Prof. Seifedine Kadry
IET Fellow, IETE Fellow, IACSIT Fellow
Noroff University College, Norway
Professor Seifedine Kadry has a Bachelor degree in 1999 from Lebanese University, MS degree in 2002 from Reims University (France) and EPFL (Lausanne), PhD in 2007 from Blaise Pascal University (France), HDR degree in 2017 from Rouen University.
At present his research focuses on Data Science, education using technology, system prognostics, stochastic systems, and applied mathematics. He is an ABET program evaluator for computing, and ABET program evaluator for Engineering Tech. He is a Fellow of IET, Fellow of IETE, and Fellow of IACSIT. He is a distinguish speaker of IEEE Computer Society.
Prof. Wenbin Chen
Guangzhou University, China
Professor, School of Computer Science and Network Engineering, master Supervisor. Research interests: Theoretical computer science, including lattice cryptographic algorithms and applications, graph algorithms and applications, online algorithms and approximation algorithms, computational complexity, etc. He received his Ph.D. in Computer science from North Carolina State University in December 2010.
In May 2011, I began to work at Guangzhou University. He has published more than 30 academic papers, including 1 paper in IEEE Transactionson Information Theory, a famous journal of information theory. He has published 8 papers in the internationally renowned journal Theoretical Computer Science.
Title：A primal-dual online algorithm for the k-server problem on weighted HSTs
Abstract：In this talk, we show that there is a 2.5Lln (1+k)-competitive randomized algorithm for the k-sever problem on weighted Hierarchically Separated Trees (HSTs) with depth L when n=k+1 where n is the number of points in the metric space, which improved previous best competitive ratio 12 L ln (1+4L (1+k)).
Prof. Wanyang Dai
Nanjing University, China
Wanyang Dai is a Distinguished Professor in Nanjing University, Chief Scientist in Su Xia Control Technology. He is the current President & CEO of U.S. based (Blockchain & Quantum-Computing) SIR Forum, President of Jiangsu Probability & Statistical Society, Chairman of Jiangsu BigData-Blockchain and Smart Information Special Committee. He received his Ph.D. in mathematics and systems & industrial engineering from Georgia Institute of Technology in USA. He was an MTS and principal investigator in U.S. based AT&T Bell Labs (currently Nokia Bell Labs) with some project won “Technology Transfer” now called cloud system. He was the Chief Scientist in DepthsData Digital Economic Research Institute. He published numerous influential papers in big name journals including Quantum Information Processing, Operations Research, Operational Research, Queueing Systems, Computers & Mathematics with Applications, Communications in Mathematical Sciences, and Journal of Computational and Applied Mathematics. He received various academic awards and has presented over 50 keynote/plenary speeches in IEEE/ACM, big data and cloud computing, quantum computing and communication technology, computational and applied mathematics, biomedical engineering, mathematics & statistics, and other international conferences. He has been serving as IEEE/ACM conference chairs, editors-in-chief and editorial board members for various international journals ranging from artificial intelligence, machine learning, data science, wireless communication, pure mathematics & statistics to their applications.
Title: Smart control algorithm via coupled forward-backward neural nets and federated machine learning
Abstract:We develop a smart control algorithm via coupled forward-backward neural nets and federated machine learning. The constraint of our control system is represented by a stochastic differential equation (SDE) with Levy jumps and boundary reflection, which includes many practical buffer storage (i.e., queueing) systems, scheduling, routing and control systems as special cases. The objective functional can take different forms corresponding to their associated practical systems. The optimal control policies are proposed by designing a smart algorithm through coupled forward-backward neural nets and federated machine learning with time evolution. The policies cover both conventional parameter control (e.g., drift and diffusion control) policies and jump size control (e.g., impulse and singular control) policies. In real-world systems, these policies may represent the demand/resource rate, pricing, and sequential batch assembly policies. Simulation examples will be provided to illustrate the effectiveness of our developed algorithm and policies.