Keynote Speakers
Akira Hirose
Professor, Department of Electrical Engineering and Information Systems
The University of Tokyo, Japan
Brief Speaker Bio :

Akira Hirose received the Ph.D. degree in electronic engineering from the University of Tokyo in 1991. In 1987, he joined Research Center for Advanced Science and Technology (RCAST), the University of Tokyo, as Research Associate. In 1991, he was appointed as Instructor at RCAST. From 1993 to 1995, on leave of absence from the University of Tokyo, he joined the Institute for Neuroinformatics, University of Bonn, Bonn, Germany. He is currently Professor with the Department of Electrical Engineering and Information Systems, the University of Tokyo. The main fields of his research interests are wireless electronics and neural networks. In the fields, he published several books such as Complex-Valued Neural Networks, 2nd Edition (Springer 2012). He served as the Founding President of Asia-Pacific Neural Network Society (APNNS) (2016), President of Japanese Neural Network Society (JNNS) (2013-2015), Vice President of the IEICE Electronics Society (ES) (2013-2015), Editor-in-Chief of the IEICE Transactions on Electronics (2011-2012), Associate Editor of journals such as the IEEE TRANSACTIONS ON NEURAL NETWORKS (2009-2011), IEEE GEOSCIENCE AND REMOTE SENSING NEWSLETTER (2009-2012), Chair of the Neurocomputing Technical Group in the IEICE, Founding Chair of the IEEE CIS NNTC Complex-Valued Neural Network Task Force (2010-), Governing Board Member of APNNA/APNNS (2006-), IEEE Geoscience and Remote Sensing Society (GRSS) All Japan Chapter Chair (2013-2015) and IEEE Computational Intelligence Society (CIS) All Japan Chapter Chair (2017- 2018). He also served as the General Chair of Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) 2013 Tsukuba, International Conference on Neural Information Processing (ICONIP) 2016 Kyoto, and International Geoscience and Remote Sensing Symposium (IGARSS) 2019 Yokohama. Dr. Hirose is a Fellow of the IEEE and the IEICE, and a member of JNNS and APNNS.

Chandrajit Bajaj
Computational Applied Mathematics Chair in Visualization
Professor of Computer Science
Director of Center for Computational Visualization
The University of Texas at Austin, USA
Brief Speaker Bio :

Dr. Chandrajit Bajaj received his Bachelor of Technology degree in Electrical Engineering from IIT Delhi in 1980. He received his Ph.D. degree in Computer Science from Cornell University in 1984.

Dr. Chandrajit Bajaj is presently the Director of the Computational Visualization Center, at the University of Texas at Austin. He is also Professor of Computer Sciences at the University of Texas at Austin since 1997 and he also holds the Computer Applied Math (CAM) Chair of Visualization since 1997.

Dr. Chandrajit Bajaj was a faculty member in the Computer Science department at Purdue University from 1984 and became the Director of Image Analysis and Visualization Center in 1996. In 1997 he was recruited by the University of Texas, Computer Science Department and the then Texas Institute of Computational and Applied Mathematics, to establish a center in computational data analysis and visualization and be its founding director.

Dr. Chandrajit Bajaj is Fellow of the American Association for the Advancement of Science, Fellow of the Institute of Electrical and Electronics Engineers (IEEE), Fellow of the Society of Industrial and Applied Mathematics (SIAM), and also Fellow of the Association of Computing Machinery (also known as ACM), which is the world’s largest education and scientific computing society.

Dr. Chandrajit Bajaj has won the University of Texas Faculty research award, the Dean Research Assignment award, and also thrice won the University of Texas, Institute of Computational Engineering and Sciences, Moncreif Grand Challenge research award. He has also received a Collaborative Teaching Award of the Architecture Department at the University of Texas.

Dr. Chandrajit Bajaj is also committed to being an international bridge and interdisciplinary sciences and engineering collaborator for institutes and universities spanning several countries in Asia and Europe.

Dr. Chandrajit Bajaj’s main pursuit is focused on the algorithmic and computational mathematics underpinnings of Imaging and Geometry Data Sciences, Computer Graphics, Bio-Informatics and Visualization with applications stemming from bio-medical engineering, physical and chemical sciences and bio-inspired architecture. He is committed to the field of computational and predictive medicine. He designs and implements scalable solutions for forward and inverse problems in microscopy, spectroscopy and biomedical imaging; constructing spatially realistic and hierarchical phenomenological models, development of fast high- dimensional search/scoring engines for predicting energetically favourable multi-molecular and cellular complexes; and statistical analysis and interrogative visualization of neuronal form-function.

Risto P Miikkulainen
Professor of Computer Science
The University of Texas at Austin, USA
Associate VP for Evolutionary AI, Cognizant AI Labs
Brief Speaker Bio :

Prof. Risto Miikkulainen is a Professor of Computer Science at the University of Texas at Austin and AVP of Evolutionary Intelligence at Cognizant AI Labs. He received an M.S. in Engineering from the Helsinki University of Technology (now Aalto University) in 1986, and a Ph.D. in Computer Science from UCLA in 1990.

Prof. Risto’s research focuses on biologically-inspired computation such as neural networks and evolutionary computation. On one hand, the goal is to understand biological information processing, and on the other, to develop intelligent artificial systems that learn and adapt by observing and interacting with the environment. The three main focus areas are: (1) Neuroevolution, i.e. evolving complex deep learning architectures and recurrent neural networks for sequential decision tasks such as those in robotics, games, and artificial life; (2) Cognitive Science, i.e. models of natural language processing, memory, and learning that, in particular, shed light on disorders such as schizophrenia and aphasia; and (3) Computational Neuroscience, i.e. development, structure, and function of the visual cortex, episodic memory, and language processing. He is an author of over 450 articles in these research areas.

Simon See
Global Head, NVIDIA
Brief Speaker Bio :

Prof. Simon See is the Global Head, NVIDIA AI Technology Centre, Singapore. He is also a renowned Professor and Chief Scientific Computing Officer in Shanghai Jiao Tong University. Prof. Simon See is also the Adjunct Professor of the Department of Computer Science & Engineering, Mahindra University. He is currently involved in a number of smart city projects, especially in Singapore and China. His research interests are in the area of High Performance Computing, Big Data, Artificial Intelligence, machine learning, computational science, Applied Mathematics and simulation methodology. Prof. See is also leading some of the AI initiatives in Asia Pacific.

He has published over 200 papers in these areas and has won numerous awards in the field. Prof. See is also a member of SIAM, IEEE and IET. He is also a committee member of more than 50 conferences.

Prof. See graduated from University of Salford (UK) with a PhD in electrical engineering and numerical analysis in 1993. Prior to joining NVIDIA, Dr. See worked for SGI, DSO National Laboratory of Singapore, IBM, International Simulation Ltd (UK), Sun Microsystems and Oracle. He is also providing consultancy to a number of national research and supercomputing centres.

Chin‐Teng Lin
Distinguished Professor of Computer Science
Co-Director, Australian Artificial Intelligence Institute (AAII)
Director, Computational Intelligence and Brain Computer Interface Lab
University of Technology Sydney, Australia
Brief Speaker Bio :

Chin‐Teng Lin received the B.S. degree from the National Chiao-Tung University (NCTU), Taiwan in 1986, and the Master and Ph.D. degree in electrical engineering from Purdue University, West Lafayette, Indiana, U.S.A. in 1989 and 1992, respectively. He is currently a Distinguished Professor, Co-Director of Australian AI Institute, and Director of CIBCI Lab, FEIT, UTS. He is also invited as the International Faculty of the University of California at San Diego (UCSD) from 2012 and Honorary Professorship of University of Nottingham from 2014.

Prof. Lin’s research focuses on machine-intelligent systems and brain computer interface, including algorithm development and system design. He has published over 390 journal papers (H-Index 78 based on Google Scholar), and is the co-author of Neural Fuzzy Systems (Prentice-Hall) and author of Neural Fuzzy Control Systems with Structure and Parameter Learning (World Scientific). Dr. Lin served as Editorin-Chief of IEEE Transactions on Fuzzy Systems from 2011 to 2016, and has served on the Board of Governors of IEEE Circuits and Systems Society, IEEE Systems, Man, and Cybernetics Society, and IEEE Computational Intelligence Society. Dr. Lin is an IEEE Fellow, and received the IEEE Fuzzy Pioneer Award in 2017

Title: AI‐Human Teaming via Brain Computer Interfacing

Summary: Human Machine Autonomous Systems (HMAS) are increasingly gaining attention. This is because future human-centric intelligent systems, such as autonomous vehicles will be able to make better decisions and perform tasks more accurately by exploiting both humans and machines. Employing machine agents to assist human operations in time-critical and mission-critical applications such as industry, manufacturing, agriculture, transportation and health is important and efficient. Nevertheless, reliable operations and interventions by humans are required to improve overall system performance. The physical and cultural evolution of humans and the corresponding development of computing technologies have resulted in the respective ability of humans and machines to subjectively and objectively judge situations and make decisions in dynamic and uncertain environments. Because of the different characteristics of humans and machines, there is a need for a general approach to analyse and fuse information from these distinct agents to assist HMAS in making collaborative decisions under various uncertainties and levels of mutual trust between human and machine agents. This talk will address an intelligent engine to adaptively fuse multiple trusts-based information from various agents in HMAS and introduce a general framework to facilitate human-machine interaction and enable better collaborative decisions in HMAS. A key component of this framework is the brain computer interface (BCI). BCI is widely considered a ‘disruptive technology’ for the next-generation humanmachine interface. BCI can help the machine to understand the cognitive states of the human such as action planning, intention, preference, perception, attention, situational awareness, and decision-making. This talk will also introduce the fundamental physiological changes of human cognitive functions in the interaction with autonomous machines and explain how to combine the bio-findings and AI techniques to develop monitoring and feedback systems to enhance the cooperation of human and autonomy.