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.
AIEE Past 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.
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.