Keynote Speaker I

Prof. Tianzi Jiang (MAE, IEEE Fellow, IAPR Fellow, AIMBE Fellow)
The Chinese Academy of Sciences

 

Tianzi Jiang, Professor and Director of the Brainnetome Center at the Institute of Automation, Chinese Academy of Sciences. He obtained PhD degree at Zhejiang University and BSc degree at Lanzhou University. His research interests include neuroimaging, Brainnetome, imaging genetics, and their clinical applications in brain disorders. He is the author or co-author of over 300 reviewed journal papers, with a total citation of over 36000 from Google Scholar and H-index of 89. He was elected a member of the Academy of Europe, a fellow of IEEE, IAPR and AIMBE. He is the recipient of Hermann von Helmholtz Award, Turan Itil Career Contribution Award, Wu Wen-Jun AI Distinguishing Contribution Award, and Natural Science Award of China.

 

Speech Title: "The Human Brainnetome Atlas and its Applications in Brain Diseases"

 

Abstract: The Human Brainnetome atlas has been constructed with brain connectivity profiles obtained using multimodal magnetic resonance imaging. It is in vivo, with fine-grained brain subregions, and with anatomical and functional connection profiles. In this lecture, we will summarize the advance of the human brainnetome atlas, its biological basis and practical applications in brain diseases. We first present the basic ideas of the human brainnetome atlas and the procedure to construct this atlas. Then some parcellation results of the human brain areas with different types of cytoarchitectures will be provided. After that, we will present the biological basis of the brainnetome atlas from aspects of genetics and relationships between structure and functions of the brain. Next, we will show how to use the human brainnetome atlas in practice to address issues in clinical researches. Finally, we will give a brief perspective on multiscale brainnetome atlas and the related neurotechniques.

 

Keynote Speaker II

Prof. Wing-Kin Sung
Chinese University of Hong Kong and Hong Kong Genome Institute

 

Professor Wing-Kin Sung is a Global Stem Professor in the Department of Chemical Pathology, the Chinese University of Hong Kong. He is the director of the Laboratory of Computational Genomics. He is also the Chief Bioinformatics officer (Honorary) in the Hong Kong Genome Institute. His recent research focuses on identifying genomic mutations from high-throughput sequencing data and on understanding the relationship between mutations (in particular, structural variations) and diseases. Prof. Sung received both the B.Sc. and the Ph.D. degree in the Department of Computer Science from the University of Hong Kong in 1993, 1998, respectively. He has over 25 years of experience in Algorithm and Bioinformatics research. Prior to joining CUHK, Professor Sung was a Professor in the Department of Computer Science at the National University of Singapore (NUS) and was a senior group leader at the Genome Institute of Singapore. He is an expert in the field of bioinformatics, who has been leading the development of a number of bioinformatics software and has over 290 high impact papers published in renowned academic journals, including Bioinformatics, Cell, Nature, Nature Genetics and Nucleic Acids Research. In recognition of his research contributions, Professor Sung was conferred the FIT Paper Award (Japan) in 2003, the National Science Award (Singapore) in 2006, and the Young Researcher Award (NUS) in 2008. He has also served in the programming committee for over 70 international conferences.

 

Speech Title: "Repeat-aware Insertion Calling and its Application in Human and Arabidopsis"

 

Abstract: Insertions are one of the major types of structural variations and are defined as the addition of 50 nucleotides or more into a DNA sequence. Several methods exist to detect insertions from next-generation sequencing short read data, but they generally have low sensitivity. Our contribution is two-fold. First, we introduce INSurVeyor (Nature communication, to appear), a fast, sensitive and precise method that detects insertions from next-generation sequencing paired-end data. Using publicly available benchmark datasets (both human and non-human), we show that INSurVeyor is not only more sensitive than any individual caller we tested, but also more sensitive than all of them combined. Furthermore, for most types of insertions, INSurVeyor is almost as sensitive as long reads callers. Second, we provide state-ofthe-art catalogues of insertions for 1,047 Arabidopsis Thaliana genomes from the 1001 Genomes Project and 3,202 human genomes from the 1000 Genomes Project, both generated with INSurVeyor. We show that they are remarkably more complete and precise than existing resources, and important insertions are missed by existing methods.

 

 

Previous Keynote Speakers

 

Keynote Speaker I

Prof. Phoebe Chen
La Trobe University

 

Professor Phoebe Chen is Professor and Chair at the Department of Computer Science and Information Technology, La Trobe University, Melbourne Australia. She was Head of Department of Department of Computer Science and Computer Engineering, La Trobe Uni. Prof Phoebe Chen is a member of the College of Experts of the Australian Research Council. Phoebe received her BInfTech and PhD from the University of Queensland. Prof Chen has been the Chief Investigator of ARC Centre of Excellence in Bioinformatics. Phoebe has been awarded 30 research grants. Professor Chen has been doing multi-discipline research for more than 20 years and has been associate editors of IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Multimedia etc. She has published over 265 research papers, many of them appeared in top journals and conferences such as Artificial Intelligence, Nature Machine Intelligence, Bioinformatics, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Biomedical Engineering, Molecular Systems Biology, Nucleic Acids Research etc. She is ACM SIGMM Vice-Chair, steering committee chairs of Asia-Pacific Bioinformatics Conference (founder) and International conference on Multimedia Modelling. She has been on the program committees of over 100 international conferences, including top ranking conferences such as ICDE, ICPR, ISMB, CIKM etc.

 

Speech Title: "Machine Learning for Computational Complexity on Medical Detection and Diagnosis"

 

Abstract: In this talk, I will present on the computational complexity detection and classification of side effects using machine learning approaches. The effective integration of heterogeneous, multidimensional medical data sources, together with the innovative deployment of machine learning approaches helps reduce or prevent the occurrence of adverse reactions. Machine learning approaches can also be exploited to find replacements for medical detection and diagnosis which have side effects or help to diversify the utilization of medical informatics.

 

Keynote Speaker II

Prof. Hongbing Lu
Fourth Military Medical University

 

Hongbing Lu, Ph.D., professor and director, Faculty of Biomedical Engineering, Fourth Military Medical University. Her research interests cover a spectrum from medical image reconstruction to image analysis for computer-aided detection and diagnosis, including brain network analysis for mental disorder. As the principal investigator of near twenty projects including key projects funded by the National Science Foundation of China, by Ministry of Science and Technology, and by the Military Research Foundation, she has published over 170 research papers including leading journals like Biomaterial, IEEE Trans Med Imag, Euro Radiol, and IEEE Trans Biomed Eng (with single highest citation over 420), holds more than ten US and Chinese licensed patents, and awarded by many prizes including the First Prize of State Science and Technology Award. She is currently the committee chair of the Shaanxi Society of Biomedical Engineering, and has served as an associate editor of IEEE Transactions on Medical Imaging and Medical & Biological Engineering & Computing.

 

Speech Title: "Brain Connectivity Analysis: Methods and Applications"

 

Abstract: Based on the dynamic model of brain network, a series of improvements have been proposed for brain effective connection analysis. By using the above methods, the normal connection patterns of healthy subjects and abnormal brain functions of psychiatric diseases, especially depression, have been analyzed for the identification, subtype classification, and treatment outcome prediction.

 

Keynote Speaker III

Prof. Tuan D. Pham
Prince Mohammad Bin Fahd University, Saudi Arabia

 

Tuan D. Pham currently holds positions as Professor in AI and Founding Director of the Center for Artificial Intelligence at Prince Mohammad Bin Fahd University, Saudi Arabia. The Center for Artificial Intelligence is equipped with state-of-the-art computing facilities and infrastructure. His previous position was Professor of Biomedical Engineering at Linkoping University, University Hospital Campus, Linkoping, Sweden. He was appointed as Professor and Leader of the Aizu Research Cluster for Medical Engineering and Informatics, and the Medical Image Processing Lab, both at the University of Aizu, Japan. Before his appointments in Japan, he was appointed as Associate Professor and the Bioinformatics Research Group Leader at the University of New South Wales, Canberra, Australia. His current research focuses on AI and machine learning methods for image processing, time-series analysis, complex networks, and pattern recognition applied to medicine, biology, and mental health. He serves as an Associate/Section Editor for several scholarly journals, series, and conference proceedings. In 2020, Dr. Pham is selected as an Expert in Artificial Intelligence by the U.S. Food and Drug Administration (FDA) Center for Devices and Radiological Health (CDRH) Network of Digital Health Experts Program (NoDEx).

 

Keynote Speaker IV

Prof. Taesung Park
Seoul National University, South Korea

 

Prof. Taesung Park received his B.S. and M.S. degrees in Statistics from Seoul National University (SNU), Korea in 1984 and 1986, respectively and received his Ph.D. degree in Biostatistics from the University of Michigan in 1990. From Aug. 1991 to Aug. 1992, he worked as a visiting scientist at the NIH, USA. From Sep. 2002 to Aug. 2003, he was a visiting professor at the University of Pittsburgh. From Sep. 2009 to Aug. 2010, he was a visiting professor in Department of Biostatistics at the University of Washington. From Sep. 1999 to Sep. 2001, he worked as an associate professor in Department of Statistics at SNU. Since Oct. 2001 he worked as a professor and currently the Director of the Bioinformatics and Biostatistics Lab. at SNU. He served as the chair of the bioinformatics Program from Apr. 2005 to Mar. 2008, and the chair of Department of Statistics of SNU from Sep. 2007 and Aug. 2009. He has served editorial board members and associate editors for the international journals including Genetic Epidemiology, Computational Statistics and Data Analysis, Biometrical Journal, and International journal of Data Mining and Bioinformatics. His research areas include microarray data analysis, GWAS, gene-gene interaction analysis, and statistical genetics.

 

Keynote Speaker V

Prof. Fabio Roli, IEEE&IAPR Fellow
University of Cagliari, Italy

 

Fabio Roli is a Full Professor of Computer Science at the University of Cagliari, Italy, and Director of the Pattern Recognition and Applications laboratory (https://pralab.diee.unica.it/). He is partner and R&D manager of the company Pluribus One that he co-founded (https://www.pluribus-one.it ). He has been doing research on the design of pattern recognition and machine learning systems for thirty years. His current h-index is 71 according to Google Scholar (April 2021). He has been appointed Fellow of the IEEE and Fellow of the International Association for Pattern Recognition. He was a member of NATO advisory panel for Information and Communications Security, NATO Science for Peace and Security (2008 – 2011). Prof. Roli is the recipient of the 2020 Pattern Recognition Medal of the international scientific journal Pattern Recognition, and the 2020 IAPR Pierre Devijver Award, granted to an outstanding scientist who has significantly contributed to the field of statistical pattern recognition.