Title: Advanced Topics in Graph Representation Learning
Time: 02:00pm
Venue: Online via Zoom (Registration is required - by invitation)
Speaker(s): Dr. Chao Huang, Assistant Professor, Musketeers Foundation Institute of Data Science (HKU-IDS) and Dept. of CS, HKU
Remark(s):
The Department of Computer Science is pleased to announce the following interesting mini lecture series to be given by Dr. Chao Huang in February - April 2022. All CS RPg students and senior undergraduate students are welcome to join.
Title: Advanced Topics in Graph Representation Learning
Speaker: Dr. Chao Huang, Assistant Professor, Musketeers Foundation Institute of Data Science (HKU-IDS) and Department of Computer Science, HKU
Period: February 23 - April 6, 2022 (Wednesday) (total 5 lectures, see detailed schedule below)
Time: 2:00 pm - 3:00 pm
Venue: Online via Zoom (Registration is required - by invitation)
Description:
Graph representation learning research has grown at an incredible pace in data mining and machine learning communities. This lecture series will cover recent core techniques and advances in graph representation research for modeling a variety of real-world applications and problems, including graph representation, heterogeneous graph mining, graph neural networks, recommendation with graphs, graph-based spatial-temporal learning, and others.
About the Speaker:
Dr. Chao Huang, Assistant Professor, Musketeers Foundation Institute of Data Science (HKU-IDS) and Department of Computer Science, HKU. His research focuses on developing novel machine learning frameworks to tackle various challenges in Data Mining, Information Retrieval, Spatial-Temporal Data Analytics, User Behavior Modeling, Recommendation, Graph Mining, and Deep Representation Learning. Prior to that, he received his Ph.D. in Computer Science from the University of Notre Dame in USA.
Schedule and topics:
Lecture 1:
February 23, 2022 (Wed)
Introduction of graph mining;
Core concepts of graph representation learning/network embedding;
Heterogeneous graph analysis
Lecture 2:
March 2, 2022 (Wed)
Graph neural networks (GNN)/GNN-based applications/self-supervised graph learning
Lecture 3:
March 23, 2022 (Wed)
Recommendation with graphs I;
Social and knowledge-aware recommender systems/user personalization
Lecture 4:
March 30, 2022 (Wed)
Recommendation with graphs II;
Recommendation with behavior heterogeneity and diversity
Lecture 5:
April 6, 2022 (Wed)
Graph-based spatial-temporal learning for smart cities
