Invited Keynote Speakers
ARC Laureate Fellow
Professor, School of Computer Science
Member, the Brain and Mind Centre
The University of Sydney, Australia
Keynote Title: I’am AI
Abstract: Since the concept of Turing machine has been first proposed in 1936, the capability of machines to perform intelligent tasks went on growing exponentially. Artificial Intelligence (AI), as an essential accelerator, pursues the target of making machines as intelligent as human beings. It has already reformed how we live, work, learning, discover and communicate. In this talk, I will review our recent progress on AI by introducing some representative advancements from algorithms to applications, and illustrate the stairs for its realization from perceiving to learning, reasoning and behaving. To push AI from the narrow to the general, many challenges lie ahead. I will bring some examples out into the open, and shed lights on our future target. Today, we teach machines how to be intelligent as ourselves. Tomorrow, they will be our partners to get into our daily life.
Speaker’s Bio: Professor Dacheng Tao is a Full Professor of Computer Science in the School of Computer Science at The University of Sydney. Professor Tao’s research interests include artificial intelligence (AI), computer vision, deep learning, statistical learning and their applications to neuroscience, robotics, video surveillance and medical informatics. His research is at the forefront of heralding the next generation of autonomous machines, which will have a monumental impact on key aspects of industry and the economy, including driverless cars, automated manufacturing and environmental change monitoring/emergency detection.
He has significantly contributed to national and international AI technology, systems development and knowledge. His research results have expounded in one monograph, as well as more than 500 publications in prestigious journals and prominent conferences, with multiple best paper awards.
Nanyang Associate Professor,
School of Computer Science and Engineering
Data Science and AI Center (DSAIR)
Nanyang Technological University, Singapore
Keynote Title: Convolutional Neural Networks on Graphs
Abstract: In the past years, deep learning methods have achieved unprecedented performance on a broad range of problems in various fields from computer vision to speech recognition. So far research has mainly focused on developing deep learning methods for grid-structured data, while many important applications have to deal with graph-structured data. Such geometric data are becoming increasingly important in computer graphics and 3D vision, sensor networks, drug design, biomedicine, recommendation systems, NLP and computer vision with knowledge graphs, and web applications. The purpose of this talk is to introduce convolutional neural networks architectures on graphs, as well as applications for this class of problems.
Speaker’s Bio: Xavier Bresson (PhD 2005, EPFL, Switzerland) is Associate Professor in Computer Science and member of the Data Science and AI Research Centre at NTU, Singapore. He is a leading researcher in the field of AI and graph deep learning, a new framework that combines graph theory and deep learning techniques to tackle complex data domains in natural language processing, computer vision, quantum chemistry, physics, neuroscience, genetics and social networks. In 2016, he received the highly competitive Singaporean NRF Fellowship of $2.5M to develop these new deep learning techniques. He was also awarded several research grants in the U.S. and Hong Kong. As a leading researcher in the field, he has published more than 60 peer-reviewed papers in the leading journals and conference proceedings in machine learning, including articles in NeurIPS, ICML, ICLR, CVPR, JMLR. He has organized several international workshops and tutorials on AI and deep learning in collaboration with Facebook, NYU and Imperial College such as the 2019 and 2018 UCLA workshops (https://bit.ly/2N65idn, https://bit.ly/2TC0hug), the 2017 CVPR tutorial (https://bit.ly/2vJbRa0) and the 2017 NeurIPS tutorial (https://bit.ly/2YsFvOx). He has been teaching undergraduate, graduate and industrial courses in AI and deep learning since 2014 at EPFL (Switzerland), NTU (Singapore) and UCLA (U.S.) such as https://bit.ly/2FuDQAF