Keynote Speakers
Prof. Wanyang Dai
Nanjing University, China
Title: 3D AI and spatial generative AI via feedback control strengthened quantum transformer
Abstract:
To support 3D AI and spatial generative AI for 6G communication and metaverse minded robot decision-making, we establish a unified quantum transformer (called Q-Transformer) with the capability of prediction and adaptive feedback control interaction, which consists of quantum encode-decode coupling processes. This newly proposed Q-Transformer is integrated into our previously developed quantum cloud computing platform as its smart federated learning engine, which is supported by our recently designed and justified neutral atom quantum computer. The main purpose to develop such an integrated big model platform system is to conduct high-dimensional vector decision-making for real-world internet of things (IoT) systems, which involve big data managements and heavy numerical computations. Specific applications such as multiple objective based robot routing, resource allocation, and dynamic pricing will be given. Related optimization and equilibrium policies will be trained with numerical simulations.
Biography:
Wanyang Dai is a Distinguished Professor in Mathematics Department of Nanjing University, Chief Scientist at Su Xia Control Technology, President and CEO of U.S. based (blochchain and quantum computing) SIR Forum (Industial 6.0 Forum), a Special Guest Expert in Jiangsu FinTech Research Center, President of Jiangsu Probability & Statistics Society, Chairman of Jiangsu Big Data-Blockchain and Smart Information Special Committee, Chief Scientist at Depths Digital Economy Research Institute, and Editor-in-Chief of Journal of Advances in Applied Mathematics, where his research includes stochastic processes related optimization and optimal control, admission/scheduling/routing protocols and performance analysis/optimization for various projects in BigData-Blockchain oriented quantum-cloud computing and the next generation of wireless and wireline communication systems, forward/backward stochastic (ordinary/partial) differential equations and their applications to queueing systems, stochastic differential games, communication networks, Internet of Things, financial engineering, energy and power engineering, etc. His “influential” achievements are published in “big name” journals including Quantum Information Processing, Operational Research, Operations Research, Computers & Mathematics with Applications, Communications in Mathematical Sciences, Journal of Computational and Applied Mathematics, Queueing Systems, Mathematical and Computer Modeling of Dynamical Systems, etc. His researches are awarded as outstanding papers by various academic societies, e.g., IEEE Top Conference Series, etc.. He received his Ph.D degree in applied mathematics jointly with industrial engineering and systems engineering from Georgia Institute of Technology, Atlanta, GA, U.S.A., in 1996, where he worked on stochastics and applied probability concerning network performance modeling and analysis, algorithm design and implementation via stochastic diffusion approximation. The breakthrough results and methodologies developed in his thesis were cited, used, and claimed as “contemporaneous and independent” achievements by some other subsequent breakthrough papers that were presented as “45 minute invited talk in probability and statistics” in International Congress of Mathematicians (ICM) 1998, which is the most privilege honor in the mathematical society. The designed finite element-Galerkin algorithm to compute the stationary distributions of reflecting Brownian motions (weak solutions of general dimensional partial differential equations) is also well-known to the related fields.
Prof. Lin Chen
Sun Yat-sen University, China
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.
Biography:
Born in Nanjing, China, Lin Chen received his B. Sc. degree in Radio Engineering in 2002 from Southeast University, Nanjing, China, his M. Sc. in Networking in 2005 from University of Paris 6, his engineer and Ph. D. degree in Computer Science and Networking in 2005 and 2008 from Telecom ParisTech (ENST), Paris, and the Habilitation (HDR) in 2017 from University of Paris-Sud. From 2009 to 2019, he was an associate professor in the Department of Computer Science at University of Paris-Sud. Since 2019, he has been a professor at School of Data and Computer Science, Sun Yat-sen (Zhongshan) University. He worked as a Postdoc Researcher at Telecom ParisTech during 2008-2009 and a Visiting Researcher at NICTA (National ICT Australia) in 2008.
Prof. Lazim Abdullah
Universiti Malaysia Terengganu, Malaysia
Title: A Combined Decision-Making Analysis Under Single Valued Neutrosophic Set for Selecting Knowledge Management Strategy
Abstract:
The Decision-making Trial and Evaluation Laboratory (DEMATEL) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are pivotal methods in multi-criteria decision-making, addressing causal relationships among criteria and ranking alternatives, respectively. This paper introduces a novel approach, integrating DEMATEL and TOPSIS under the framework of single valued neutrosophic set (SVNS) to handle indeterminacy in decision-making. Applied to a knowledge management strategy case, the method utilizes eight criteria and four alternatives derived from the literature. The process involves DEMATEL for expert weight determination and direct-relation matrix creation through linguistic variable influence. Subsequently, TOPSIS is employed to rank alternatives based on distance measures. The combined method identifies the 'Human Resource Department' as the most crucial in knowledge management strategy. This integrated approach facilitates organizations in pinpointing vital criteria and alternatives for effective knowledge management. Comparative analyses with existing methods are also presented.
Biography:
Prof Abdullah has always prioritised research activities as his biggest passion. His research and expertise focuses on fuzzy set theory of mathematics, decision making, applied statistics, and their applications to social ecology, environment, health sciences and management. He is interested in the measurement of social indicators, measuring index of health related quality of life, environmental evaluation and business using combinations of fuzzy sets theory, multi-criteria decision making and statistics approaches. Thus far, he has successfully led several research projects in the field of decision making, fuzzy mathematics and applied statistics. His research findings have been published in more than 400 publications including 239 refereed journals, 129 conference proceedings, and 35 chapters in book. Prof Abdullah also has authored 14 research books and 3 monographs published by the Unversity Press in the fields of statistics and fuzzy decision making. With 227 documents, 2494 citations in database Scopus and 137 documents, 1393 citations in database Web of Science, Prof Abdullah proved that he is one of the academic leaders in his field. His achievement was further recognised internationally. He has been ranked among the world’s top 2% scientists since 2019 by Stanford University in the field of artificial intelligence and image processing. He was Head of Research Cluster of Data and Digital Sciences, Center of Research Management Office, Universiti Malaysia Terengganu. Currently, he is a senior professor at Faculty Computer Science and Mathematics, Universiti Malaysia Terengganu. Prof Abdullah is a member of the IEEE Computational Intelligence Society, and a member of the International Society on Multiple Criteria Decision Making.