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.