Speech Title: Synthesizing Distributed Algorithms
for Combinatorial Network Optimization
Abstract: Many important network design problems are fundamentally combinatorial optimization problems. A large number of such problems, however, cannot readily be tackled by distributed algorithms. We develop a Markov approximation technique for synthesizing distributed algorithms for network combinatorial problems with near-optimal performance. We show that when using the log-sum-exp function to approximate the optimal value of any combinatorial problem, we end up with a solution that can be interpreted as the stationary probability distribution of a class of time-reversible Markov chains. Selected Markov chains among this class, or their carefully-perturbed versions, yield distributed algorithms that solve the log-sum-exp approximated problem. The Markov Approximation technique allows one to leverage the rich theories of Markov chains to design distributed schemes with performance guarantees. By case studies, we illustrate that it not only can provide fresh perspective to existing distributed solutions, but also can help us generate new distributed algorithms in other problem domains with provable performance, including cloud computing, edge computing, and IoT scheduling.
Biography: Minghua Chen received his B.Eng. and M.S. degrees from the Department of Electronic Engineering at Tsinghua University. He received his Ph.D. degree from the Department of Electrical Engineering and Computer Sciences at University of California Berkeley. He is currently a Professor of School of Data Science, City University of Hong Kong. He received the Eli Jury award from UC Berkeley (presented to a graduate student or recent alumnus for outstanding achievement in the area of Systems, Communications, Control, or Signal Processing) and several best paper awards, including IEEE ICME Best Paper Award in 2009, IEEE Transactions on Multimedia Prize Paper Award in 2009, ACM Multimedia Best Paper Award in 2012, IEEE INFOCOM Best Poster Award in 2021, and ACM e-Energy Best Paper Award in 2023. He is currently a Senior Editor for IEEE Systems Journal and an Executive Member of ACM SIGEnergy (as the Award Chair). His recent research interests include online optimization and algorithms, machine learning in power systems, intelligent transportation systems, distributed optimization, and delay-critical networked systems. He is an ACM Distinguished Scientist and an IEEE Fellow.
Speech Title: Credibility and
Interpretability/Explainability in Critical
Applications of Machine Learning
Abstract: Over the recent years, we have been witnessing numerous and far-reaching developments and applications of Machine Learning (ML). With the plethora of applications found in critical areas such as autonomous vehicles, health care, networks, complex decision-making environments.
Two interrelated challenges become more apparent, namely credibility and interpretability/explainability. Both of them directly impact the acceptance and usefulness of ML constructs in a real-world environment. The credibility is also of concern to any application, especially the one being associated with a high level of criticality.
The notions of interpretability and explainability are formulated and we show how they are realized through a number of auxiliary models built upon the black models of ML constructs. Model-agnostic explainable models are discussed.
Proceeding with a conceptual and algorithmic pursuits, we advocate that the above problems could be formalized in the settings of Granular Computing. We show that to credibility any numeric result be augmented by the associated information granules and the quality of the results is quantified in terms of the characteristics of information granules. Different directions are discussed and revisited including confidence/ prediction intervals, granular embedding of ML models, and granular Gaussian Process models.
When coping with interepretability nd explainability of ML, information granules and their processing offer key advantages in a number of ways: (i) by stressing the product instead of product perspective and emphasizing importance of interactivity between the user and the explanation module, (ii) by incorporating suitable levels of abstraction, (iii)by building explanation layers with rule-based computing, (iv) by defining and quantifying stability of interpretation, and (v) by proposing ideas of granular counterfactual explanation.
Biography: Witold Pedrycz (Life Fellow, IEEE) received the M.Sc. degree in computer science, the Ph.D. degree in computer engineering, and the D.Sci. degree in systems science from the Silesian University of Technology, Gliwice, Poland, in 1977, 1980, and 1984, respectively.,He is a Professor and the Canada Research Chair of computational intelligence with the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada. He is also with the System Research Institute, Polish Academy of Sciences, Warsaw, Poland. He has authored 15 research monographs covering various aspects of computational intelligence, data mining, and software engineering. His current research interests include computational intelligence, fuzzy modeling, granular computing, knowledge discovery and data mining, fuzzy control, pattern recognition, knowledge-based neural networks, relational computing, and software engineering.,Prof. Pedrycz was a recipient of the IEEE Canada Computer Engineering Medal, the Cajastur Prize for Soft Computing from the European Center for Soft Computing, the Killam Prize, and the Fuzzy Pioneer Award from the IEEE Computational Intelligence Society. He is intensively involved in editorial activities. He is an Editor-in-Chief of Information Sciences, WIREs Data Mining and Knowledge Discovery (Wiley), and the International Journal of Granular Computing (Springer). He currently serves as a member of a number of editorial boards of other international journals and is a Former Editor-in-Chief of IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans. He is a Foreign Member of the Polish Academy of Sciences. He is a Fellow of the Royal Society of Canada.(Based on document published on 25 December 2020).
Speech Title: Formal Engineering Methods:
Bridging Formal Methods and Software Engineering
Abstract: Formal methods have been established to overcome the challenges to conventional software engineering by introducing mathematical notation and calculus to support formal specification, refinement, and verification, but they are generally difficult to apply to the development of large-scale and complex systems in practice because of many constraints.
We have developed “Formal Engineering Methods’’ (FEM) as a research area since 1989 to study how formal methods can be effectively integrated into conventional software engineering technologies and process models so that formal techniques can be tailored, revised, or extended to fit the need for improving software productivity and quality in practice. We have also developed a specific FEM called Structured Object-Oriented Formal Language (SOFL) that offers rigorous but practical techniques for system modeling, transformation, and verification, including a three-step formal specification approach, transformation from structured specification to object-oriented implementation, and specification-based inspection and testing. The effective combination of these three techniques can significantly enhance software productivity and quality. In this talk, I will first give a brief introduction to formal methods and formal engineering methods, and then use SOFL as an example to discuss how formal engineering methods bridge formal methods and software engineering for software quality assurance. Finally, I will explain the future development directions in the field.
Biography: Shaoying Liu is a Professor of Software Engineering at Hiroshima University, Japan, IEEE Fellow, BCS Fellow, and AAIA Fellow. He received the Ph.D in Computer Science from the University of Manchester, U.K in 1992, and has experienced working and researching at 9 universities in China, the U.K., and Japan, including Xi’an Jiaotong University, the University of Manchester, the University of York, the University of Oxford, Hosei University, and Hiroshima University. His research interests include Formal Engineering Methods, Specification-based Program Inspection and Testing, Testing-Based Formal Verification (TBFV), Human-Machine Pair Programming(HMPP), Safety-Critical and Complex Systems, and Intelligent Software Engineering Environment. He is a pioneer and leading researcher in Formal Engineering Methods for Software Development. He proposed to use the terminology of "Formal Engineering Methods" in 1997, designed the SOFL (Structured Object-Oriented Formal Language) specification language and method, and founded the ICFEM conference in 1997 and SOFL+MVSL workshop in 2012, respectively. He has led more than 20 research projects funded by government agencies, private foundations, and industry since 1995, published a book entitled "Formal Engineering for Industrial Software Development" with Springer-Verlag, more than 13 edited books, and over 250 papers in refereed journals and international conferences. He has received many awards, including 2020 and 2022 Distinguished Research Awards from IPSJ/SIGSE, the “20 Year ICFEM Impact Award” from ICFEM 2018, “IEEE Reliability Society Japan Joint Chapter 2016 Best Paper Award”, and “Outstanding Paper Award’’ from ICECCS’96. In recent years, he has served as the General Chair of several international conferences, including QRS 2020 and ICECCS 2022. He is an Associate Editor for IEEE Transactions on Reliability and Innovations in Systems and Software Engineering, and a member of IPSJ and IEICE, respectively.