Shujian Yu (余书剑)

I am Shujian Yu, an assistant professor of machine learning in the Department of Artificial Intelligence at the Vrije Universiteit Amsterdam. I also hold a guest position in the Machine Learning Group at the UiT - The Arctic University of Norway. I graduated from the University of Florida in the Department of Electrical and Computer Engineering, working with Prof. Jose C. Principe, with a Ph.D. minor in theoretical statistics. Previously, I obtained my BEng degree from the School of Electronic Information and Communications at the Huazhong University of Science and Technology. I was a research scientist of machine learning at the NEC Laboratories Europe from 2019 to 2021.

I am doing research on the intersections between machine learning, information theory and signal processing, which include topics like information-theoretic learning and machine learning for signal processing. Particularly, I am interested in information-theoretic quantities (such as entropy, mutual information, divergence, etc.) estimation, improving the explainability and generalization of deep neural networks by information-theoretic principles (such as the information bottleneck and the principle of relevant information). I am also interested in brain data analysis (such as EEG and fMRI).

I am the recipient of the 2020 International Neural Networks Society Aharon Katzir Young Investigator Award. I am also selected for the 2023 AAAI New Faculty Highlights.

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• 26-04-2024: One paper on Cauchy-Schwarz divergence for domain adaptation is accepted by International Conference on Uncertainty in Artificial Intelligence (UAI).
• 27-03-2024: One paper on prototype learning for interpretable brain network-based psychiatric diagnosis and subtyping is accepted by Neuroimage, link, code.
• 23-01-2024: One paper on Granger causality for interpretable brain network-based psychiatric diagnosis is accepted by Neural Networks, link, code.
• 16-01-2024: Two papers on “Information Bottleneck”, “Cauchy-Schwarz divergence”, and “generalization error bound” are accepted by International Conference on Learning Representations (ICLR), link1, link2.
• 15-09-2023: One paper on “Information Bottleneck meets Invariant Risk Minimization” for fine-grained image classification is accepted by Computer Vision and Image Understanding, link.
• 15-07-2023: One paper on “minimum error entropy” for transfer learning is accepted by ECAI-23, link.
• 13-06-2023: The slides of our "Information Theory meets Deep Learning" tutorial in ICASSP-23 are available at link.
• 17-03-2023: One paper accepted by the IEEE Transactions on Information Theory.
• 27-01-2023: One paper accepted by the IEEE Transactions on Knowledge and Data Engineering.
• 27-12-2022: One paper accepted by the IEEE Transactions on Signal Processing.
• 15-12-2022: We will deliver a tutorial entitled “Information Theory meets Deep Learning” in ICASSP-23. See you in Rhodes Island, Greece!
• 22-11-2022: I am honored to be selected for the 2023 AAAI New Faculty Highlights.
• 19-11-2022: Two papers accepted by AAAI-23.

Great template from Jon Barron