Shujian Yu (余书剑)

I am an assistant professor 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.

My research interests lie primarily in the intersection of 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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• 25-09-2024: One paper on backdoor detection is accepted by NeurIPS-24, link, code.
• 20-08-2024: One paper on Information Bottleneck for interpretable brain disorder diagnosis is accepted by IEEE Transactions on Neural Networks and Learning Systems (TNNLS), link, code.
• 02-05-2024: One paper on neural network-based Granger causality is accepted by International Conference on Machine Learning (ICML), link, code.
• 26-04-2024: One paper on Cauchy-Schwarz divergence for domain adaptation is accepted by International Conference on Uncertainty in Artificial Intelligence (UAI), link, code.
• 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