Keynote Speakers

Prof. Ling Liu (IEEE Fellow)

Georgia Institute of Technology, USA

Ling Liu is a full professor in the School of Computer Science at Georgia Institute of Technology. She directs the research programs in the Distributed Data Intensive Systems Lab (DiSL), examining various aspects of big data systems and analytics. Prof. Liu is an elected IEEE Fellow, a recipient of IEEE Computer Society Technical Achievement Award (2012), and a recipient of the best paper award from numerous top venues, including IEEE ICDCS, WWW, ACM/IEEE CCGrid, IEEE Cloud, IEEE ICWS. Prof. Liu served on editorial board of over a dozen international journals, including the editor in chief of IEEE Transactions on Service Computing (2013-2016). Currently, Prof. Liu is the editor in chief of ACM Transactions on Internet Computing (since 2019). She is the chair of IEEE CS Fellow Evaluation Committee (FY2024). Her current research is primarily supported by National Science Foundation, CISCO and IBM.

Title of Speech: From Centralized Learning to Distributed Learning: Opportunities and Challenges
Abstract: Machine learning has blossomed through (centralized) learning over massive data, evidenced by recent advances in self-supervised multi-modal learning and generative AI powered large language models (LLMs). Most of the benchmark datasets are publicly available data sources and can be freely collected to a centralized Cloud repository to train large models, such as ChatGPT, LLaMA. However, for the missions-critical applications in the real world, massive proprietary data are generated 24x7 at the edge of the Internet. Centralized collection of such geographically distributed and proprietary datasets is neither feasible nor realistic w.r.t. resource/latency demand and data privacy/confidentiality requirement. In this distinguished lecture, I will illustrate the potential of self-supervised learning and generative AI, and discuss two important technological advancements in Equitable AI, which can scale the training and the deployment of large models on the edge. First, we will describe and compare a suite of large model reduction techniques for large foundation models and their fine-tuning of downstream learning tasks. Second, we will introduce Federated learning (FL), an emerging distributed learning paradigm. Federated learning holds the promise of enabling joint training of a large global model by a distributed population of edge clients, while keeping their sensitive data local and only share their local model updates with the FL server(s) during each iterative learning round until the global model reaches the convergence. I will conclude with an outlook of generative AI and LLMs.


Prof. Juyang Weng (IEEE Life Fellow)

Brain-Mind Institute and GENISAMA, USA

Prof. Juyang Weng received the BS degree from Fudan University, in 1982, M. Sc. and PhD degrees from the University of Illinois at Urbana-Champaign, in 1985 and 1989, respectively, all in computer science. He is a former faculty member of Department of Computer Science and Engineering, faculty member of the Cognitive Science Program, and faculty member of the Neuroscience Program at Michigan State University, East Lansing. He was a visiting professor at the Computer Science School of Fudan University, Nov. 2003 - March 2014, and did sabbatical research at MIT, at Media Lab Fall 1999 – Spring 2000; and at Department of Brain and Cognitive Science Fall 2006-Spring 2007 and taught BCS9.915/EECS6.887 Computational Cognitive and Neural Development during Spring 2007. Since the work of Cresceptron (ICCV 1993) the first deep learning neural networks for 3D world without post-selection misconduct, he expanded his research interests in biologically inspired systems to developmental learning, including perception, cognition, behaviors, motivation, machine thinking, and conscious learning models. He has published over 300 research articles on related subjects, including task muddiness, intelligence metrics, brain-mind architectures, emergent Turing machines, autonomous programing for general purposes (APFGP), Post-Selection flaws in “deep learning”, vision, audition, touch, attention, detection, recognition, autonomous navigation, and natural language understanding. He published with T. S. Huang and N. Ahuja a research monograph titled Motion and Structure from Image Sequences. He authored a book titled Natural and Artificial Intelligence: Computational Introduction to Computational Brain-Mind. Dr. Weng is an Editor-in-Chief of the International Journal of Humanoid Robotics, the Editor-in-Chief of the Brain-Mind Magazine, and an associate editor of the IEEE Transactions on Autonomous Mental Development (now Cognitive and Developmental Systems). With others’ support, he initiated the series of International Conference on Development and Learning (ICDL), the IEEE Transactions on Autonomous Mental Development, the Brain-Mind Institute, and the startup GENISAMA LLC. He was an associate editor of the IEEE Transactions on Pattern Recognition and Machine Intelligence and the IEEE Transactions on Image Processing.

Title of Speech: Training and Test Protocols for Conscious Learning Robots
Abstract: This is a theoretical talk. The algorithm for conscious learning has been recently published (Weng AIEE 2022). Developmental scales for human children are well developed. Such scales need to be adapted to testing conscious learning robots.
Without such adaptations, future conscious robots lack a standard, even if the theory and algorithm for conscious learning are implemented and refined in the future.
This talk discusses such an adaptation, but does not include actual experimental results. It first proposes that an open skull will not allow a conscious brain because
the open skull allows a conscious homunculus (human) who takes over the job of consciousness. That is why the currently popular “open skull” machine learning protocols will not produce conscious robots. Then, the talk borrows some of the milestones from human mental development measured in terms of human mental ages.
The author hopes that independent laboratories will conduct tests using the milestones suggested here, so as to see whether the new protocol is suited for measuring robotic consciousness. Due to space limitations, this talk does not explain conscious learning. The reader should first read (Weng AIEE 2022) before attending this talk. For four aspects of transfer across milestones, the reader should read J. Weng, Natural and Artificial Intelligence, 2nd edition, BMI Press, 2019, especially, Sec. 10.3.


Assoc. Prof. Yunbo Rao

University of Electronic Science and Technology of China, China

Yunbo Rao received his B.S. degree and M.E. degree from Sichuan Normal University and University of Electronic Science and Technology of China in 2003 and 2006, respectively, and Ph.D. degree from University of Electronic Science and Technology of China, Chengdu in 2012, both in School of Computer Science and Engineering (SCSE). He has been as a visiting scholar of Electrical Engineering of the University of Washington from Oct 2009 to Oct 2011, Seattle, USA. He has published over 110 refereed articles and 10 books. He served as Chair of the International Conference on Pattern Recognition and Artificial Intelligence (PRAI 2022). His areas of interests include video enhancement, computer vision, three-dimensional reconstruction, virtual reality, augmented reality, and crowd animation. He also worked as research interns at Neusoft Inc.during 2004-2008.
Since May.2012, he joined School of Information and Software Engineering, University of Electronic Science and Technology of China. Currently, he is an associate professor at University of Electronic Science and Technology of China(UESTC). He is a supervisor of Ph.D student in Dec.2017.

Title of Speech: Analog Gauge Detection and Interpretation in Challenging Industrial Environments
Abstract: Various types of pointer meters have been extensively used in numerous areas, such as substations and mechanical manufacturing. Traditional manual methods no longer suffice, and automated solutions have emerged as a promising alternative. These automated methods often stumble when faced with the unforgiving conditions found in environments like substations—extreme temperatures, high voltages, humidity, moisture, and intense radiation, all of which can severely impair image quality and camera performance, resulting in unreliable readings. In this talk, we will discuss new methods the interference with the camera brought by moving devices (e.g., patrol robots and drones), this method addressed problem of the persistent motion blur caused by the camera. Second, we will discuss some methods of reading the pointer meter relies heavily on semantic segmentation of the scale and pointer within the meter. Third, we will share three works: (1)Filter-Deblur-U-net is proposed to ensure accurate segmentation under motion blur. (2)The judgment reading-algorithm is developed to complete readings of 35 types of meters. (3)The dark channel prior dehaze Laplace is proposed to determine whether the meter patch is motion-blurred.



ICCDE Previous Keynote Speakers

Prof. Derong Liu (IEEE Fellow)

University of Illinois at Chicago, USA

Derong Liu received the Ph.D. degree in electrical engineering from the University of Notre Dame in 1994. He was a Staff Fellow with General Motors Research and Development Center, from 1993 to 1995. He was an Assistant Professor with the Department of Electrical and Computer Engineering, Stevens Institute of Technology, from 1995 to 1999. He joined the University of Illinois at Chicago in 1999, and became a Full Professor of Electrical and Computer Engineering and of Computer Science in 2006. He was selected for the “100 Talents Program” by the Chinese Academy of Sciences in 2008, and he served as the Associate Director of The State Key Laboratory of Management and Control for Complex Systems at the Institute of Automation, from 2010 to 2015. He is now a Full Professor with the School of Automation, Guangdong University of Technology. He has published 19 books. He is the Editor-in-Chief of Artificial Intelligence Review (Springer). He was the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems from 2010 to 2015. He received the Faculty Early Career Development Award from the National Science Foundation in 1999, the University Scholar Award from University of Illinois from 2006 to 2009, the Overseas Outstanding Young Scholar Award from the National Natural Science Foundation of China in 2008, and the Outstanding Achievement Award from Asia Pacific Neural Network Assembly in 2014. He is a Fellow of the IEEE, a Fellow of the International Neural Network Society, and a Fellow of the International Association of Pattern Recognition.


Prof. Hai Jin (IEEE Fellow)

Huazhong University of Science and Technology, China

Hai Jin received the PhD degree in computer engineering from Huazhong University of Science and Technology, China in 1994. He is a Cheung Kung scholars chair professor of computer science and engineering with Huazhong University of Science and Technology. In 1996, he was awarded a German Academic Exchange Service fellowship to visit the Technical University of Chemnitz in Germany. He worked with The University of Hong Kong between 1998 and 2000, and as a visiting scholar with the University of Southern California between 1999 and 2000. His research interests include computer architecture, virtualization technology, cluster computing and cloud computing, peer-to-peer computing, network storage, and network security. He was awarded Excellent Youth Award from the National Science Foundation of China in 2001. He is the chief scientist of ChinaGrid, the largest grid computing project in China, and the chief scientists of National 973 Basic Research Program Project of Virtualization Technology of Computing System, and Cloud Security. He has co-authored 22 books and published more than 700 research papers. He is a fellow IEEE, CCF, and a life member of the ACM.



Prof. Kay Chen Tan (IEEE Fellow)

City University of Hong Kong, Hong Kong

Kay Chen Tan received the B.Eng. (First Class Hons.) degree in electronics and electrical engineering and the Ph.D. degree from the University of Glasgow, U.K., in 1994 and 1997, respectively. He is a Full Professor with the Department of Computer Science, City University of Hong Kong, Hong Kong SAR. He has published over 300 refereed articles and 10 books. Prof. Tan is the Editor-in-Chief of the IEEE Transactions on Evolutionary Computation (IF: 8.058), was the Editor-in-Chief of the IEEE Computational Intelligence Magazine from 2010 to 2013, and currently serves as the Editorial Board Member of over 10 journals. He is currently an elected member of IEEE CIS AdCom, an IEEE DLP Speaker, and a Changjiang Chair Professor in China.



Prof. Peter Haddawy

Mahidol University, Thailand

Professor Haddawy received a BA in Mathematics from Pomona College in 1981 and MSc and PhD degrees in Computer Science from the University of Illinois-Urbana in 1986 and 1991, respectively. He was tenured Associate Professor in the Department of Electrical Engineering and Computer Science at the University of Wisconsin-Milwaukee, and Director of the Decision Systems and Artificial Intelligence Laboratory there through 2002. Subsequently, he served as Professor of Computer Science and Information Management at the Asian Institute of Technology (AIT) through 2010 and the Vice President for Academic Affairs from 2005 to 2010. He served in the United Nations as Director of UNU-IIST from 2010 through 2013. Professor Haddawy has been a Fulbright Fellow, Hanse-Wissenschaftskolleg Fellow, Avery Brundage Scholar, and Shell Oil Company Fellow. His research falls broadly in the areas of Artificial Intelligence, Medical Informatics, and Scientometrics and he has published over 130 refereed papers with his work widely cited. His research in Artificial Intelligence has concentrated on the use of decision-theoretic principles to build intelligent systems and he has conducted seminal work in the areas of decision-theoretic planning and probability logic. His current work focuses on intelligent medical training systems and application of AI techniques to surveillance and modeling of vector-borne disease. In the area of Scientometrics Prof. Haddawy has focused on development of novel analytical techniques motivated by and applied to practical policy issues. He currently holds a full professorship in the Faculty of ICT at Mahidol University in Thailand where he is Director of the Mahidol-Bremen Medical Informatics Research Unit and Deputy Dean for Research. He also holds an Honorary Professorship and the Chair for Medical Informatics at the University of Bremen in Germany.

Title of Speech: Employing AI in the Fight Against Vector-Borne Disease: From Sensing to Prediction 

Abstract: The Mahidol-Bremen Medical Informatics Research Unit has been pursuing a program of research in AI for Vector-Borne Diseases that explores the development of new techniques to provide more timely, complete, and accurate information to help target disease control efforts. The program addresses all points in the information pipeline from sensing to analysis to prediction. In this talk, I illustrate the approach with our work on dengue fever.
I present a novel approach to detect potential dengue vector breeding sites from geotagged images to create highly detailed dengue risk maps at unprecedented scale. The approach is implemented and evaluated using Google Street View images, which have the advantage of broad coverage and of being somewhat historical so that the data can be aligned with other types of data for analysis. Containers comprising eight of the most common breeding sites are detected in the images using convolutional neural networks. The generated container density counts and other relevant information are displayed on an immersive data analytics platform that supports rapid exploration of the data.
We carry out extensive analyses to validate the approach over three provinces in Thailand. We show that the container densities agree well with manual container counts and are well correlated with larval survey data during the dengue season. To evaluate the added value of the container densities for prediction, we build predictive models for the three provinces at the sub-district level with and without the container information. The models with container density information outperform the baseline models in all three provinces, with improvements in accuracy of between 6% and 42%. We conclude that creation of container density maps from geotagged images is a promising approach to providing detailed information on potential breeding sites at large scale.  




Prof. Chen-Huei Chou

College of Charleston, SC, USA

Chen-Huei Chou received the B.S. in Information and Computer Engineering from Chung Yuan Christian University, Taiwan, the M.S. in Computer Science and Information Engineering from National Cheng Kung University, Taiwan, the M.B.A. from the University of Illinois at Chicago, Chicago, Illinois, USA, and the Ph.D. in Management Information Systems from the University of Wisconsin-Milwaukee, Wisconsin, USA.
He is an Associate Professor of Information Management and Decision Sciences in the School of Business at the College of Charleston, SC, U.S.A. His research has been published in MIS journals and major conference proceedings, including MIS Quarterly, Journal of Association for Information Systems, Decision Support Systems, IEEE Transactions on Systems, Man, and Cybernetics, Computers in Human Behavior, Internet Research, and Journal of Information Systems and e-Business Management. His areas of interests include web design issues in disaster management, ontology development, Internet abuse in the workplace, text mining, data mining, knowledge management, and behavioral studies related to the use of IT.

Title of Speech: Artificial Intelligence for Modern Internet Abuse Detection 

Abstract: As the use of the Internet in organizations continues to grow, so does Internet abuse in the workplace. Internet abuse activities by employees—such as online chatting, gaming, investing, shopping, illegal downloading, pornography, and cybersex—and online crimes are inflicting severe costs to organizations in terms of productivity losses, resource wasting, security risks, and legal liabilities. Organizations have started to fight back via Internet usage policies, management training, and monitoring. Internet filtering software products are finding an increasing number of adoptions in organizations. These products mainly rely on blacklists, whitelists, and keyword/profile matching. In this talk, I would like to share a text mining approach to Internet abuse detection. I have empirically compared a variety of term weighting, feature selection, and classification techniques for Internet abuse detection in the workplace of software programmers. The experimental results are very promising; they demonstrate that the text mining approach would effectively complement the existing Internet filtering techniques. In this speech, I would like to share my knowledge and experience in conducting text mining approach for detecting Internet abuse in the workplace.