Applications of Advanced Data Mining Techniques on Healthcare Data Analytics


Distinguished Professor Chengqi Zhang

Associate Vice-President (Research Relationships China)

University of Technology Sydney

Chair of the ACS National Committee for Artificial Intelligence,

General Chair of the 2024 International Joint Conference on Artificial Intelligence

The Title of Speech: Applications of Advanced Data Mining Techniques on Healthcare Data Analytics

Abstract of Speech:

The embedding-based data mining is to transform the raw data into useful information that is easy to consume by the downstream tasks, such as classification, predictive analysis, and clustering. The embedding function is traditionally dominated by various pattern mining algorithms and is recently driven by the deep learning-based embedding technique. In this talk, I will briefly introduce our recent data mining practices on the application domain of big healthcare data.

Biography of the Speaker:

Professor Chengqi Zhang 's key research fields are Artificial Intelligence, data mining, deep learning and their applications. With h-index of 54, he has a total of 339 scientific papers that most of them have been published in the top-class journals and conference proceedings. Since 2004, he has been awarded 14 ARC grants. He has been invited to deliver 26 keynote speeches at international conferences. In 2011, he won the New South Wales Science and Engineering (Engineering and ICT category) Award and the UTS Vice Chancellor Award for Research Excellence (Leadership category). He has supervised more than 30 PhD students to completion, of which eight graduates are now full professors. His recent research work with his PhD students has received support from the Australian Artificial Intelligence Institute (AAII), UTS.

He had been the founding director of the Centre for Quantum Computation and Intelligent Systems (QCIS, 2008-2016) - a UTS flagship research centre with 30+ academic staffs and 100+ PhD students. He was the founding director for UTS Data Science (2016-2017) that was a UTS organization to promote the cross-discipline research collaboration in terms of data-driven applications. He was appointed to Associate Vice President at UTS in 2017.

had been elected as the Chair of the ACS National Committee for Artificial Intelligence since 2005. He is a Fellow of the Australian Computer Society (ACS). In addition, from 2012 to 2014, he had been a college of expert on the ARC panel. He was also elected as the Chairman (2014-2018) of the IEEE Computer Society Technical Committee on Intelligent Informatics (TCII). He has served three world-leading academic conferences (ICDM-2010, KDD-2015, IJCAI-2024) as the General Chair.

Data Mining for Novel Discovery in Bioinformatics and Medical Data Analytics


Professor Phoebe Chen

La Trobe University

Editor-in-Chief: Current Bioinformatics

Hosts the ARC Centre of Excellence in Bioinformatics from 2008 to present

The Title of Speech: Data Mining for Novel Discovery in Bioinformatics and Medical Data Analytics

Abstract of Speech:

Bioinformatics and medical data analysis has become more accurate with the emergence of modelling methods based on data mining development. The huge quantities of data and escalating demands of modern biomedical research increasingly require the sophistication and power of computational techniques for their novel discovery. In this talk, I will demonstrate data mining methods for gathering high-quality approximations and modelling of genomic and medical data analytics.

Biography of the Speaker:

Phoebe Chen is Professor and Chair at the Department of Computer Science and Information Technology, La Trobe University, Melbourne Australia. She is also the Chief Investigator of ARC Centre of Excellence in Bioinformatics. Phoebe received her PhD at the University of Queensland. She has published over 250 research papers, many of them appeared in top journals and conferences. She has been associate editor for IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Multimedia, Editor-in-Chief Current Bioinformatics. Phoebe is steering committee chair of Asia-Pacific Bioinformatics Conference (founder) and International conference on Multimedia Modelling. She has been on the program committee’s chairs and member of over 100 international conferences.

Computer and Robot Vision


Professor Mohammed Bennamoun

The University of Western Australia

The Title of Speech: Computer and Robot Vision

Abstract of Speech:

Robotics has made significant progress in cases of structured and constrained environments, e.g. manufacturing. However, it is still in its infancy when it comes to applications in unstructured and unconstrained situations e.g. social environments. In some aspects such as speed, strength and accuracy, robots have superior capacities compared to humans but that is not the case for person/object recognition, language, manual dexterity, and social interaction and understanding capabilities.

Developing a computer vision system with Human visual recognition capabilities has been a very big challenge. It has been hindered mainly by: (i) the non-availability of 3D sensors (with the capabilities of the human eye) which are able to simultaneously capture appearance (colour and texture), surface shapes of objects while in motion, and (ii) the non-availability of algorithms to process this information in real-time. Recently, a number of affordable 3D sensors appeared in the market which is resulting in the development of practical 3D systems. Examples include 3D object and 3D face recognition for biometric applications, as well as the development of home robotic platforms to assist the elderly with mild cognitive impairment.

The objective of the talk will be to describe few 3D computer vision projects and tools used towards the development of a platform for assistive robotics in messy living environments. Various systems with applications and their motivations will be described including 3D object recognition, 3D face/ear biometrics, Grasping of unknown objects, and systems to estimate the 3D pose of a person.

Biography of the Speaker:

He is currently a Winthrop Professor in the Department of Computer Science and Software Engineering at The University of Western Australia (UWA). He served as the Head of the School of Computer Science and Software Engineering at UWA for five years (February 2007-March 2012). His areas of interest include conmputer vision (particularly 3D) e.g., object recognition & biometrics; machine/deep learning; robotics (e.g., obstacle avoidance and robot grasping); signal/image processing; control theory.

He published 130+ journal and 250+ conference publications. He served as a guest editor for a couple of special issues in International journals, such as a Special Issue in the IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), the International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI). He was selected to give conference tutorials at the European Conference on Computer Vision (ECCV) and the International Conference on Acoustics Speech and Signal Processing (IEEE ICASSP), and the International Conference on Computer Vision and Pattern Recognition (CVPR 2016). He was invited to deliver a tutorial at the International Summer School on Deep Learning (DeepLearn 2017). He is currently Senior Area Editor of the IEEE Signal Processing Letters and Associate Editor of the IEEE Transactions on Image Processing (impact Factor = 9.34).

High-Performance Heterogeneous Data Processing for AI Applications


Professor X. Sean Wang

Fudan University

The Title of Speech: High-Performance Heterogeneous Data Processing for AI Applications

Abstract of Speech:

At the current stage of its development, Artificial Intelligence mostly is via methods that seek knowledge from data, which often requires massive computing resources to process massive amounts of heterogenous data in order to gain useful knowledge that can be applied automatically in various scenarios. Due to the large differences in modes of computation between the various steps of data processing, high-performance data processing needs to handle heterogeneous hardware and software platforms. Some changes are needed regarding the software stack of the data processing system to achieve the goal of high performance. This talk is to outline some research towards this goal, and to present some relevant results.

Biography of the Speaker:

Xiaoyang Sean Wang is Professor at the School of Compute Science, Fudan University, Shanghai, China. He received his PhD degree in Computer Science from the University of Southern California, USA. Before joining Fudan University in 2011, he held the Dorothean Chair Professor in Computer Science position at the University of Vermont, USA, and served as a Program Director at the National Science Foundation, USA, in the IIS division. His research has been supported by NSF and NSFC, as well as other US and Chinese funding agencies. He has published widely in the general area of databases and information security, and was a recipient of the US National Science Foundation CAREER award. He served as the general chair of IEEE ICDE 2011 held in Washington DC and ACM CIKM 2014 in Shanghai, China, and in various other roles for international conferences and journals. He’s currently Editor in Chief of the Springer Journal of Data Science and Engineering, associate editor of IEEE TCC, and past associate editor of IEEE TKDE. He’s also currently on the steering committees of the IEEE ICDE and IEEE BigComp conference series, and past Chair of WAIM (now merged into ApWeb-WAIM) Steering Committee.

Contextual Correlation Visual Media Understanding and Search


Professor Shuqiang Jiang

Chinese Academy of Sciences

The Title of Speech: Contextual Correlation Visual Media Understanding and Search

Abstract of Speech:

The amount of multimedia data from different sources are growing very fast, especially image and video data. This causes that visual media are becoming the majority component of information acquisition, exchange and consumption in various applications. Visual search has important application potentials, where visual representation and understanding plays an important role. In this talk, I will first give a background analysis about this research area and then introduce our recent processes on two aspects. One aspect is contextual based visual understanding, including techniques of object-object-relation based image recognition, scene graph based image captioning, attribute based venue recognition. Another aspect is regional contextual analysis for visual search, including techniques of common object detection, expressional region retrieval, and two stage regional processing integration for instance-based image search.

Biography of the Speaker:

Shuqiang Jiang is a professor with the Institute of Computing Technology, Chinese Academy of Sciences(CAS) and a professor in University of CAS. He is also with the Key Laboratory of Intelligent Information Processing, CAS. His research interests include multimedia processing and intelligent understanding, pattern recognition, and computer vision. He has authored or coauthored more than 100 papers on the related research topics. He was supported by the New-Star program of Science and Technology of Beijing Metropolis in 2008, NSFC Excellent Young Scientists Fund in 2013, Young top-notch talent of Ten Thousand Talent Program in 2014. He won the CAS International Cooperation Award for Young Scientists in 2014, and the CCF Award of Science and Technology in 2012. He is the senior member of IEEE and CCF, member of ACM, Associate Editor of ACM ToMM, Multimedia Tools and Applications. He is the vice Chair of IEEE CASS Beijing Chapter, vice chair of ACM SIGMM China chapter. He has served as an organization member of more than 20 academic conferences, including the general chair of ICIMCS 2015, program chair of ICIMCS2010, PCM2017, ACM Multimedia Asia2019, He has also served as a TPC member for many conferences, including ACM Multimedia, CVPR, ICCV, IJCAI, ICME, ICIP, etc.

Advances of deep learning-based COVID-19 Detection


Professor Yu-Dong Zhang

School of Informatics, University of Leicester, UK

The Title of Speech: Advances of deep learning-based COVID-19 Detection

Abstract of Speech:

COVID-19 is a pandemic disease, which already caused more than 1.15 million deaths till 27/Oct/2020. Deep learning is a rapidly advancing field in recent years, in terms of both methodological development and practical applications. It allows computational models of multiple processing layers to learn and represent data with multiple levels of abstraction. It is able to implicitly capture intricate structures of large-scale data and ideally suited to some of the hardware architectures that are currently available. This invited speak presents the recent advances of using deep learning technologies in COVID-19 detection.

Biography of the Speaker:

Prof. Yu-Dong Zhang received his PhD degree in Signal and Information Processing from Southeast University in 2010. He worked as a postdoc from 2010 to 2012 with Columbia University, USA; and as an assistant research scientist from 2012 to 2013 with Research Foundation of Mental Hygiene (RFMH), USA. He served as a Full Professor from 2013 to 2017 with Nanjing Normal University. Now he serves as Professor with Department of Informatics, University of Leicester, UK. His research interests include deep learning and medical image analysis.

Prof. Zhang is the Fellow of IET (FIET), and Senior Members of IEEE and ACM. He was included in “Most Cited Chinese researchers (Computer Science)” by Elsevier from 2014 to 2018. He was the 2019 recipient of “Highly Cited Researcher” by Web of Science. He won “Emerald Citation of Excellence 2017” and “MDPI Top 10 Most Cited Papers 2015”. He was included in "Top Scientist" in Guide2Research. He is the author of over 250 peer-reviewed articles, including more than 30 “ESI Highly Cited Papers”, and 3 “ESI Hot Papers”. His citation reached 13118 in Google Scholar with h-index of 63, and 7779 in Web of Science with h-index of 50. He has conducted many successful industrial projects and academic grants from NSFC, NIH, Royal Society, EPSRC, MRC, and British Council.