Plenary Speaker - ICIC 2023

ICIC 2023 Plenary Speakers

Vasu Alagar

C.L.Philip Chen

Prashan Premaratne

Yongduan Song

Andrew E Teschendorff

Fangxiang Wu

Tiantian Xu

Lefei Zhang

	Vasu Alagar PhD & Professor Emeritus, Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada
	C.L. Philip Chen PhD & Professor, FIEEE, FAAAS, FIAPR, MAE, Dean, School of Computer Science and Engineering, South China University and Technology, China
	Prashan Premaratne PhD & Senior Lecturer, Senior Member IEEE, Australian TEQSA Expert in Artificial Intelligence, School of Electrical, Computer & Telecommunications Engineering, University of Wollongong, Australia.
	Yongduan Song PhD & Professor, IEEE/AAIA/CAA Fellow, FIEAS, IEEE TNNLS Editor-in-Chief Dean, Research Institute of Artificial Intelligence, Chongqing University
	Andrew E Teschendorff PhD & Professor, Head of Computational Systems Epigenomics, Shanghai Institute for Nutrition and Health, CAS, and Honorary Research Fellow University College London
	Fangxiang Wu PhD & Professor, Departments of Computer Science, Biomedical Engineering, and Mechanical Engineering, University of Saskatchewan, Canada
	Tiantian Xu PhD & Professor, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
	Lefei Zhang PhD & Professor, School of Computer Science, Wuhan University, Wuhan, China

Patient-Centered Treatment Based on Semantics of Similar Situations

Vasu Alagar, PhD, Professor Emeritus
Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada H3G 1M8
Email:vangalur.alagar@concordia.ca

Abstract:In patient-centered care the attending physician, in consultation with the patient, determines a personalized treatment plan for the patient. In order to avoid delay and expensive pre-diagnosis procedure, it is suggested that the knowledge of existing patient cohort be used for comparative effectiveness studies and better understanding of patient health situation. In this talk we define a health situation to include disease type, drugs administered, and set of reactions. By a similarity computation on health situations, it is possible to discover patient cohort for a given patient provided the similarity is based on correct semantics. We propose a formal generic structure of Electronic Health Record (HER) in which a situation can be formally represented. By formal we mean the situation characteristics are captured by different attributes and their data types in HER, thus a HER is the virtual patient having the health situation. We explain scoring functions for attribute pairs, defined on ontology-based semantic graphs, and how they are aggregated to compute similarity between situations. We have found several scoring functions. The experimental results demonstrate that they are all effective in ranking the patients in a cohort group. We believe that by leveraging drug similarity in combination with disease similarity, our method could support the treating team to remain more vigilant and prepared for any disease complication or detection of new symptoms at the earliest. It can lead them to take quick and confident decisions with better outcome.

Bio-Sketch:Vasu Alagar is an Emeritus Professor in the Department of Computer Science and Software Engineering at Concordia University, Montreal, Canada. His academic career, spawning over four decades, has been rich and varied that includes Algorithm Development and Complexity Analysis, Formal Methods, Language Semantics, and Rigorous Development of Large Complex Systems. His recent research centers around Formal Component-based Software Development, Context-aware Systems, and in particular the embedding of context in programming languages and Service-oriented Systems, and Big Data discovery and Analytic. He has written and edited several books and conference proceedings. He has graduated more than 150 masters and PhD students, and his research results are widely published in many journals and conferences.

Fuzzy Broad Learning (Neuro) Systems (FBLS): Explainability and Analysis on the Tradeoff between Accuracy and Complexity

C.L. Philip Chen, FIEEE, FAAAS, FIAPR, MAE
Dean, School of Computer Science and Engineering, South China University and Technology

Abstract:The fuzzy broad learning system (FBLS) is a recently proposed neuro-fuzzy model that shares the similar structure of a broad learning system (BLS). It shows high accuracy in both classification and regression tasks and inherits the fast computational nature of a BLS. However, the ensemble of several fuzzy subsystems in an FBLS decreases the possibility of understanding the fuzzy model since the fuzzy rules from different fuzzy systems are difficult to combine together while keeping the consistence. To balance the model accuracy and complexity, this talk is to discuss a synthetically simplified FBLS with better interpretability, named compact FBLS (CFBLS), which can generate much fewer and more explainable fuzzy rules for understanding. In such a way, only one traditional Takagi�CSugeno�CKang fuzzy system is employed in the feature layer of a CFBLS, and the input universe of discourse is equally partitioned to obtain the fuzzy sets with proper linguistic labels accordingly. The random feature selection matrix and rule combination matrix are employed to reduce the total number of fuzzy rules and to avoid the “curse of dimensionality.” The experiments on the popular datasets indicate that the CFBLS can generate a smaller set of comprehensible fuzzy rules and achieve much higher accuracy than some state-of-the-art neuro-fuzzy models. Moreover, the advantage of CFBLS is also verified in a real-world application.

Bio-Sketch:C. L. Philip Chen is the Chair Professor and Dean of the College of Computer Science and Engineering, South China University of Technology. He is a Fellow of IEEE, AAAS, IAPR, CAA, and HKIE; a member of Academia Europaea (AE), and a member of European Academy of Sciences and Arts (EASA). He received IEEE Norbert Wiener Award in 2018 for his contribution in systems and cybernetics, and machine learnings, and IEEE Joseph G. Wohl Outstanding Career award, and Wu WenJun Outstanding Contribution award from Chinese AI Association, received two times best transactions paper award from IEEE Transactions on Neural Networks and Learning Systems for his papers in 2014 and 2018. He is a highly cited researcher by Clarivate Analytics from 2018-2022. His current research interests include cybernetics, systems, and computational intelligence. He was the Editor-in-Chief of the IEEE Transactions on Cybernetics, the Editor-in-Chief of the IEEE Transactions on Systems, Man, and Cybernetics: Systems, and the President of IEEE Systems, Man, and Cybernetics Society.

Human Computer Interaction Using Hand Gestures �C Past, Present and Future

Prashan Premaratne, PhD & Senior Lecturer
Senior Member IEEE, Australian TEQSA Expert in Artificial Intelligence
School of Electrical, Computer & Telecommunications Engineering
University of Wollongong, New South Wales, Australia

Abstract:Today, with the advent of technology especially due to advances in artificial intelligence, voice recognition-based computer interactions are unprecedented. Due to the lightening advances in object detection with the emergence of YOLO algorithms, object detection is highly accurate and in realtime. Yet, hand gesture recognition hasn’t received the same advancements due to many challenges it faced. One of the major challenges is the temporal information present in hand signs which are dynamic in nature. They convey a message few sentences in length. Despite the modern research is highly advanced in detecting objects in images using massive computing power, tracking a hand sign with its intricate details and interpreting a dynamic hand gesture has been an enormous challenge. Many researchers predict that RNN will be the future for recognising such temporal visual data, yet, the results are still in its infancy.

Bio-Sketch: Prashan was born in Sri Lanka in 1972 and was awarded an Australian government scholarship under John Crawford Scholarship Scheme (JCSS) to pursue undergraduate studies at the University of Melbourne, Australia in 1994. Since 2003, he has been an academic at the University of Wollongong, Australia and is currently a Senior Lecturer at the School of Electrical, Computer and Telecommunications Engineering. In 2005, he developed a computer vision-based system to control any computer interface which resulted in worldwide acclaim which was called ‘The Wave Controller’. Dr. Premaratne is a Senior Member of IEEE and is the author of the book “Human Computer Interaction Using Hand Gestures” published by Springer International. Dr. Premaratne has been a founding member of the International Conference on Intelligent Computing (ICIC). He has been the program co-chair, tutorial chair, plenary speech chair and International Liaison Chair for the past 19 years and has received Outstanding Leadership Award for his contribution to ICIC in 2015. Dr. Premaratne has published over one hundred publications and is also a reviewer for major International Journals. He has been Guest Editor for many technological Journals over the years and was also an Assistant Editor of Springer Journal of Cognitive Science.

Several Critical Issues in Neural Network (NN) Driven Control Design and Analysis

Yongduan Song, IEEE/AAIA/CAA Fellow, FIEAS, IEEE TNNLS Editor-in-Chief
Dean, Research Institute of Artificial Intelligence, Chongqing University.

Abstract:Neural networks and related learning algorithms are crucial components of artificial intelligence. The utilization of neural networks combined with learning algorithms for controller design has become a mainstream direction in the field of intelligent control. This talk will examine the typical NN driven design approaches and expose several critical issues related to functionality and effectiveness of the NN based control methods.

Bio-Sketch:Professor Yong-Duan Song is a Fellow of IEEE, Fellow of AAIA, Fellow of International Eurasian Academy of Sciences, and Fellow of Chinese Automation Association. He was one of the six Langley Distinguished Professors at National Institute of Aerospace (NIA), USA and register professional engineer (USA). He is currently the dean of Research Institute of Artificial Intelligence at Chongqing University. Professor Song is the Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems (TNNLS) and the founding Editor-in-Chief of the International Journal of Automation and Intelligence.

Using Network Physics to Improve Analysis and Interpretation of Single-Cell Omic Data

Andrew E Teschendorff, PhD & Professor
Head of Computational Systems Epigenomics, CAS Key-Lab of Computational Biology, Shanghai Institute for Nutrition and Health, Chinese Academy of Sciences, and Honorary Research Fellow University College London

Abstract:Graph-theory and network physics are branches of complexity science that have found ubiquitous successful applications in science generally. This talk will describe a number of concrete examples where network-theoretical concepts have entered the relatively young field of single-cell genomics, driving important breakthroughs and discoveries. One example shows how the differentiation state of single cells can be successfully modelled in terms of the diffusion network entropy of a stochastic signaling process in the cell. The talk will further describe how this concept has led to the identification of cancer-stem-cells, the presumed cells of origin of tumors, opening up new strategies for personalized and preventive medicine. Another example explores the use of node-attribute-aware clustering algorithms to detect differential abundance of cell-types in relation to aging and disease. I will demonstrate how cell-attribute aware clustering of single-cell data can improve the sensitivity to detect important shifts in cell-type abundance, including increased stem-cell fractions in colonic polyps or loss of olfactory sensory neurons in Covid-19 patients experiencing long-term smell loss.

Bio-Sketch:Andrew Teschendorff studied Mathematical Physics at the University of Edinburgh (1990-1995) under the supervision of Physics Nobel Laureate Peter Higgs. In 2000 he obtained a PhD in Theoretical Physics from Cambridge University. In 2003 he became a Senior Research Fellow in Statistical Cancer Genomics at the University of Cambridge. In 2008 he moved to University College London (UCL) to work in Statistical Cancer Epigenomics and where he was awarded the Heller Research Fellowship. He currently holds an appointment as a PI at the CAS Key Lab of Computational Biology in Shanghai, formerly a joint CAS-Max-Planck Partner Institute for Computational Biology, and remains an Honorary Research Fellow at University College London. His research interests are broad and include Cancer System-omics & Systems Biology and Network Physics. He is well-known for developing pioneering statistical methods for analyzing various forms of genomic data, notably epigenomics and single-cell data. Professor A. Teschendorff has a Google H-index of 77, more than 150 peer-reviewed publications, including 8 book-chapters. He is an Associate Editor for many journals, including notably Genome Biology, and a reviewer and statistical advisor for journals that include Nature, Science, Bioinformatics, PLoS Computational Biology and IEEE Transactions on Computational Biology & Bioinformatics. He is a recipient of the Wolfson College Jennings Prize, Cambridge-MIT Initiative and Isaac Newton Trust Awards, a Wellcome Trust VIP Award, a CAS Visiting Professorship and a CAS-Royal Society Newton Advanced Fellowship. He holds various patents on algorithms for cancer risk prediction and cell-type deconvolution.

Intelligent Computing: from Matrix Factorization to Deep Network, for Biomarker Discovery

FangXiang Wu, PhD & Professor
Departments of Computer Science, Biomedical Engineering, and Mechanical Engineering, the University of Saskatchewan.

Abstract:Intelligent computing refers to the field of computer science and technology that focuses on developing computational systems and algorithms to perform tasks that typically require human intelligence. As one of intelligent computing subfields, machine learning focuses on designing and training computer algorithms to learn from and act on data. A biomarker?is a measurable indicator of some biological state or condition, including molecular biomarkers, cellular biomarkers, or digital biomarkers. In this talk, after an introduction to machine learning formulation, I will present some of research work from my group in the areas of intelligent computing, from matrix factorization to deep network, for molecular biomarker discovery.

Bio-Sketch:Dr FangXiang Wu is?currently a full professor in the Departments of Computer Science,Biomedical Engineering, and Mechanical Engineering at the University of Saskatchewan. His research interests include Artificial Intelligence,Machine/Deep Learning,Computational Biology, Health Informatics, Medical Image Analytics,and Complex Network Analytics. Dr. Wu has published about 350 journal papers and more than 130 conference papers.?His total google scholar citations are over 13000, h-index is 55 (dated in early June, 2023). He is among top 2% world's scientists ranked by Stanford?University. Dr Wu is serving as the editorial board member of several international journals (including IEEE TCBB, Neurocomputing, etc.) and as the guest editor of numerous international journals, and as the program committee chair or member of many international conferences. He is an IEEE senior member.

Motion Control of Magnetically Actuated Microrobots Towards Targeted Therapy

Tiantian Xu, PhD & Professor
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Email:tt.xu@siat.ac.cn

Abstract:Untethered, wirelessly controlled microrobots have a broad application prospects for the bioengineering due to their small scales. Multiple small-scale robots enable cooperation and increase the operating efficiency. However, independent control of multiple magnetic small-scale robots is a great challenge, because the robots receive identical control inputs from the same external magnetic field. We propose a novel strategy of completely decoupled independent control of magnetically actuated flexible swimming millirobots. The strategy is verified by experiments of independent position control of up to four millirobots and independent path following control of up to three millirobots with small errors. Then, we propose an adaptive leader-follower formation control of two magnetically actuated millirobots with heterogeneous magnetization and achieved an autonomous navigation in confined environments.

Bio-Sketch:Tiantian Xu is currently Professor in Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. She received the Ph.D. degree at University of Pierre and Marie Curie, Paris, France. Her research interests are currently focused on magnetic microrobots, soft robots, medical robots, and etc. She has published over 20 IEEE Transactions papers, including TRO, T-cyber, TMECH, TASE, 6 of them are ESI high cited papers. She has received the NSFC excellent young scholar in 2020, the best application paper award in IROS2019, and the Second Prize of Wu Wenjun Natural Science of Artificial Intelligence in 2021 as first author, CAA Young Scientist. She is associate editor for TRO, TASE and RAL.

AI Innovation for Big Vision Data

Lefei Zhang, PhD & Professor
School of Computer Science, Wuhan University, Wuhan, China.
Email:zhanglefei@whu.edu.cn

Abstract:Artificial intelligence (AI) plays a growing role in all traditional areas. In this talk, we will introduce our recently developed AI techniques for computer vision data processing tasks, including image super-resolution, inpainting, semantic segmentation, and object detection. From these successful examples, we observe that the carefully designed AI algorithms and networks are usually inspired by human experiences of solving problems in practice. Furthermore, benefit from the strong support of the computational resources and big data, AI algorithms could reach even exciting performance. However, there are also critical concerns exist. In the future work, we will study how to run the AI models with extremely limited human expert labeled data, to serve for more challenging tasks such as autonomous driving and medical data analysis.

Bio-Sketch:Lefei Zhang received the B.S. and Ph.D. degrees from Wuhan University, Wuhan, China, in 2008 and 2013, respectively. He was a Big Data Institute Visitor with the Department of Statistical Science, University College London, U.K., and a Hong Kong Scholar with the Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China. He is a professor with the School of Computer Science, Wuhan University, Wuhan, China, and also with the Hubei Luojia Laboratory, Wuhan, China. His research interests include pattern recognition, image processing, and remote sensing. Dr. Zhang serves as a topical editor of IEEE Transactions on Geoscience and Remote Sensing, an associate editor of Pattern Recognition, and a section editor-in-chief of Remote Sensing.