Giáo sư Heng Ji, Đại học UIUC (Mỹ) đang có chuyến thăm một số trường đại học lớn ở Việt Nam. Thứ tư tuần nay, GS sẽ trình bày một Seminar Khoa học cho sinh viên và quý thầy cô quan tâm. 
Thông tin cụ thể buổi Seminar như sau:
  • Thời gian: 14h30 đến 16h00 chiều thứ 4 ngày 31/01/2024.
  • Địa điểm: Phòng E202B, Trường ĐH Khoa học tự nhiên, cơ sở 1, 227 Nguyễn Văn Cừ, Q5, TP. HCM.

Buổi trình bày được phát trực tiếp và trực tuyến với các chủ đề bên dưới:

  1. Topic 1: Combating with Misinformation and Cancer: A Unified Multimodal AI Approach to Healthy and Happy Life Untitled Title
    • Abstract: In this talk, I will give a research overview of our 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 multimodal knowledge representation and extraction and consistency checking. I’m aiming to recruit several new PhD students to be co-advised by Prof. Khoa D Doan 
    • Bio: Heng Ji is a professor at the Computer Science Department and an affiliated faculty member at the Electrical and Computer Engineering Department and Coordinated Science Laboratory of the University of Illinois Urbana-Champaign. She is an Amazon Scholar. She is the Founding Director of the Amazon-Illinois Center on AI for Interactive Conversational Experiences (AICE). She received her B.A. and M.A. in Computational Linguistics from Tsinghua University and her M.S. and Ph.D. in Computer Science from New York University. Her research interests focus on Natural Language Processing, especially on Multimedia Multilingual Information Extraction, Knowledge-enhanced Large Language Models, AI for Science, Knowledge-driven Generation, and Conversational AI.
  1. Topic 2: Toward Reliable and Practical Machine Learning Applications
    • Abstract: While machine learning (ML) has rapidly transformed several domains and applications with incredible success, 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 state-of-the-art performance in “controlled” research environments, usually ignore important efficiency and practical constraints of real-world problems. In this talk, I will discuss the research effort to bridge the gap between ML research and practice with examples in various ML domains. Finally, I will discuss various projects, including Trustworthy/Federated ML, Causal Inference, Low-resource NLP, and CV for Multi-modal Environmental Intelligence, and PhD/Research Assistant opportunities (co-advised by Prof. Heng Ji and others at UIUC). 
    • Bio: Khoa D. Doan is currently an Assistant Professor of Computer Science in the College of Engineering and Computer Science and Environment Monitoring Lab Director at the Center for Environment Intelligence at VinUniversity. Previously, he worked as an AI Researcher at Baidu Research, USA. He received his Ph.D. in computer science at Virginia Tech and his MS in computer science at the University of Maryland, College Park. He has extensive experience working as a software engineer, data engineer/scientist, and researcher in various industries, from scientific centers such as NASA/UMD and advertising companies such as Criteo/Baidu to ML and data analytic startups. 

Các bạn sinh viên, học viên cao học, nghiên cứu sinh và các đồng nghiệp quan tâm có thể quét mã QR bên dưới để đăng ký tham dự.