KEYNOTE Speakers

 

Prof. Francis Chin (Fellow of IEEE, Fellow of HKIE, Fellow of HKACE, Emeritus Professor of University of Hong Kong, China)
University of Hong Kong, China

Speech Title: Development and Future of Deep Learning

Abstract: Over the past decade, Deep Learning has already demonstrated great success across many applications such as object detection, image classification, speech recognition, translation, summarization, and chatbots (LLMs and ChatGPT), text to image and text to video. We envision that Deep Learning will have great potential in many other areas of research and applications.
In this talk, we shall revisit the development of Deep Learning, explain the key technologies for its success and how Deep Learning works. Finally, we shall give insights on the future development of Artificial General Intelligence.

Biography: Professor Francis Chin has taught at University of Maryland Baltimore County, University of Alberta, University of California San Diego, Chinese University of Hong Kong, University of Texas at Dallas. Professor Chin joined HKU in 1985, was founding Head and Chair of the Department of Computer Science and Taikoo Professor of Engineering at HKU. He had served as an Associate Dean of the Graduate School from 2002 to 2006 and the Faculty of Engineering from 2007 to 2014.
Professor Chin has served as conference chairman and a member of the program committee of numerous international workshops and conferences. He was the Managing Editor of the International Journal of Foundations of Computer Science and on the editorial boards of journals. Professor Chin received the HKU Teaching Best Teaching, Teaching Excellence Award and Outstanding Research Award in 1991, 2000 and 2010 respectively. Professor Chin with his bioinformatics team has won the RECOMB 2022 Test-of-Time Award based on the impact of their RECOMB2010 IDBA paper. He is also listed within the World’s Top 2% Scientists published by Stanford University in October 2022.

 

Prof. Min Chen (Fellow of IEEE, Fellow of IET, Fellow of AAIA, Highly Cited Researcher (2018-2024))
South China University of Technology, China

Speech Title: Large Language Model (LLM) Fine Tuning: Concepts, Opportunities, and Challenges

Abstract: As a foundation of Large Language Models, fine-tuning drives rapid progress, broad applicability, and profound impacts on human-AI collaboration, surpassing earlier technological advancements. This talk examines the core principles, development, and applications of fine-tuning techniques, emphasizing its growing significance across diverse field and industries. By analyzing the latest round of LLM fine-tuning advancements, this talk explores potential future directions for the co-evolution of humans and AI, as well as emphasizing their potential to achieve higher levels of cognitive and operational intelligence. Specifically, this talk introduces Natural Language Fine-Tuning (NLFT). The pioneering work of NLFT is the first superior technique for paving the way to deploy various innovative LLM fine-tuning applications when resources are limited at network edges.

Biography: Min Chen is a tenured full professor in School of Computer Science and Engineering at South China University of Technology. He was the director of Embedded and Pervasive Computing (EPIC) Lab at Huazhong University of Science and Technology. He is the founding Chair of IEEE Computer Society Special Technical Communities on Big Data. He was an assistant professor in School of Computer Science and Engineering at Seoul National University. He worked as a Post-Doctoral Fellow in Department of Electrical and Computer Engineering at University of British Columbia from 2006 to 2009. He received Best Paper Award from IEEE ICC 2012 and IEEE IWCMC 2016, etc. He serves as associate editor for IEEE Transactions on Big Data, and ACM Transactions on Multimedia Computing, Communications, and Applications, etc. He was a Series Editor for IEEE Journal on Selected Areas in Communications. He was General Chair of CEI 2024, Symposium Chair of IEEE Globecom 2022 eHealth, Co-Chair of IEEE ICC 2012-Communications Theory Symposium, and Co-Chair of IEEE ICC 2013-Wireless Networks Symposium. He was General Co-Chair for IEEE CIT-2012, Tridentcom 2014, Mobimedia 2015, and Tridentcom 2017. He was keynote speaker for IEEE BHI-BSN 2022. He has 200+ SCI papers, including IEEE JSAC, IEEE TNNLS, IEEE TPDS, IEEE TWC, IEEE TSC, INFOCOM, AAAI, CVPR, Science, Advanced Materials, Nature Communications, etc. He has published 12 books, including Big Data Analytics for Cloud/IoT and Cognitive Computing (2017) with Wiley. His Google Scholar Citations reached 49,700+ with an H-index of 101. His top paper was cited 5,170+ times. He was selected as ESI Highly Cited Researcher from 2018 to 2024. He got IEEE Communications Society Fred W. Ellersick Prize in 2017, the IEEE Jack Neubauer Memorial Award in 2019, and IEEE ComSoc APB Oustanding Paper Award in 2022. His research focuses on cognitive computing, 5G Networks, wearable computing, big data analytics, robotics, emotion detection, mobile edge computing, LLM and fabric computing etc. He is an IEEE Fellow since 2021.

 

Prof. Ljiljana Trajkovic (Fellow of IEEE)
Simon Fraser University, Canada

Speech Title: Machine Learning for Detecting Internet Traffic Anomalies

Abstract: Collection and analysis of data from deployed networks is essential for understanding communication networks. Hence, data mining and statistical analysis of network data have been employed to determine traffic loads, analyze patterns of users' behavior, predict future network traffic, and detect traffic anomalies. The Internet has historically been prone to failures and attacks that significantly degrade its performance, affect the Internet connectivity, and cause routing disconnections. Frequent cases of various cyber threats have been encountered over the years and, hence, detection of anomalous behavior is a topic of great interest in cybersecurity. In described case studies, traffic traces collected by various collection sites are used to classify network anomalies. Various anomaly and intrusion detection approaches based on machine learning have been employed to analyze collected data. Deep learning, broad learning, gradient boosted decision trees, and reservoir computing algorithms were used to develop models based on collected datasets that contain Internet worms, viruses, power outages, ransomware events, router misconfigurations, Internet Protocol hijacks, and infrastructure failures in times of conflict. The reported results indicate that while performance of machine learning models greatly depends on the used datasets, they are viable tools for detecting the Internet anomalies.

Biography: Ljiljana Trajkovic received the Dipl. Ing. degree from University of Pristina, Yugoslavia, the M.Sc. degrees in electrical engineering and computer engineering from Syracuse University, Syracuse, NY, and the Ph.D. degree in electrical engineering from University of California at Los Angeles. She is currently a professor in the School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada. Her research interests include communication networks and dynamical systems. Dr. Trajkovic served as IEEE Division X Delegate/Director, President of the IEEE Systems, Man, and Cybernetics Society, and President of the IEEE Circuits and Systems Society. She serves as Editor-in-Chief of the IEEE Transactions on Human-Machine Systems. She was a Distinguished Lecturer of the IEEE Circuits and System Society and a Distinguished Lecturer of the IEEE Systems, Man, and Cybernetics Society. She is a Fellow of the IEEE.