• Advanced Institute of Engineering and Technology (AVITECH)

  • Seminars

    June 12, 2021: Prof. Vo Ba Ngu (Curtin Univ., Australia), Random Set Models for Multi-Object Dynamical Systems

    Multi-object systems are complex dynamical systems in which the number of objects and their states are unknown and vary randomly with time. These systems arise in many application areas, including multi-agents systems, surveillance, computer vision, robotics, machine learning, and so on. The standard vector State Space Model (SSM) fundamental to systems theory is not adequate to address multi-object systems. This seminar presents a multi-object system framework that generalizes the standard SSM via random set theory. We also present an overview of exciting developments in multi-object filtering, system identification, and control solutions, as well as applications such as autonomous cars/drones, computer vision, and large-scale problems.

    Speaker: Prof. Vo Ba Ngu, Curtin Univ.

    Time: 15:30, Saturday, June 12, 2021

    Venue: Webinar; Access code: https://bit.ly/2FkRlae


    Vo Ba Ngu received his Bachelor degrees in Mathematics and Electrical Engineering with first class honors in 1994, and PhD in 1997. He had held various research positions before joining the University of Melbourne in 2000. In 2010, he joined the University of Western Australia as Winthrop Professor. He is currently Professor of Signals and Systems at Curtin University. Prof. Vo received the Australian Research Council’s inaugural Future Fellowship and the 2010 Eureka Prize for Outstanding Science in support of Defence or National Security. He is a Fellow of the IEEE, and is best known as a pioneer in the random set approach to multi-object system. His research interests include signal processing, systems theory and stochastic geometry with emphasis on multiple object tracking, robotics, and computer vision.


    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, […]