Graph Neural Networks

Towards Structured Intelligence with Deep Graph Neural Networks

Guohao Li, Ph.D. Student, Computer Science

Oct 5, 18:00 - 20:00

B5 L5 R5209

graph machine learning Graph Neural Networks

This dissertation discusses approaches to building large-scale and efficient graph machine learning models for learning structured representation with applications to engineering and sciences. This work would present how to make Graph Neural Networks (GNNs) go deep by introducing architectural designs and how to automatically search GNN architectures by novel neural architecture search algorithms.

Yasir Ghunaim

Ph.D. Student, Computer Science

Graph Neural Networks Continual Learning Efficient Machine Learning

Yasir is a Computer Science Ph.D. student in Image and Video Understanding Lab (IVUL) Group at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Bernard Ghanem.

Rihan Huang

M.S. Student, Computer Science

Graph Neural Networks explainable AI framework Reinforcement Learning

Juyi Lin

M.S. Student, Computer Science

Graph Neural Networks

Juyi Lin is a computer science MS/Ph.D. student supervised by Prof. Marco Canini. He obtained an EE bachelor’s degree from Zhejiang University, Hangzhou, China 2022. And now he is working on Graph Neural Network training accelerating. Juyi is from China, Zhejiang Province, Wenzhou city. He is a fan of rowing and Ultimate Frisbee and enjoys communicating with people from all over the world.