• Advanced Institute of Engineering and Technology (AVITECH)

  • Seminars

    April 22, 2022: Prof. Chin-Teng Lin (Univ. Tech. Sydney, Australia), Brain-Computer Interface (BCI) The Next-Generation Human-Machine Interface

    Brain-Computer Interface (BCI) enhances the capability of a human brain in communicating and interacting with the environment directly. BCI plays an important role in natural cognition, which is studying the brain and behavior at work. Human cognitive functions such as action planning, intention, preference, perception, attention, situational awareness, and decision- making are omnipresent in our daily life activities. BCI has been considered as the disruptive technology for the next-generation human-computer interface in wearable computers and devices. In addition, there are many potential real-life impacts of BCI technology in both daily life applications for augmenting human performance, and daily care applications for elder/patients healthcare. In this talk, Professor Lin will also introduce the BCI applications in Human-Machine Autonomous Systems (HMAS). HMAS is 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. BCI is a key technology to facilitate human-machine interaction and enable better collaborative decisions in HMAS.

    Speaker: Prof. Chin-Teng Lin, Univ. Tech. Sydney

    Time: 15:30, Friday, April 22, 2022

    Venue: Webinar


    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, the U.S.A. in 1989 and 1992, respectively. He is currently a Distinguished Professor, Co-Director of the 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 the University of Nottingham from 2014. Prof. Lin’s research focuses on machine-intelligent systems and brain- computer interfaces, including algorithm development and system design. He has published over 390 journal papers (H-Index 79 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 Editor-in-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. He is the Chair of the 2022 CIS Awards Committee. Dr. Lin is an IEEE Fellow and received the IEEE Fuzzy Pioneer Award in 2017.


    February 2, 2024: Dr. Khoa D. Doan (Vin Univeristy) Toward Reliable and Practical Machine Learning Applications

    While Machine Learning (ML) has rapidly transformed several domains and applications with incredible successes, there are also important areas where the progress is significantly slower. Specifically, there exists a widened complexity gap between the methods currently investigated in research and those used in practice in these areas. One reason is that many algorithms, despite achieving […]

    February 2, 2024: Prof. Heng Ji (University of Illinois at Urbana-Champaign), Combating with Misinformation and Cancer: A Unified Multimodal AI Approach to Healthy and Happy Life

    A research overview of ongoing research projects, especially focusing on two that are most related to the VinUni-UIUC Smart Health Center: (1) Misinformation Detection and Trustworthy Large Language Models; (2) Joint Natural Language and Molecule Learning for Drug Discovery. Unsurprisingly these two seemingly different research problems can be tackled with a unified approach based on […]

    January 11, 2024: Dr. Le Duc Trong (FIT-UET) Resilient Multimodal Learning for Multimodal Emotion Recognition in the Presence of Incomplete Modalities

    Multimodal Emotion Recognition in Conversation (Multimodal ERC) is a critical area of research for interpreting human communication in diverse applications. Nevertheless, the persistent issue of uncertain missing modalities poses a major hurdle, hampering the development of robust Multimodal ERC models. Existing approaches face limitations in effectively leveraging a fusion of diverse data modalities encompassing audio, […]