• Advanced Institute of Engineering and Technology (AVITECH)

  • Seminars

    October 26, 2021: MD.,PhD. Tran Vu Hoang (Univ. Massachusetts Medical School, US), Inflammation and cardiac arrhythmias in ischemic heart disease

    Heart disease contributed to 23 percent of deaths in the United States in 2017. Cardiac arrhythmias (i.e., abnormal heart rhythm) are the major causes of death in heart disease. Cardiac arrhythmias can occur in the general population, but they tend to occur more commonly in patients who suffer from a heart attack. Some arrhythmias, such as ventricular tachycardia or ventricular fibrillation, can be lethal and are the most common causes of sudden cardiac deaths, while other arrhythmias, e.g., atrial fibrillation, can lead to heart failure, stroke, or organ ischemia. Knowledge of the epidemiology of cardiac arrhythmias in terms of incidence, prevalence, associated outcomes, changes over time, and their mechanism is crucial to public health. This knowledge is frequently used to identify patients at risk of adverse outcomes and candidates for medical interventions. This talk links several traditional biomarkers of inflammation, namely leukocytosis and hyperglycemia, as risk factors for cardiac arrhythmias

    Speaker: MD.,PhD. Tran Vu Hoang, Univ. Massachusetts Medical School

    Time: 15:30, Tuesday, October 26, 2021

    Venue: E3-710,144 Xuan Thuy,Cau Giay, Hanoi; Access code: https://bit.ly/3b1YZCC


    Dr. Tran attained his medical education at Hanoi Medical University. Subsequently, he finished a residency in Cardiology at Vietnam National Heart Institute – Bach Mai Hospital and Hanoi Medical University. He worked briefly as a cardiologist at the Department of Cardiology, Hanoi Medical University Hospital, before moving to the United States for more training in research. Dr. Tran obtained a Master of Public Health degree in Epidemiology at the University of Nebraska Medical Center with a graduation thesis focusing on chronic rejection and vasculopathy in patients with heart transplantation. He subsequently obtained his Doctor of Philosophy in Clinical and Population Health Research at the University of Massachusetts Medical School. His research interests are healthcare disparity and cardiac arrhythmias, specifically their epidemiology, association with inflammation, and psychogenic effects. Dr. Tran has published scientific articles and book chapters in top-tier journals in medicine and cardiovascular disease. His research has been widely cited and featured in scientific editorials and media.


    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, […]