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Our People

  /  Our People

Director and Distinguished Professor

Chin-Teng Lin

Prof. Chin-Teng Lin was invited as the founding director of CI&BCI Centre in February 2016. The research team in the CI&BCI Centre is growing.

Director, Computational Intelligence and Brain Computer Interface Centre (CIBCI), FEIT, UTS

Co-Director, Centre for AI (CAI), FEIT, UTS

Dr. Chin-Teng Lin has received the B.S. degree from National Chiao-Tung University (NCTU), Taiwan in 1986, and the Master and Ph.D. degree in electrical engineering from Purdue University, USA in 1989 and 1992, respectively. He is currently Distinguished Professor of Faculty of Engineering and Information Technology, and Co-Director of Center for Artificial Intelligence, University of Technology Sydney (UTS), Australia

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Dr. Chin-Teng Lin has been also invited as Honorary Chair Professor of Electrical and Computer Engineering, NCTU, International Faculty of University of California at San-Diego (UCSD), and Honorary Professorship of University of Nottingham. Dr. Chin-Teng Lin was elevated to be an IEEE Fellow for his contributions to biologically inspired information systems in 2005, and was elevated International Fuzzy Systems Association (IFSA) Fellow in 2012. Dr. Chin-Teng Lin received the IEEE Fuzzy Systems Pioneer Awards in 2017. Dr. Chin-Teng Lin served as the Editor-in-chief of IEEE Transactions on Fuzzy Systems from 2011 to 2016. Dr. Chin-Teng Lin also served on the Board of Governors at IEEE Circuits and Systems (CAS) Society in 2005-2008, IEEE Systems, Man, Cybernetics (SMC) Society in 2003-2005, IEEE Computational Intelligence Society in 2008-2010, and Chair of IEEE Taipei Section in 2009-2010. Dr. Chin-Teng Lin was the Distinguished Lecturer of IEEE CAS Society from 2003 to 2005 and CIS Society from 2015-2017. Dr. Chin-Teng Lin serves as the Chair of IEEE CIS Distinguished Lecturer Program Committee in 2018~2019. Dr. Chin-Teng Lin served as the Deputy Editor-in-Chief of IEEE Transactions on Circuits and Systems-II in 2006-2008. Dr. Chin-Teng Lin was the Program Chair of IEEE International Conference on Systems, Man, and Cybernetics in 2005 and General Chair of 2011 IEEE International Conference on Fuzzy Systems. Dr. Chin-Teng Lin is the co author of Neural Fuzzy Systems (Prentice-Hall), and the author of Neural Fuzzy Control Systems with Structure and Parameter Learning (World Scientific). Dr. Chin-Teng Lin has published more than 300 journal papers (Total Citation: 20,163, H-index: 65, i10-index: 254) in the areas of neural networks, fuzzy systems, brain computer interface, multimedia information processing, and cognitive neuro-engineering, including about 120 IEEE journal papers.


Fellow, Institute of Electrical and Electronic Engineers (IEEE) (2005)

Fellow, International Fuzzy Systems Association (IFSA) (2013)

IEEE Fuzzy Systems Pioneer Awards, IEEE Computational Intelligence Society (2017)

Outstanding Achievement Award by Asia Pacific Neural Network Assembly (APNNA) (2013)

Outstanding Electrical and Computer Engineer (OECE) by School of Electrical and Computer Engineer of Purdue University (2011)

IEEE Distinguished Lecture Speaker (2003–2005, 2015–2017)

Editor-in-Chief, IEEE Transactions on Fuzzy Systems (2011–2016)

Founding Editor-in-Chief, Journal of Neuroscience and Neuroengineering, American Scientific Publishers (2012–)

Deputy Editor-in-Chief, IEEE Trans. on Circuits and Systems II (2006–07)

Chairman, IEEE Taipei Section (2009-2010) (received the IEEE Member and Geographic Activities Board Outstanding Large Section Award)

President, Asia Pacific Neural Network Assembly (APNNA) (2004-2005)

General Chair, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), Taipei, Taiwan, 2011.

Program co-chairs, 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015), Istanbul, Turkey, 2015.

Panel Sessions chair, 2016 The IEEE World Congress on Computational Intelligence (IEEE WCCI 2016), Vancouver, Canada, 2016.

Panel Sessions Chair2014 IEEE World Congress on Computational Intelligence (WCCI 2014), Beijing, China, 2014.

Invited Session Chair2012 IEEE World Congress on Computational Intelligence (IEEE-WCCI 2012), Brisbane, Australia, 2012.

Merit NSC Research Fellow Award, National Science Council (NSC), Taiwan, 2009.

Outstanding Research Award, National Science Council (NSC), Taiwan, 1996 – Present.

The 38th Ten Outstanding Rising Stars in Taiwan granted by International Junior Chamber – Taiwan Chapter, 2000.

Taiwan Outstanding Information-Technology Expert Award, 2002.

Outstanding Electrical Engineering Professor Award, the Chinese Institute of Electrical Engineering (CIEE), 1997.

Outstanding Engineering Professor Award, the Chinese Institute of Engineering (CIE), 2000.

Positions and Employment

Dr. Chin-Teng Lin is currently Distinguished Professor of University Technology Sydney (UTS), Australia. Prior to joining UTS on 1 Feb. 2016, Dr. Chin-Teng Lin was a fulltime academic staff member at National Chiao-Tung University (NCTU), Taiwan where he had been actively involved in research and both postgraduate and undergraduate teaching since 1992. Over the past 27 years, Dr. Chin-Teng Lin had excellent research opportunities in the following roles:

– Professor, Department of Electrical Engineering, NCTU, Taiwan (1992-2015)

– Director, Office of Research and Development, NCTU, Taiwan (1998-2000)

– Chairman, Department of Electrical and Control Engineering, NCTU, Taiwan (2000-2003)

– Deputy Dean, College of EECS, NCTU, Taiwan (2003-2005)

– Dean, College of Computer Science, NCTU, Taiwan (2005-2007)

– Provost, NCTU, Taiwan (2007-2014)

– Director, Brain Research Center, NCTU, Taiwan (2003-2015)

– Lifetime Chair Professor, NCTU, Taiwan (2010-)

– International Faculty, UCSD, US (2012-2015)

– Honorary Professorship of University of Nottingham, England, UK (2014-)

– Fellow, Institute of Electrical and Electronic Engineers (IEEE) (2005-)

– Fellow, International Fuzzy Systems Association (2012-)

– Editor-in- Chief, IEEE Trans. on Fuzzy Systems (2011-2016)

– Distinguished Professor, UTS, Australia (2016-)

– Director, Computational Intelligence & Brain-Computer Interface Centre (CI-BCI), UTS, Australia (2016-)

– Co-Director, Centre for AI, UTS, Australia (2017-)

Research Interests

Dr. Chin-Teng Lin current research interests include the following domains:
Intelligent Technology, Computational Intelligence, Fuzzy Neural Networks (FNN), Cognitive Neuro-engineering, Brain-computer Interface, Multimedia Information Processing, Machine Learning, Robotics, and Intelligent Sensing and Control.

Important Academic Achievements:
Publication: (Total Citation: ~20,324, H-index: 65, i10-index: 258 by Google Scholar)
Journal papers: over 300 (including over 120 IEEE Transactions/magazine papers)
Conference papers: over 260
Patents: 108
Dr. Chin-Teng Lin devoted himself to the research of Computational Intelligence (CI) since his infant stage of early 1990’s. Dr. Chin-Teng Lin took the lead in developing the models of Fuzzy Neural Networks (FNN), which have now become one of the most prevailing research areas in CI. Since 1996, the Intelligent Information Technology (iIT) has been his major research interest.  In 2003, Dr. Chin-Teng Lin founded the Brain Research Center (BRC) at the National Chao-Tung University (NCTU) and served as it’s director to this day. By applying the CI and iIT to the research of cognition neuroscience, Dr. Chin-Teng Lin had focused mainly on the area of Translational Neuroscience since 2003, trying to bring basic findings in neuroscience into daily life applications. To achieve this goal, He had focused on 2 major problems: Natural Cognition and Brain-Computer Interface (BCI). Natural cognition studies the brain and its behavior at work. One major research problem is the study of brain dynamics of alertness, drowsiness, motional sickness, distraction, orientation, etc. during driving. For real-world applications of BCI, they had designed wearable and wireless devices for measuring brain waves (EEG signals) with dry sensors and developed algorithms for brain waves analysis. The Brain Research Center is now a platform for conducting various interdisciplinary research activities in cognition neuroscience and engineering.

Research Support

Dr. Chin-Teng Lin had served as the Chief Investigator (CI) of about 290 research projects funded by government and industries between 1992 and 2019. The total amount of research funding is approximately US$42,765K (AUD$53,469K). In particular, He served as the CI of several international large-scale research projects on Intelligent Systems, Natural Cognition, and Brain-Computer Interface, spanning several countries and industries.

(in UTS)

  1. Discovery Projects (3 projects), funded by Australia Research Council, 2015-2017; 2018-2020, amount: AU$ 721,266. These projects are developing (1) A personalised navigation system to provide effective augmented-reality (AR)-based support information, built on different navigation preference and the momentary cognitive workload of the user, to significantly enhance spatial learning and alleviate the apparent decline in navigational ability experienced across the life span; (2) an intelligent engine to adaptively fuse multiple trusts-based information from various agents in human machine autonomous systems (HMAS) to benefit human-centric automation systems in general and next-generation autonomous vehicles in particular.
  2. CI, “Cognition and Neuroergonomics Collaborative Technology Alliance Program,” US Army Research Lab / DCS Corp., June 1, 2016 to May 31, 2020, AU$ 763,995 (US$552,500), including the following projects:
    1. “Online Mutually Adaptive & Closed-Loop Overall Performance Management System,”  funded by US Army Research Lab (US$140,000)
    2. “The Analysis and Interpretation of Real-Life EEG and non-EEG Data Collected in Two Unprecedented Longitudinal Studies” funded by US Army Research Lab (US$187,500)
    3. “Adaptive Robust Closed-Loop Real-Time BCI Systems with Minimum Calibration,” funded by US Army Research Lab (US$135,000)
    4. “Brain-State Drift Detection and Management for Uncertainty Estimation and Human Performance Modeling,” funded by US Army Research Lab (US$90,000)
  3. Co-CI, “Concept Drift Detection and Reaction for Data-driven Decision Support Systems,” Australian Research Council Discovery Project, Jan. 1, 2015 to Dec. 31, 2017, AU$320,000
  4. CI, “Cognitive Intelligent Information Processing and Presentation in Navigation,” Australian Research Council Discovery Project, Jan. 1, 2018 to Dec. 31, 2020, AU$366,663
  5. CI, “A Neural Fuzzy Fusion Engine for Human-Machine Autonomous Systems,” Australian Research Council Discovery Project, Jan. 1, 2018 to Dec. 31, 2020, AU$354,592
  6. CI, “Deep Learning for EEG-based Fatigue Assessment,” Brain Rhythm Inc., Oct. 1, 2016 to Sep. 31, 2019, AU$700,000
  7. CI, “Deep Learning for Human Action Analysis,” V5 Technology Ltd., Feb. 1, 2017 to Jan. 31, 2018, AU$206,000
  8. CI, “Development of Self-Learning Algorithms for Decision Support Tool for Smart Aviation Maintenance,” 1ANSAH Pty Ltd., Aug. 1, 2017 to Jan. 31, 2018, AU$95,847
  9. CI, “Image Analysis of Fire Trails with P300,” Commonwealth Bank of Australia, Jan. 1, 2018 to May. 31, 2018, AU$26,500
  10. CI, “How Fatigue Changes Autonomy Use,” Lockheed Martin Corporation (US), Jan. 1, 2018 to Dec. 31, 2020, US$260,000 (AUD$ 371,428)
  11. Co-CI, “HealthSense: Technological Human Health Sensors Capable of Monitoring, and Providing Feedback and Intervention to the User,” Defence Innovation Network (NSW), April 1, 2018 to Sep. 30, 2018, AU$125,163 (CI: Sara Lal)
  12. CI, “Intelligent Multi-Agent Coordination and Learning,” The Defence Science and Technology Group (DSTG, Australia Department of Defence), Aug. 1, 2018 to July. 31, 2021, AU$650,000
  13. CI, “Hierarchical Multi-agent Navigation,” The Defence Science and Technology Group (DSTG, Australia Department of Defence), Aug. 1, 2018 to July. 31, 2021, AU$60,000
  14. CI, “Adversarial Autonomous Cyber Operation based on Hierarchical Multi-Agent Deep Reinforcement Learning,” The Defence Science and Technology Group (DSTG, Australia Department of Defence), March. 1, 2019 to June 31, 2019, AU$46,952
  15. CI, “Covert State Discovery and Multi-Agent Reinforcement Learning for Human-Autonomy Teaming,” US Office of Navy Research Global, US, April 1, 2019 to March 31, 2022, AU$580,687
  16. Co-CI, “A Biometrically Enabled Training Solution for the Measurement of Cognitive Overload and Threat Perception in Air Traffic Controllers,” Defence Innovation Network (NSW), July 1, 2019 to June 30, 2020, AU$240,000 (CI: Eugene Nalivaiko)

(3 major international projects in Taiwan)

  1. Cognition and Neuroergonomics (CAN) Collaborative Technology Alliance (CTA), funded by US Army Research Lab, 2010 – 2016, amount: US$ 3.5 million (my share). This project consists of three research topics: Neurocognition, Neurocomputation, and Neurotechnology. I am the CI for the topic of Neurotechnology, studying the Effects of Vehicle Motion and Cognitive Fatigue, and Wearable EEG Devices Development and Testing. This is the first time that the US ARL has supported a foreign organization on large scale, and it is one of the major projects in Neuroscience and Engineering Research in the US.
  2. International Center of Excellence for Advanced Bioengineering Research, funded by Taiwan NSC (National Science Council), 2011 – 2015, amount: US$ 6.65 million. This Center conducts interdisciplinary research in Neural Engineering, Translation Neuroscience, and Healthcare. The goals are to enhance our understanding of human perception and cognition, to create and apply innovative technology to advance neuroscience research, and to develop novel methods and strategies for improving the prevention, diagnosis, and treatment of neurological diseases and injuries. In this project, I focus on two research topics: Computational Neuroscience and Bio-sensing Technology.
  3. Development of Smart Living Technologies, funded by Taiwan NSC and Farglory Construction Company, 2008 – 2015, amount: US$ 8.2 million. This project has developed smart living technologies and service models, including i-Home (smart home technology), i-Service (smart services platform), i-Sensing (environment monitoring), i-Care (home care), i-Traffic (intelligent traffic systems), and i-Light (smart energy management), to meet users’ needs and real-life experiences through testing in living labs. The project was awarded the LLG Award 2011 with the citation “Innovation and Excellence for smart Urban Technologies.” The LLG (Living Labs Global) is an NGO started in 2003, connecting 52 countries, 127 cities, and more than 160 organizations/companies, as a platform for service innovation (more than 500 showcases).

Teaching Areas

Machine Learning, Logic Design, Fuzzy Systems, Neural Networks, Computational Intelligence, Cognitive Neuroscience

Post-Doctoral Fellow

Avinash K Singh

Degree:  B.Sc., M.S., MCA, Ph.D.

Positions:  Postdoctoral Research Fellow, School of Computer Science

Dr. Avinash K Singh is working as an Australian Research Council Postdoctoral Research Fellow (PDF) at Australian Artificial Intelligence Institute in University of Technology Sydney (UTS), Australia. Dr. Avinash K Singh has completed Ph.D. in Computer Science in May 2019 from University of Technology Sydney (UTS), Australia. Before his doctorate, he received his M.S. degree in Software Systems in 2013 from Birla Institute of Technology and Science, Pilani, India, and B.Sc. degree in Mathematics in 2007 from Chhatrapati Shahu Ji Maharaj University, India.

Kha Vo

Degree: Postdoc

Research topic: Neural Message-Passing For Large-Scale Multi-Agent Reinforcement Learning

Research description:

We propose a graph-based learning architecture for Multi-Agent Reinforcement Learning (MARL) scenarios. The most distinguishing characteristic of this model is its capability to be deployed in completely decentralized scenarios with thousands of agents. By selecting appropriate information to attend to in each time step, we form sub-graphs of connectivity to ease the training burden while preserving important information from the perspective of each individual agent. Empirical results indicate that our approach outperforms state-of-the-art algorithms in typical collaborative and competitive MARL scenarios, in terms of training loss as well as training speed.

Ye Shi

Position:  Ph.D., Postdoc Fellow

Research topic:   Distributed Machine Learning Paradigms for big data

Research description:

This research focuses on developing distributed machine learning algorithms for big data, that is associated with inherent issues, such as massive scales, heterogeneity, uncertainty, and privacy concerns. Supervised, unsupervised, semi-supervised and weakly-supervised learning frameworks are respectively investigated to handle different scenarios in the big data environment

PhD Students

Jie Yang

Degree: Ph.D. Student

Research topic: Advanced Clustering Algorithms and their Applications on EEG Data Processing

Research description:

For this project, he try to develop a brand new clustering algorithm that can identify various clusters with different characteristics regardless of their shape, density, or size. More importantly, it does not require any prior knowledge or parameter settings. Then, he will use the algorithm to process and analyze more complex data, such as EEG signals.

Alka John

Position: Ph.D. Student

Research topic: Real-time Mental Workload Adaptive System

Research description:

Alka Johan work on developing an integrated hardware-software solution using wearable technology for the real-time measurement of cognitive load. The system will adapt its behavior real-time based on the workload experienced by the human operator measured by the physiological signals such brain dynamics, eye activity and the heart rate variability

Daniel Leong

Position: Doctor of Philosophy

Research topic: Object Recognition using electroencephalogram (EEG) signal

Research description:

The aim of the research is to investigate the change in EEG dynamic when a person recognize an object and decode this EEG information for a BCI application. The task of object recognition can be divided into two parts: 1. when people notice an object, they will analyse the features of the object and associate them with our memory, thus recognize the object. 2. Once objects are recognized, people will make a decision in mind and select the object of their choice.

Jia Liu

Position: Ph.D., University of Technology Sydney

Research topic: Investigating the impact of Landmarks on Spatial Learning during Active Navigation.

Research description:

Landmarks play a significant role in spatial navigation but their effects on spatial learning and the accompanying brain dynamics in actively moving participants have not been explored yet. This study proposes a systematic approach to compare the brain dynamics during active physical navigation addressing spatial knowledge acquisition based on allocentric and egocentric reference frames using local and global landmarks.

Khuong Hoang Tran

Position:  Ph.D. (Computer Science)

Research topic: Deep Hierarchical Reinforcement Learning in solving complex problems.

Research description:

Investigate the use of Deep HRL to improve scalability of Deep RL algorithms in real world scenarios, especially in large action space, highly stochastic and sparse rewards. The research focuses on automatically factor the action space, discover subgoals to guide the agents in explore and exploit the environments

Leijie Zhang

Position:  Ph.D., Casual Research Assistant

Research topic:   Fuzzy Neural Network .

Research description:

Fuzzy Neural Network (FNN) model is a powerful and effective solution for dealing with uncertainty, which involves fuzzy rules and fuzzy inference systems, to make optimal use of the given data. FNNs deal with data uncertainty through ‘fuzzification’ operations and architectures based on if-then rules.
Few high-resolution pictures of your research with description

Yuan Zhu

Position: Doctor of Philosophy, Computer Systems

Research topic: A Closed-Loop Brain-Computer Interface System for the Detection of Acute Stress and stress-based intervention on Autonomous System.

Research description:

In the past decade, society has seen the integration of various autonomous robotic systems such as manufacturing lines, autonomous trains, self-driving cars, and the usage of drone for deliveries (Amazon Prime Air) and security. His research investigates the possibility of using biosignals (ECG, EEG, GRS, etc.) to provide these robotic systems with information on the emotional state of the surrounding or interacting humans and using this information to alter or intervene in its autonomous behaviour.

Liang Ou

Position:  Ph.D. Student

Research topic:  Multi-Agent Reinforcement Learning (MARL)

Research description:

Liang Ou enrolled at UTS in Spring session 2020 after graduate from his masters’ degree (Master of IT in Data Analytics) at University of Technology Sydney (UTS) Australia. He is now focusing on Multi-Agent Reinforcement Learning (MARL) using explainable AI (xAI) techniques under the supervision of Dr. Chin-Teng  Lin.

Sai Kalyan Ranga Singanamalla

Position:  Ph.D. in Computer Science

Research topic:  Spiking Neural Networks for Brain Computer Interface

Research description:

EEG signals being an accumulation of spiking neural activity, abstracts the dynamics and thereby losing spatial granularity. My research surrounds demystifying the basis and origins of EEG signal by modelling it via Spiking Neural Networks (SNN) towards the advancement of BCI applications from control to rehabilitation studies.

Sami Fahad Karali

Position:  Arabic Rumor News Detection System Using Machine Learning

Research topic:  Spiking Neural Networks for Brain Computer Interface

Research description:

His thesis topic focuses on Arabic rumor news that spread on Twitter. Now more than ever, social media has turned into news’ source for most people especially for breaking-news situations. However, social media has a lack of oversight of services. Therefore, unverified and unsubstantiated information need to be detected and verify in social media.

Xiaofei Wang

Position:  Ph.D.

Research topic:  ErrP-based Brain-Robot-Interaction

Research description:

EEG-based Brain-Computer Interface (BCI) is an emerging option for Human-Robot Interaction (HRI) because it is a natural form that allows hands-free interaction. His research focus on the application of EEG-measured Error-Related Potentials (ErrP) to closed-loop mobile robots

Tien-Thong Nguyen Do

Position:  Research Associate

Research topic:  Brain-Computer Interfaces, Machine Learning, Spatial Navigation, Mobile Brain/Body Imaging (MoBI)

Research description:

He is interested in exploring naturalistic Brain-Computer Interface (BCI) Technology, which is interdisciplinary research involving  Mobile Brain/Body Imaging (MoBI), Data Science, Machine Learning, and VR/AR.

Yanqiu Tian

Position:  Ph.D. Student

Research topic:  Closed-loop Multi-modality System for Inattentional Blindness Prediction and Reduction under Stress

Research description:

Yanqiu Tian Ph.D. research focuses on exploring the critical phenomenon/effect known as Inattentional Blindness (IB) which individuals fail to notice salient and unexpected objects when their attention is occupied elsewhere via building a closed-loop multi-modality system to predict and reduce IB through establishing Physiological and Neurological models, and Bio/Neuro-feedback under stress.

Yurui Ming

Position:  Ph.D., Casual Research Assistant

Research topic:   Deep (Reinforcement) Learning and Its Application to EEG Data Analysis

Research description:

His research is by drawing inspirations from the principles of brain science, to enhance and to generalize deep neural networks for analyzing EEG data that captured during different BCI experiments. His current research focuses on memory network, to address the challenges of limited EEG sample data with high variance.

Jimmy Cao

Position:  Collaborator | Lecturer, Discipline of Information & Communication Technology, University of Tasmania, Australia

Sunny Chiu

Position:  Past Student

Yurui Ming

Position:  Past Student

Fred Chang

Position:  Past Student

Visiting Master Students

Yu-Chia Hung

Position: Past Visiting Student

Yu-Feng Cheng

Position: Past Visiting Student


Dr. YK Wang

Position: Lecturer School of Computer Science

Dr. YK Wang received the B.S. degree in Mathematics Education from National Taichung University of Education, Taichung, Taiwan, in 2006, and the M.S. degree in Biomedical Engineering from National Chiao Tung University, Hsinchu Taiwan, in 2009. Dr. YK Wang received PhD degree from the Department of Computer Science and Engineering, National Chiao Tung University, Hsinchu, Taiwan, in 2015.

Dr. Mukesh Prasad

Position: Senior Lecturer School of Computer Science

Dr. Mukesh Prasad received the M.S. degree from the School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India, in 2009. After receiving the M.S. degree, Dr. Mukesh Prasad worked as a software engineer for a year, New Delhi, India. Afterward, he pursued his Doctoral degree with the Department of Computer Science, National Chiao Tung University (NCTU) and was awarded my Ph.D. degree in 2015.