SPEAKERS
Lin Chen Sun Yat-sen University, China | Brief Introduction: Lin Chen is a Full Professor in the School of Computer Science and Engineering at Sun Yat-sen University, which he joined in 2019. He received his B.E. in 2002 from Southeast University, his Engineer Diploma in 2005 and Ph.D. in 2008 from Telecom ParisTech (ENST). He received his Habilitation thesis at University of Paris-Sud in 2017. He was an associate professor at the Department of Computer Science at University of Paris-Sud from 2009 to 2019 and a visiting scholar at National ICT Center of Australia (NICTA) in 2008. His research interests revolve around modeling and design of distributed algorithms in emerging networked systems, with particular emphasis on energy efficiency, resilience, and security. He received the 2018 CNRS Bronze Medal and was a Junior Member of the Institut Universitaire de France (IUF). He has served on the editorial board of several international journals and in the TPC of major conferences in communications and networking. He served as the Chair of the IEEE TCGCC SIG on Green and Sustainable Networking and Computing with Cognition and Cooperation. Title: On Batching Task Scheduling: Theoretical Foundation and Algorithm Design Abstract: This talk is focused on the following batching task scheduling problem. There is a set of tasks to be executed on a number of machines. Some can be executed simultaneously on a single machine, while others require exclusive use of an entire machine. We seek an optimal scheduling policy to maximize the overall system utility. This problem is a significant generalization of the broadcast and lock scheduling problems, and arises in a variety of engineering fields where communication, computing, and storage resources are potential bottlenecks and thus need to be carefully scheduled. In the talk I will start by introducing the motivation and theoretical background of the problem. I will then present the algorithmic framework we have developed for batching task scheduling in its most generic form, which is the first approximation algorithm with deterministic performance guarantee. I will focus on the core technicality in our design, a novel LP relaxation mechanism and a rounding and coloring approach that turns the solution of the LP relaxation to a feasible scheduling policy. I will conclude the talk by discussing a number of variants and extensions and our on-going work along this line of research. |
Han Huang South China University of Technology, China | Brief Introduction: Dr. Huang is a professor and doctoral supervisor of the School of Software Engineering at South China University of Technology. He is currently serving as an associate editor of IEEE Transactions on Evolutionary Computation (IF: 14.3), Complex & Intelligent Systems (IF: 5.8) and IEEE Transactions on Emerging Topics in Computational Intelligence (IF: 5.3), and Director of Teaching Steering Committee for Software Engineering of Undergraduate Colleges and Universities in Guangdong Province. Prof. Huang has made great contributions to the scholarship on the theories and application of intelligent optimization algorithms. For example, he has proposed a time complexity analysis method of real-world evolutionary algorithms, algorithms for efficient and accurate image matting, a method for automated test case generation based on path coverage, etc. Prof. Huang has hosted more than twenty national and provincial projects. He has published two books, Theory and Practice of Intelligent algorithm and Theory, Methods and Tools for Time Complexity Analysis of Evolutionary Algorithm. He has also published more than 80 papers in IEEE TCYB, IEEE TETC, IEEE TSE, IEEE TEVC, IEEE TIP, IEEE TFS, and Science China, including ESI highly cited papers. As the first inventor, Prof. Huang has 44 invention patents granted in China and seven invention patents granted in the United States. He won China Patent Excellence Award and developed an association standard entitled “Standard for glass-box testing without source code” as the first completer. Additionally, Prof. Huang pays attention to social services. Over the past five years, he has given more than 50 public lectures on science and technology for government offices, primary and secondary schools, CCF, YOCSEF, media, etc. He has been in charge of the development and release of six public software systems such as Unit Test Algorithm Platform http://www.unittestpc.com.cn, Automatic Structural Equation Modeling System http://www.autosem.net, Evolutionary Algorithm Time Complexity Analysis System http://www.eatimecomplexity.net, and Energy Storage Optimization System http://energystorage.autosem.net, which have provided free technical service and support for lots of researchers and engineers. Title:Micro-scale searching algorithm and its application Abstract:Intelligent optimization algorithm is an important artificial intelligence method which is often used to solve complex black-box optimization problems. From the perspective of the nature of algorithm performance, this report will describe the fundamental reasons and key points of algorithm performance improvement. It will introduce the idea of micro-scale searching algorithm: By determining the effective decision subset of optimization problems, adjust the reasonable allocation of computational resources and achieve effective search in a small space, thus obtaining the optimal solution or high-quality feasible solution of the problem. Based on this algorithmic idea, The report will analyze the micro-scale searching assumptions and performance nature of intelligent optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), ant colony optimization (ACO). It will also summarize and discuss the pitfalls of analyzing performance of evolutionary computing methods. Finally, this report will introduce the applications of micro-scale searching algorithm ideas in industrial software, software engineering, computer vision, digital logistics, etc. |
Philippe Fournier-Viger Shenzhen University, China | Brief Introduction: Philippe Fournier-Viger (Ph.D) is 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 an important talent title from the National Science Foundation of China. He has published more than 380 research papers related to data mining algorithms for complex data (sequences, graphs), intelligent systems and applications, which have received more than 14,500 citations (H-Index 61 - Google Scholar). 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 30 international conferences and co-edited four books for Springer. He appears in the top 2% of researchers for scientific influence in the Stanford list, and is a Elsevier «Highly Cited Chinese Researcher» (2022). Website: http://www.philippe-fournier-viger.com. 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 such as graphs and sequences. 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. |
Zongwei Luo Beijing Normal University Zhuhai, China | Brief Introduction: Luo Zongwei, Independent Director,Chief Expert of high-tech companies, Secretary of GD AI Institute of Higher Education. Dr Luo is also affliated with Beijing Normal University Zhuhai and UIC. Dr. Luo has rich experience in R&D management in universities and industries in the fields of artificial intelligence, big data analysis, and financing/investment. He has been consecutively selected in Stanford Worldwide Top Scientists List of Lifelong Impact (2023) and Yearly Impact (2021, 2022, 2023). Title: Artificial Intelligence: Development, Impacts, and Potential Issues Abstract: In this talk, we will focus on AI and its development trend. We first examine what happened in Industrial Revolution. By making the lessons learned relevant, we introduce the potential impact AI might bring. The discussion of AI's several rounds of up and down would make AI more relevant in better understanding potential issues which may arise. |