leadership roles in the Institute for Infocomm Research (I2R), Agency for Science, Technology, and Research (A*SATR) in Singapore, including head of the Visual Attention Lab, deputy head of the Satellite Department, co-chair of the Image and Pervasive Access Laboratory (a CNRS overseas laboratory hosted by A*STAR in Singapore). Lu’s research focuses on computer vision and sensing, image and video analytics, and deep learning. He won a number of international benchmarking competitions such as DIBCO2009, H-DIBCO2010, DIBCO2013, RRC2013, ANWRESH-2014, etc.

Education

• Ph.D. in electrical and computer engineering from the National University of Singapore
• Master’s degree in electrical engineering from the Xi’an Jiaotong University, China

Research

Lu has authored or co-authored more than 200 academic papers, and has filed up to 10 patents in the US and EU. His developed technology has been successfully licensed to industry and deployed in daily operations.

• Guan, D., Huang, J., Xiao, A., & Lu, S. (2022). Unbiased subclass regularization for semi-supervised semantic segmentation. CVPR.
• Zhan, F. Z., Zhang, J., Yu, Y., Wu, R., & Lu, S. (2022). Modulated contrast for versatile image translation. CVPR.
• Zhang, G., Luo, Z., Yu, Y., Cui, K., & Lu, S. (2022). Accelerating DETR convergence via semantic-aligned matching. CVPR.
• Zhang, J., Huang, J., Tian, Z. T., & Lu, S. (2022). Spectral unsupervised domain adaptation for visual recognition. CVPR.
• Zhou, C., Luo, Z. L., Luo, Y., Liu, T., Pan, L., Cai, Z., Zhao, H., & Lu, S. (2022). PTTR: Relational 3d point cloud object tracking with transformer. CVPR.
• Xue, C., Tian, Z., Zhan, F., Lu, S., & Bai, S. (2022). Fourier document restoration for robust document dewarping and recognition. CVPR.

Accolades

• Senior Program Committee (SPC) of the International Joint Conference on Artificial Intelligence (IJCAI18 – IJCAI21)
• Senior Program Committee (SPC) of the AAAI Conference on Artificial Intelligence (AAAI18 – AAAI21)
• Area Chair of 15th International Conference on Document Analysis and Recognition (ICDAR-19)
• Area Chair of IEEE Winter Conference on Applications of Computer Vision (WACV-17 and WACV-18)
• Area Chair of 14th IAPR International Conference on Document Analysis and Recognition (ICDAR-17) 
• Top winner of the ICFHR2014 Competition on Word Recognition from Historical Documents
• Top winner of the ICDAR 2013 Robust Reading Competition (1^st in the scene text segmentation task)
• Top winner of the ICDAR 2013 Document Image Binarization Contest (DIBCO 2013)
• Top winner of the H-DIBCO 2010 – Handwritten Document Image Binarization Competition
• Top winner of the ICDAR 2009 Document Image Binarization Contest (DIBCO 2009)

Xia is a Global Network Assistant Professor in Computer Science at New York University, Shanghai. He also holds affiliations at Tandon, CILVR at the Center for Data Science, and MARL at Steinhardt. He received his Ph.D. in the machine learning department at Carnegie Mellon University (CMU) in 2016, and he was a Neukom Fellow at Dartmouth from 2016 to 2017.

Xia is also a professional Di and Xiao (Chinese flute and vertical flute) player. He plays as a soloist in the NYU Shanghai Jazz ensemble, Pitt Carpathian Ensemble, and Chinese Music Institute of Peking University.

His research is very interdisciplinary. He is broadly interested in the design of interactive intelligent systems to extend human musical creation and expression. This research lies in the intersection of machine learning, HCI, robotics, and computer music. Some representative works include interactive composition via style transfer, human-computer interactive performances, autonomous dancing robots, large-scale content-based music retrieval, haptic guidance for flute tutoring, and bio-music computing using slime mold.