Graph Neural Networks

Efficient Machine Learning for Scientific and Medical Applications

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B4/5 L0 A0215

This dissertation addresses key challenges of machine learning in scientific and medical domains by developing methods that improve model efficiency, data efficiency, and learning under real-world constraints.
KAUST-CEMSE-CS-PhD-Dissertation-Defense-Xiaochuan-Gou-Explainability-and-Efficiency-in-Spatio-Temporal-Models

Explainability and Efficiency in Spatio-Temporal Models: Applications to Traffic Forecasting

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This dissertation addresses key challenges in deep learning-based traffic forecasting, including computational efficiency, model interpretability, and data limitations, despite recent progress in spatio-temporal modeling techniques.

Theoretical Foundations of Efficient and Scalable Graph Learning

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Auditorium between Building 2 - 3, L0 R0215

This talk addresses key challenges in graph neural networks (GNNs), including expressive power and model depth, by theoretically analyzing and providing solutions in three areas: universal approximation of linear program properties, strong expressive power of subgraph GNNs on graphs with bounded cycles, and the role of residual connections in mitigating oversmoothing and enabling deeper models.

Elham Binshaflout

Towards Structured Intelligence with Deep Graph Neural Networks

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B5 L5 R5209

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

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

Juyi Lin

M.S. Student, Computer Science
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.