Learning on Graphs: From Representation to Minimally Supervised

Explore a comprehensive seminar on graph learning techniques, from representation to minimally supervised approaches, presented by Dr. Nitesh Chawla, a distinguished expert in artificial intelligence and data science. Delve into advanced topics such as representation learning methods for heterogeneous data, graph learning algorithms for temporal and dynamic data, and few-shot learning techniques for limited annotated data scenarios. Gain insights into the application of these concepts in diverse fields including social systems, healthcare, and chemical synthesis. Learn from Dr. Chawla's extensive experience as a professor, researcher, and innovator in the field of data science and artificial intelligence.