SONG Zixing, a recent PhD graduate from the Department of Computer Science and Engineering, received the 2024 International Neural Network Society (INNS) Doctoral Dissertation Award for his PhD thesis entitled Towards Trustworthy Graph-based Semi-Supervised Learning: From Graph Construction to Label Inference. Dr. Song is currently a Postdoctoral Research Associate at University of Cambridge. The award ceremony took place during this year’s IJCNN 2025 conference in Rome, Italy.
Each year, the INNS (International Neural Network Society) presents its INNS Doctoral Dissertation Award to the author(s) of the best doctoral dissertation(s) in neural networks, machine learning, and related fields. Awardees are chosen by INNS members in recognition of their high-quality, excellent, and outstanding research work. INNS established the program to recognize and encourage doctoral candidates in computer science and engineering among its members.
Beyond this award, the broader INNS Awards program has a distinguished history of recognizing pioneering scholars whose transformative contributions have shaped the trajectory of biological learning, computational neuroscience, and artificial intelligence. Notably, INNS honored Prof. John Hopfield with the Hermann von Helmholtz Award in 1999; he was later awarded the 2024 Nobel Prize in Physics. Additionally, Prof. Andrew Barto received the Donald O. Hebb Award in 2014 and was subsequently named the 2024 recipient of the Turing Award, often referred to as the “Nobel Prize of Computing.”
Dr. Song pursued his PhD at the Department of Computer Science and Engineering under the supervision of Prof. Irwin King from 2020 to 2024. His PhD thesis explores how semi-supervised machine learning models can be made more reliable and trustworthy when working with graph-structured data. Graph-based semi-supervised learning, a central focus of the thesis, represents data as a graph, enabling labels to propagate across connected nodes. His thesis exemplifies how rigorous theory and practical needs can converge to create machine learning models that are not only effective but also responsible.
Reflecting on the award, Dr. Song described it as a tremendous honour and a meaningful recognition of the dedication invested in the research during his time at the Department of Computer Science and Engineering. He also expressed his deepest appreciation for the tremendous guidance and support from Prof. Irwin King, whose mentorship played a key role in shaping the work and achieving this milestone.
