• Advanced Institute of Engineering and Technology (AVITECH)

  • Seminars

    May 21, 2019: Prof. Paul B. Jantz (Texas State University, US), Magnetic Resonance Imaging (MRI) Techniques Used in Brain Injury Research

    This seminar will present basic information about how a magnetic resonance imaging (MRI) scanning machine works and how T1-weighted, T2-weighted, FLAIR (fluid attenuated inversion recovery), GRE (gradient recalled echo), and DTI (diffusion tensor imaging) MRI scans are used in brain research. It will also discuss how CT (computed tomography) x-ray scans are combined with MRI scans in brain research.

    Speaker: Prof. Paul B. Jantz, Texas State University

    Time: 15:30, Tuesday, May 21, 2019

    Venue: E3-212, 144 Xuan Thuy, Cau Giay, Hanoi

    speaker

    Prof. Paul B. Jantz is a Fulbright Scholar from Texas State University where he is a full-time faculty member in the school psychology Specialist Degree program. At Texas State University, Dr. Jantz teaches about brain anatomy, the biological basis of behavior, data-based decision-making, and ethics. Dr. Jantz’s research and professional interests include the role of neuroimaging in the assessment of children with traumatic brain injury (TBI), how brain networks are affected by TBI, and how brain networks contribute to violent acts in children and adults. Prof. Dr. Jantz has published a number of practitioner-focused articles and case studies on TBI in peer-reviewed journals and co-authored the book Working with TBI in Schools: Transition, Assessment, and Intervention. Dr. Jantz has acted in the capacity of an expert witness in legal cases involving children who have sustained a TBI. Dr. Jantz is currently a 10-month Fulbright Scholar at Vietnam National University of Hanoi, University of Social Sciences and Humanities, Faculty of Psychology located at 336 Nguyen Trai Str., Thanh Xuan District, Ha Noi, Vietnam.

    SAME CATEGORY

    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, […]