• Advanced Institute of Engineering and Technology (AVITECH)

  • Seminars

    September 03, 2019: Mr. Le Trung Thanh (AVITECH), EEG Source Localization in the Context of Epilepsy: A New Multiway Temporal-Spatial-Spectral Analysis

    Epilepsy is one of the most common neurological disease, approximately 65 million people diagnosed with epilepsy in the world. For the removal of epileptogenic region in particular and epilepsy diagnosis in general, accurate localization of the epileptic focus is highly meaningful. In this talk, we would like to introduce a robust method for EEG source localization based on a new multiway temporal spatial-spectral (TSS) analysis via spectral graph theory and tensorial blind source separation. In particular, instead of using the temporal behavior of sources (i.e., time variable), we apply the graph wavelet transform (GWT), which is one of the most powerful vertex-frequency tools for graph signal processing (GSP), to the space variable in order to exploit information of the spatial domain. Numerical experiments on both simulated and real data are carried out to evaluate the model performance of the TSS analysis in comparison to space-timefrequency (STF), space-time-wave-vector (STWV) analysis.

    Speaker: Mr. Le Trung Thanh, AVITECH

    Time: 15:30, Tuesday, September 03, 2019

    Venue: G2-315, 144 Xuan Thuy, Cau Giay, Hanoi


    Le Trung Thanh received B.Sc. and M.Sc. degrees in Electronics and Telecommunications from the VNU University of Engineering and Technology (VNU-UET), a member of Vietnam National University, Hanoi (VNU) in 2016 and 2018 respectively. He is now pursuing his Ph.D. study at the University of Orleans, France. His research interests include signal processing, subspace tracking, tensor analysis, and system identification.


    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, […]