Prof. Witold Pedrycz, IEEE Life Fellow
University of Alberta, Edmonton, Canada
Witold Pedrycz (IEEE Life Fellow) is Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. Dr. Pedrycz is a foreign member of the Polish Academy of Sciences and a Fellow of the Royal Society of Canada. He is a recipient of several awards including Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society, IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society, and 2019 Meritorious Service Award from the IEEE Systems Man and Cybernetics Society.
His main research directions involve Computational Intelligence, fuzzy modeling and Granular Computing, knowledge discovery, pattern recognition, data science, knowledge-based neural networks among others.
Dr. Pedrycz is involved in editorial activities. He is an Editor-in-Chief of Information Sciences, Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley), and Co-editor-in-Chief of Int. J. of Granular Computing (Springer) and J. of Data Information and Management (Springer).
Keynote speakers of previous conference
David Abramson, Fellow of ACM, IEEE, TSE, ACS
Director, Research Compt Cntr
Research Computing Centre
The University of Queensland, Australia
David has been involved in computer architecture and high performance computing research since 1979.
Prior to joining UQ, he was the Director of the Monash e-Education Centre, Science Director of the Monash e-Research Centre, and a Professor of Computer Science in the Faculty of Information Technology at Monash.
From 2007 to 2011 he was an Australian Research Council Professorial Fellow.
David has expertise in High Performance Computing, distributed and parallel computing, computer architecture and software engineering.
He has produced in excess of 200 research publications, and some of his work has also been integrated in commercial products. One of these, Nimrod, has been used widely in research and academia globally, and is also available as a commercial product, called EnFuzion, from Axceleon.
His world-leading work in parallel debugging is sold and marketed by Cray Inc, one of the world's leading supercomputing vendors, as a product called ccdb.
David is a Fellow of the Association for Computing Machinery (ACM), the Institute of Electrical and Electronic Engineers (IEEE), the Australian Academy of Technology and Engineering (ATSE), and the Australian Computer Society (ACS).
Speech Title: Scalable Distributed Infrastructure for Data Intensive Science
Abstract: Modern research and science organisations face challenges in storing and preserving the increasing amounts of data generated by scientific instruments. Data must be delivered in a variety of modes depending on the end use, ranging from Web portals through to supercomputers. Building infrastructure to meet this need is complex and expensive. There is a need for mechanisms that support both managed and unmanaged data in a coherent and scalable way, often over a physically distributed multi-campus environment.
At the University of Queensland, long term hierarchical storage, and many of the computing systems, are housed in a commercial data centre 20 kms from the main campus in St Lucia. Some high performance machines and desktops, and all scientific instruments, are housed on campus. University researchers work with local, national and international collaborators, requiring the need to share data securely and efficiently across a variety of scales.
In this talk I will discuss the ways we are delivering supporting infrastructure and services at the University of Queensland:
- Our COTS based “MeDiCI data fabric” provides seamless access to data in this environment.
- A local-developed meta-data management service (RDM) provides a single point of access for storage requests and improves the management, curation and preservation of data
- A dedicated, flexible and efficient environment (CAMERA) connects linked unmanaged collections to managed repositories.
- Our data transport network delivers data to a range of commodity and novel computing platforms such as the FlashLite data intensive cluster and the Wiener GPU supercomputer.
Yunghsiang S. Han, IEEE Fellow
Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China
Consultant, Theory Lab, Huawei Technologies Co., Ltd.
National Taipei University (Chair Professor)
National Taiwan University of Science and Technology （Chair Professor Emeritus)
Yunghsiang S. Han was born in Taipei, Taiwan, China, in 1962. He received B.Sc. and M.Sc. degrees in electrical engineering from the National Tsing Hua University, Hsinchu, Taiwan, China, in 1984 and 1986, respectively, and a Ph.D. degree from the School of Computer and Information Science, Syracuse University, Syracuse, NY, in 1993. Dr. Han was from 1986 to 1988 a lecturer at Ming-Hsin Engineering College, Hsinchu, Taiwan, China. He was a teaching assistant from 1989 to 1992, and a research associate in the School of Computer and Information Science, Syracuse University from 1992 to 1993. Dr. Han was, from 1993 to 1997, an Associate Professor in the Department of Electronic Engineering at Hua Fan College of Humanities and Technology, Taipei Hsien, Taiwan, China. He was with the Department of Computer Science and Information Engineering at National Chi Nan University, Nantou, Taiwan, China from 1997 to 2004. Dr. Han became a Professor in 1998. He was a visiting scholar in the Department of Electrical Engineering at the University of Hawaii at Manoa, HI from June to October 2001, the SUPRIA visiting research scholar in the Department of Electrical Engineering and Computer Science and CASE center at Syracuse University, NY from September 2002 to January 2004. Dr. Han also was a visiting scholar in the Department of Electrical and Computer Engineering at University of Texas at Austin, TX from August 2008 to June 2009, and Fulbright visiting scholar in the Department of Electrical Engineering and Computer Science and CASE center at Syracuse University, NY from July 2012 to June 2013. He was with the Graduate Institute of Communication Engineering at National Taipei University, Taipei, Taiwan, China from August 2004 to July 2010. From August 2010 to January 2017, Dr. Han was with the Department of Electrical Engineering at National Taiwan University of Science and Technology and became Chair Professor in May 2011. From February 2017 to February 2021, he was with the School of Electrical Engineering & Intelligentization at Dongguan University of Technology. Now he is with the Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China. He is also a Chair Professor at National Taipei University from February 2015 and the consultant of the Theory Lab, Huawei Technologies Co., Ltd.
Dr. Han's research interests are in error-control coding, wireless networks, and security. Dr. Han has conducting state-of-the-art research in the area of decoding error-correcting codes for more than sixteen years. He first developed a sequential-type algorithm based on algorithm A* from artificial intelligence. At the time, this algorithm drew a lot of attention since it was the most efficient maximum-likelihood decoding algorithm for binary linear block codes.
Dr. Han has also successfully applied coding theory in the area of wireless sensor networks. He first proposed a random key pre-distribution scheme in wireless sensor networks based on Blom's protocol, which is an application of Reed-Solomon Codes. This scheme drew tons of attention since it was not only a pioneer work in the respective area but also substantially improved the resilience of the network compared to previous methods. This work has become incredibly famous, and it (along with its conference version) has been cited more than 2200 times since 2003, according to Google scholar. It also appears in almost every book where security in wireless sensor networks is one of the topics.
Dr. Han has published more than 80 journal papers. Most significantly, he has published several highly cited works on wireless sensor networks. Dr. Han has been the TPC members for many IEEE conferences, including the International Conference on Communications (ICC) and Global Communications Conference (GLOBECOM). He also serves as the editors of the International Journal of Ad Hoc and Ubiquitous Computing and the Journal of Internet Technology.
Dr. Han was the winner of the Syracuse University Doctoral Prize in 1994 and a Fellow of IEEE. One of his papers won the prestigious 2013 ACM CCS Test-of-Time Award in cybersecurity.
Speech Title: A Novel Polynomial Basis and Fast Fourier Transform for Finite Fields
Abstract: Finding an n-point Fast Fourier Transform (FFT) algorithm over an arbitrary finite field with additive and multiplicative complexity O(n log(n)) has been a long standing open problem in the coding area. It has been known for a long time that a better FFT algorithm can improve the encoding and decoding complexity of Reed-Solomon (RS) codes, one of the most popular codes in the world. Even though an FFT algorithm over a complexity field with additive and multiplicative complexity O(n log(n)) was invented decades ago, it remains unknown whether such an algorithm exists over finite fields. In this talk, we present the first FFT algorithm over finite fields with additive and multiplicative complexity O(n log(n)). A new basis of polynomial over finite fields is invented and then apply it to the FFT over finite fields. The proposed polynomial basis allows that n-point FFT can be computed in O(n log(n)) finite field operations with extremely small leading constant. Based on this novel FFT algorithm, we then develop the encoding algorithms for the (n = 2r, k) Reed-Solomon codes. Thanks to the efficiency of transform based on the polynomial basis, the encoding can be completed in O(n log2(k)) or O(n log2(n − k) finite field operations. As the complexity of leading factor is small, the algorithms are advantageous in practical applications such as encoding/decoding of Reed-Solomon codes and polynomial multiplications in cryptography.
Prof. Kang Zhang,
Fulbright Distinguished Chair and Professor Emeritus, The University of Texas at Dallas, USA
Kang Zhang is Professor Emeritus of Computer Science at the University of Texas at Dallas. He was a Fulbright Distinguished Chair in 2019. Zhang received his B.Eng. in Computer Engineering from University of Electronic Science and Technology of China in 1982, Ph.D. from the University of Brighton, UK, in 1990, and Executive MBA from the University of Texas at Dallas in 2011. Prior to joining UT-Dallas, he held academic positions in the UK, Australia, and China. Zhang's current research interests include generative art, visual languages, aesthetic computing, and software engineering; and has published 8 books, and over 260 papers in these areas. He is an ACM Distinguished Speaker and on the Editorial Boards of Journal of Big Data, The Visual Computer, Journal of Visual Language and Computing, International Journal of Software Engineering and Knowledge Engineering, International Journal of Advanced Intelligence and 《软件学报》.
Speech Title: High-Speed Edge Bundling for Information Visualization
Abstract:Edge bundling has been widely used to reduce visual clutter and reveal high-level edge patterns for large graphs in information visualization. Due to strong edge attraction, bundled results often show considerable curvature and tangling at bundle intersections. Inappropriate bundling may fail to reveal true data patterns and even mislead users. This talk presents a parametrizable 6-step edge bundling framework called LEB that clearly reveals the patterns of the input graph, with distinguishable and traceable bundles. The bundling results by LEB are also easily adjustable by tuning a small number of parameters. We have conducted a user experiment to test and compare LEB with previous approaches. The experiment on three datasets (including two common ones) demonstrates LEB’s superiority over previous approaches in visualizing data patterns. Our implementation with reusable computation delivers an execution speed fast enough for real-time interaction and animation.
Finally, I will mention other on-going research in computational aesthetics.