Lecture 1
Topic: Toward an Edu-Metaverse Supporting Immersive Explorations and Collaborative Learning Through Knowledge Graph and VR Techniques
主讲
李青教授，香港理工大学电子计算学系 Chair Professor 兼系主任
主讲人介绍
李青教授主要研究领域为：机器学习、数据挖掘、人工智能，及在 multi-modal data fusion, social media mining, event modeling and detection, and sentiment analysis 等领域的应用。在相关领域已经发表了超过 480 篇论文，主要科研成果发表于 TPAMI、TKDE、ICCV、WWW、KDD、SIGMOD、VLDB、VLDBJ、ICDE、AAAI、IJCAI、ACM MM 等。Google Scholar 引用量达 20,900 次。李青教授还（曾）担任多个国际主流期刊的副主编/领域主编，包括人工智能领域顶级期刊 IEEE Transactions on Artificial Intelligence(TAI)，数据库领域顶级期刊 IEEE TKDE、英特网技术领域顶级期刊 ACM Transactions on Internet Technology (TOIT) 和万维网领域的主流期刊 WWW Journal 等。他同时还是 IEEE Fellow，IEE/IET (UK) Fellow，CCF distinguished member 等。
讲座内容介绍
Metaverse as an education platform aims at bringing students and educators together into an interactive virtual environment that could potentially unleash a much richer educational content medium due to the highly immersive learning experience. To facilitate association, exploration, and engagement in collaborative learning, we combine the structure of KGs and the immersion of virtual reality (VR) in our pilot metaverse prototype, K-Cube VR, which is developed and tested to validate the underlying edu-metaverse theory and framework. Examples will also be provided to illustrate the effectiveness of our Edu-Metaverse approach.
Lecture 2
Topic: Imbalanced Learning
主讲
张晓明教授，香港浸会大学人工智能讲席教授及香港研资局高级研究学者，香港浸会大学深圳研究院院长以及计算和理论科学研究所副所长
主讲人介绍
张晓明(CHEUNG, Yiu-ming)为香港浸会大学（浸大）人工智能讲席教授及香港研资局高级研究学者, 同时担任浸大深圳研究院院长以及计算和理论科学研究所副所长, 是 IEEE Fellow、AAAS Fellow、IET Fellow、英国计算机学会 Fellow 以及教育部长江学者(讲座教授),列入 2019 至 2023 年斯坦福大学所发表的人工智能与图像处理专业领域世界顶尖科学家排名前 1%。张教授现为 IEEE Transactions on Emerging Topics in Computational Intelligence 期刊主编。此外，他也是 IEEE 计算智能学会香港分会始创者及前任主席，曾于 2018-2022 年担任 IEEE 计算机学会智能信息学委员会（TCII）主席。张教授是 IEEE智能计算学会以及计算机学会的 Fellow 评审委员会评委、香港研究资助局优配研究金工程学科评委, 以及国家基金委、深圳科创委项目评审专家。
讲座内容介绍
In many practical problems, the number of data forming difference classes can be quite imbalanced, which could make the performance of the most machine learning methods become deteriorate to a certain degree. In general, the problem of learning from imbalanced data is nontrivial and challenging in the field of data engineering and machine learning, which has attracted growing attentions in recent years. In this talk, we will introduce the imbalanced data learning and its related techniques, as well as its applications.
讲座时间与地点
2024年5月24日 （周五）
10:00 AM - 12:00 PM
SEK104, Simon & Eleanor Kwok Building
联系我们
Enquiry: sds@ln.edu.hk
Registration: https://forms.office.com/r/UhrWm1cnKK
