Invited Speakers
Invited Speakers (Alphabetize by Last Name)
Mr. Ahmet Tugrul Bayrak
Ata Technology Platforms, Turkey
Ata Technology Platforms, Turkey
Ahmet Tugrul Bayrak is the Data Science Manager at Ata Technology Platforms in Turkey. He holds a Bachelor of Science degree in Mathematical Engineering from Yildiz Technical University and another in Computer Engineering from The Technological University of the Shannon. Additionally, he attended Uppsala University for a Master's degree in Computer Science. With a career rooted in software engineering and data science, Mr. Bayrak has contributed to numerous projects, particularly in Natural Language Processing and Recommender Systems. Alongside his industry work, he remains active in academia, with a substantial portfolio of published research papers.
Dr. Raunak Bhattacharyya
GOALS Lab,
Oxford Robotics Institute
University of Oxford, UK
GOALS Lab, Oxford Robotics Institute
University of Oxford, UK
Raunak Bhattacharyya is a postdoctoral research associate at the Oxford Robotics Institute. His research broadly focuses on decision making under uncertainty for autonomous systems. His postdoctoral work is sponsored by the Embodied Intelligence grant where he is investigating techniques from Markov Decision Processes to solve robotics mission planning problems, and techniques from metacognition and learning from demonstration to enable effective robot human collaboration in shared autonomy systems. Before joining the Oxford Robotics Institute, Raunak obtained his Ph.D. from Stanford University in 2021, where he was a doctoral researcher in the Stanford Intelligent Systems Lab. His dissertation research focused on autonomous driving. Specifically, he used techniques from deep reinforcement learning and system identification to learn driver models from human demonstrations. At Stanford, Raunak was a teaching assistant for Decision Making under Uncertainty and Optimization.
Prof. Jun Cai
IEEE Senior Member
Concordia University, Canada
IEEE Senior Member
Concordia University, Canada
Jun Cai (Senior Member, IEEE) received the Ph.D. degree from the University of Waterloo, Waterloo, ON, Canada, in 2004. From 2004 to 2006, he was with McMaster University, Hamilton, ON, Canada, as a Natural Sciences and Engineering Research Council of Canada (NSERC) Postdoctoral Fellow. From 2006 to 2018, he has been with the Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MI, Canada, where he was a full Professor and the NSERC Industrial Research Chair. Since January 2019, he has joined the Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada, as a full Professor and the PERFORM Centre Research Chair. His current research interests include edge/fog computing, ehealth, radio resource management in wireless communication networks, and performance analysis. Dr. Cai was the Technical Program Committee (TPC) Co-Chair for IEEE GreenCom 2018, Track/Symposium TPC Co-Chair for the IEEE VTC-Fall 2019, IEEE CCECE 2017, IEEE VTC-Fall 2012, IEEE Globecom 2010, and IWCMC 2008, the Publicity Co-Chair for IWCMC 2010, 2011, 2013, 2014, 2015, 2017, 2020, and the Registration Chair for QShine 2005. He was the recipient of the Best Paper Award from Chinacom in 2013, the Rh Award for outstanding contributions to research in applied sciences in 2012 from the University of Manitoba, and the Outstanding Service Award from IEEE Globecom 2010.
Asst. Prof. Hamza Djigal
Wenzhou Kean University, China
Speech Title: Enabling Technologies for Machine and Deep Learning Tasks in Multi-access Edge Computing
Wenzhou Kean University, China
Speech Title: Enabling Technologies for Machine and Deep Learning Tasks in Multi-access Edge Computing
Dr. Hamza Djigal is an Assistant Professor in the Department of Computer Science at Wenzhou Kean University, China. Previously, he was a postdoctoral researcher in Computer Science and Technology at Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing University of Posts and Telecommunications. He received his Ph.D. degree in Computer Science and Technology from Hohai University, Nanjing, China, in 2020; the M.Sc. degree in Software Engineering from Central China Normal University, Wuhan, China, in 2014, and the B.Sc. degree in Mathematics from Cheikh Anta Diop University, Dakar, Senegal, in 2009. His main research interests include Parallel and Distributed Computing, MEC, Machine Learning, and Resource Allocation in Heterogeneous MEC Networks.
Abstract: With the rapid development of Internet-of-Things (IoT) devices and mobile communication
technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm to
extend cloud computing and storage capabilities to the edge of cellular networks. MEC enables
IoT devices with limited battery capacity and computation/storage capabilities to execute their
computation-intensive and latency-sensitive applications at the edge of the networks.
Conversely, Artificial Intelligence (AI) has recently emerged as a crucial technology for many
challenging aspects of MEC. Machine learning (ML) and deep learning (DL) play a vital role in
addressing the challenges of resource management at MEC. However, As the number of IoT
devices increases, the need for researchers to understand how to design architectures that
integrate ML/DL training and inference with MEC proliferates.
Additionally, given the distributed architecture of MEC, a key question arises: “Where should
we perform the training and inference, and where should we deploy the fully trained model in
MEC?”.
Dr. Qiyu Kang
Nanyang Technological University (NTU), Singapore
Speech Title: Graph Neural Networks Informed by Continuous Dynamics
Nanyang Technological University (NTU), Singapore
Speech Title: Graph Neural Networks Informed by Continuous Dynamics
Dr. Qiyu Kang is currently a postdoctoral research fellow at the School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore. He obtained his B.S. degree from the University of Science and Technology of China (USTC), Hefei, China, in 2015, followed by his Ph.D. degree from NTU in 2020. Dr. Kang has been actively involved in research at various prestigious laboratories, including the Continental-NTU Corporate Lab, the Connected Smart Mobility (COSMO) Lab, and Temasek Laboratories @ NTU, where his work has been centered around robust localization and drone perception. Currently, his interests lie in the adversarial robustness of machine learning, the innovative integration of graph neural networks with physics dynamics, and their broad applications in autonomous driving, networks, and smart cities. Dr. Kang has significantly contributed to top-tier machine learning conferences such as NeurIPS, ICML, ICLR, CVPR, and AAAI, and serves as a reviewer for these and other renowned machine learning journals and conferences.
Abstract: Graph Neural Networks (GNNs) have emerged as a powerful tool for modeling relational data, with broad applications across diverse disciplines. Central to the function of GNNs is the message-passing mechanism, which allows for the iterative aggregation of information from the nodes' respective neighborhoods. This method shares a conceptual similarity with physical processes such as heat diffusion, coupled network oscillators, and Hamiltonian systems. For instance, the operational flow of GNNs can be likened to the distribution of heat across nodes, offering a tangible framework to explore the capabilities and limitations of these networks. By integrating physical constraints into the message-passing paradigm, we gain a deeper comprehension of GNNs as more than mere 'black boxes,' paving the way for the refinement of their architecture. Recent advances have seen a surge in GNNs that draw inspiration from continuous dynamic systems, aiming to address inherent challenges like oversmoothing and a lack of robustness. For example, the over-smoothing issue in GNNs is associated with the asymptotic stability of graph neural ordinary differential equations. This talk aims to illuminate cutting-edge research in this cross-disciplinary field, focusing on innovative developments such as Hamiltonian GNNs and GNNs informed by fractional calculus, and highlighting their practical applications.
Asst. Prof. Hugo Wai Leung Mak
The Hong Kong University of Science and Technology & The Chinese University of Hong Kong, Hong Kong, China
Speech Title: Uncovering and Evaluation of Land Cover Dynamics via Support Vector Machine and Image Classification Framework
The Hong Kong University of Science and Technology & The Chinese University of Hong Kong, Hong Kong, China
Speech Title: Uncovering and Evaluation of Land Cover Dynamics via Support Vector Machine and Image Classification Framework
Hugo Mak obtained his B.Sc and Ph.D. degrees from the Hong Kong University of Science and Technology. He has served as a guest editor in various journals related to data analytics, acquired extensive experience as an organizing and scientific committee member in academic conferences and science education events, and actively participated in numerous international satellite missions related to environmental informatics. His research interests include Computational Data Science, Remote Sensing, Data Assimilation and Machine Learning Algorithms, as well as Smart City and Sustainability Development.
Abstract: As modern society advances, the exacerbated altering of landscape patterns and urban expansion were induced for the purpose of human settlement. These spatial transitions of physical infrastructures signify the enhancement of technology within an area, but can also trigger numerous undesirable environmental and socioeconomic consequences, for example, urban environmental degradation and the loss of natural green lands. With an attempt to strive for a balance between smooth urban-rural transition and the preservation of ecological environment for proper functioning of developing countries, a systematic and automated data analytic framework has to be established for uncovering and evaluating LULC within regular basis. In this talk, we will illustrate how remotely sensed images, image pre-processing approaches, supervised machine learning algorithms and statistical metrics can be synergized to monitor spatial and temporal LULC changes of developing cities, assess effectiveness of relevant data driven approaches, and search for reasons of relevant land use transitions that have taken place. Using Landsat images of two Pakistan cities, Hyderabad and Karachi as examples, after image preprocessing was conducted, the supporting vector machine (SVM) supervised classification framework with Gaussian-based Radial Basis Function (RBF) kernel, together with the Semi-automatic Classification Plugin (SCP) algorithm, were adopted for data training, multisource classification of land use feature, and tracking temporal and spatial variations of individual land cover category. Similarities and differences of spatial dynamics and detailed mechanisms within these two developing cities will be presented and elaborated. The extension of this data analytic framework will also be discussed, in particular, its association with environmental impact assessments (EIA) and the enhancement of sustainable city development. This open new windows for future land use monitoring and laying down urban development plans.
Prof. Zhizhong Liu
Yantai University, China
Speech Title: How to Construct the Digital Service Cognitive Intelligent Agent
Yantai University, China
Speech Title: How to Construct the Digital Service Cognitive Intelligent Agent
Zhizhong Liu received his Ph.D. degree in Computer Application Technology from Hohai University, Nanjing, China, in 2011. He was a postdoctoral researcher at the School of Computer Science and Technology in Harbin Institute of Technology. He has been a Visiting Scholar at the Department of Computer Science, Macquarie University. Now, he is a Professor in the School of Computer and Control Engineering at Yantai University, China. Currently, his interests lie in the Intelligent Services, Intelligent recommendation, Cognitive Intelligent and Defect Detection. He has published more than 60 research papers in high impact factor journals.
Abstract: At present, a variety of large-scale digital services have emerged in various fields such as digital government, smart cities, smart elderly care, smart healthcare, smart energy, intelligent transportation, and smart cultural tourism, which greatly promoting social production and economic development. However, the active intelligence level of digital services is generally low, making it difficult to meet the significant demand for service intelligence in the vigorous fields. Therefore, how to enable digital services to own comprehensive cognitive intelligence becomes a challenge. This report mainly talks about the idea of How to Construct the Digital Service Cognitive Intelligent Agent based on the architecture of cognitive intelligence and cognitive agents, with the help of current Multimodal large models, generative artificial intelligence, and so on.
Dr. Vidya Sudarshan
Nanyang Technological University, Singapore
Vidya Sudarshan is a Lecturer at School of Computer Science and Engineering (SCSE) in Nanyang Technological University (NTU). She received her PhD from Nanyang Technological University (NTU), Singapore and has more than 15+ years of research and teaching experience. Her main research expertise is in medical data mining, time series prediction, image and bio-signal processing, deep learning, Intelligent systems and AI applications. She has published more than 40 research papers in high impact factor journals.
Prof. Baicun Wang
Zhejiang University, China
Dr. Baicun Wang is currently a Research Professor at Zhejiang University (ZJU), China. Before joining ZJU, he has been a Postdoctoral Research Fellow at Tsinghua University, Chinese Academy of Engineering, and University of Michigan-Ann Arbor, from 2016 to 2020. Dr. Wang's research interests focus on research, innovation, and applications of human-centric new-generation intelligent manufacturing based on human-cyber-physical systems (HCPS) theory, which include: (1) HCPS & Intelligent Manufacturing, (2) Energy Conversion & Management, and (3) Technology Management & Strategy. Baicun serves as a reviewer for over 10 international scientific journals, including Robotics and Computer-Integrated Manufacturing, Journal of Manufacturing Systems, Nano Energy, and Applied Energy.
Invited Speakers in CEII 2023
Dr. Hyeseung Maria Chang
Korean Educational Research Institute (KEDI), Korea
Asst. Prof. Hamza Djigal
Wenzhou Kean University, China
Assoc. Prof. Yinqiang Zheng
The University of Tokyo, Japan
Prof. Hai-Ning
Liang
Xi' an Jiaotong-Liverpool University, China
Asst. Prof. Carl
Yang
Emory University, US
Assoc. Prof. Boyang Zhou
Zhejiang Lab, China
Nanyang Technological University, Singapore
Vidya Sudarshan is a Lecturer at School of Computer Science and Engineering (SCSE) in Nanyang Technological University (NTU). She received her PhD from Nanyang Technological University (NTU), Singapore and has more than 15+ years of research and teaching experience. Her main research expertise is in medical data mining, time series prediction, image and bio-signal processing, deep learning, Intelligent systems and AI applications. She has published more than 40 research papers in high impact factor journals.
Prof. Baicun Wang
Zhejiang University, China
Zhejiang University, China
Dr. Baicun Wang is currently a Research Professor at Zhejiang University (ZJU), China. Before joining ZJU, he has been a Postdoctoral Research Fellow at Tsinghua University, Chinese Academy of Engineering, and University of Michigan-Ann Arbor, from 2016 to 2020. Dr. Wang's research interests focus on research, innovation, and applications of human-centric new-generation intelligent manufacturing based on human-cyber-physical systems (HCPS) theory, which include: (1) HCPS & Intelligent Manufacturing, (2) Energy Conversion & Management, and (3) Technology Management & Strategy. Baicun serves as a reviewer for over 10 international scientific journals, including Robotics and Computer-Integrated Manufacturing, Journal of Manufacturing Systems, Nano Energy, and Applied Energy.
Invited Speakers in CEII 2023
Dr. Hyeseung Maria Chang
Korean Educational Research Institute (KEDI), Korea
Asst. Prof. Hamza Djigal
Wenzhou Kean University, China
Assoc. Prof. Yinqiang Zheng
The University of Tokyo, Japan
Prof. Hai-Ning
LiangXi' an Jiaotong-Liverpool University, China
Emory University, US
Zhejiang Lab, China