Prof. Jianguo Ma IEEE Fellow
Zhejiang Lab, China

Biography: Jianguo Ma received the doctoral degree in engineering in 1996 from Duisburg University, Duisburg, Germany. He was a faculty member of Nanyang Technological University (NTU) of Singapore from Sept 1997 to Dec. 2005 after his post-doctoral fellowship with Dalhousie University of Canada in Apr 1996 – Sept 1997. He was with the University of Electronic Science and Technology of China in Jan 2006 – Oct 2009 and he served as the Dean for the School of Electronic Information Engineering and the founding director of the Qingdao Institute of Oceanic Engineering of Tianjin University in Oct. 2009 – Aug 2016; he joined Guangdong University of Technology as a distinguished professor in Sept 2016 – Aug 2021. Dr. Ma served as the Vice Dean for the School of Micro-Nano Electronics of Zhejiang University in Sept, 2021 – Oct 2022, Starting from 1 Nov 2022 he joins the Zhejiang Lab. His research interests are: Microwave Electronics; RFIC Applications to Wireless Infrastructures; Microwave and THz Microelectronic Systems; He served as the Associate Editor for IEEE Microwave and Wireless Components Letters in 2003 –2005; He was the member for IEEE University Program ad hoc Committee (2011~2013). Dr. Ma was the Member of the Editorial Board for Proceedings of IEEE in 2013-2018 He is Fellow of IEEE for the Leadership in Microwave Electronics and RFICs Applications Dr. Ma was serving as the Editor-in-Chief of IEEE Transactions on Microwave Theory and Techniques in 2020 –2022.

Speech Title: AI Empowered Microwave Power-Amplifier Designs and Optimizations
Abstract: Microwave power amplifiers have been and are still paying critical roles in any wireless communication systems and radar systems. The designs of microwave power-amplifiers are strongly experience-dependent because of the strong nonlinearities. It is time-consuming and very hard for getting optimal designs. Conceptual-wise the design procedures of microwave power-amplifiers are AI algorithms, it is straightforward to make use of AIs as tools for optimizing power-amplifier designs. The design challenging is discussed firstly followed by examples of using ANNs. PA-GPT will be the trend.

Prof. Bing Liu
AAAI/ACM/IEEE Fellow, University of Illinois at Chicago (UIC), United States

Biography:   Bing Liu is a distinguished professor of Computer Science at the University of Illinois at Chicago (UIC). He received his Ph.D. in Artificial Intelligence (AI) from the University of Edinburgh. Before joining UIC, he was a faculty member at the School of Computing, National University of Singapore (NUS). He was also with Peking University for one year (2019-2020). His research interests include lifelong and continual learning, sentiment analysis, lifelong learning chatbots, open-world AI/learning, natural language processing (NLP), and data mining and machine learning. He has published extensively in top conferences and journals (his Google Scholar page). He also authored four books: one about lifelong machine learning (first ever book on the topic), two about sentiment analysis and one about Web mining. Three of his papers received Test-of-Time awards: two from SIGKDD (ACM Special Interest Group on Knowledge Discovery and Data Mining) and one from WSDM (ACM International Conference on Web Search and Data Mining). Another of his papers received Test-of-Time award - honorable mention also from WSDM. Some of his work has been widely reported in the international press, including a front-page article in the New York Times. On professional services, he has served as the Chair of ACM SIGKDD from 2013-2017, as program chair of many leading data mining conferences, including KDD, ICDM, CIKM, WSDM, SDM, and PAKDD, as associate editor of leading journals such as TKDE, TWEB, DMKD and TKDD, and as area chair or senior PC member of numerous NLP, AI, Web, and data mining conferences. He is a recipient of ACM SIGKDD Innovation Award (the most prestigious technical achievement award from SIGKDD), and he is a Fellow of the ACM, AAAI, and IEEE.
Title:  AI Autonomy: Self‐initiated Open‐world Continual Learning and Adaptation
Abstract: As more and more AI agents are used in practice, it is time to think about how to make these agents fully autonomous so that they can (1) learn by themselves continually in a self‐motivated and self‐initiated manner rather than being retrained offline periodically on the initiation of human engineers and (2) accommodate or adapt to unexpected or novel circumstances. As the real‐world is an open environment that is full of unknowns or novelties, the capabilities of detecting novelties, characterizing them, accommodating to them, and gathering ground‐truth training data and incrementally learning the unknowns/novelties become critical in making the AI agent more and more knowledgeable, powerful and self‐sustainable over time. 

Prof. Philippe Fournier-Viger
Shenzhen University, China

Biography: Philippe Fournier-Viger (Ph.D) is a Canadian researcher, distinguished professor at Shenzhen University (China). Five years after completing his Ph.D., he came to China in 2015 and became full professor after receiving a talent title from the National Science Foundation of China. He has published more than 375 research papers related to data mining algorithms for complex data (sequences, graphs), intelligent systems and applications, which have received more than 13,000 citations. He is the founder of the popular SPMF data mining library, offering more than 250 algorithms to find patterns in data, cited in more than 1,000 research papers. He is former associate editor-in-chief of the Applied Intelligence journal and has been keynote speaker for over 20 international conferences and co-edited four books for Springer. He is a co-founder of the UDML, PMDB and MLiSE series of workshops held at the ICDM, PKDD, DASFAA and KDD conferences. Website: http://www.philippe-fournier-viger.com
Speech Title: Advances and challenges for the automatic discovery of interesting patterns in data

Abstract: Intelligent systems and tools can play an important role in various domains such as for factory automation, e-business, and manufacturing. To build intelligent systems and tools, high-quality data is generally required. Moreover, these systems need to process complex data and can yield large amounts of data such usage logs, images, videos, and data collected from industrial sensors. Managing data to gain insights and improve these systems is thus a key challenge. It is also desirable to be able to extract information or models from data that are easily understandable by humans. Based on these objectives, this talk will discuss the use of data mining algorithms for discovering interesting and useful patterns in data generated from intelligent systems and other applications.The talk will first briefly review early study on designing algorithms for identifying frequent patterns. Then, an overview of recent challenges and advances will be presented to identify other types of interesting patterns in more complex data. Topics that will be discussed include high utility patterns, locally interesting patterns, and periodic patterns. Lastly, the SPMF open-source software will be mentioned and opportunities related to the combination of pattern mining algorithms with traditional artificial intelligence techniques for intelligent systems will be discussed.