Biography

Dr. Xin-Ya Zhang obtained her B.Sc. and Ph.D. degrees in Physics from Tongji University in 2019 and 2024, respectively. During her Ph.D., she conducted a one-year visiting research at University College London. In January 2025, she has joined the Center for Interdisciplinary Science at Westlake University as a postdoc. Her research interests center on complex systems, artificial intelligence, and computational neuroscience. She has published papers as the first author in top physics journals such as Physical Review Letters and Physical Review Research. She has also co-authored papers presented at top AI conferences, including NeurIPS (Spotlight) and AAAI (Oral). Her research has earned honors such as Editors' Suggestion and Featured in Physics, as well as the Best Student Paper Award at the 20th Chinese Conference on Complex Networks. At present, her research focuses on frontier explorations in the intersection of physics and artificial intelligence.

Research Interests

1. Complex Systems

2. Artificial Intelligence

3. Computational Neuroscience 

Representative Publications

1. X.-Y. Zhang, J. M. Moore, X. Ru, G. Yan. Geometric scaling law in real neuronal networks. Physical Review Letters, 2024, 133 (13): 138401.

2. X.-Y. Zhang, H. Lin, Z. Deng, M. Siegel, E. K. Miller, G. Yan. Decoding region-level visual functions from invasive EEG data. bioRxiv, 2024.04. 02.587853

3. Z. Liu, X. Ru, J. M. Moore, X.-Y. Zhang, G. Yan. Mixup in latent geometry for graph classification. IEEE Transactions on Network Science and Engineering, 2024.

4. X.-Y. Zhang, S. Bobadilla-Suarez, X. Luo, M. Lemonari, S. L. Brincat, M. Siegel, E. K. Miller, B. C. Love. Adaptive stretching of representations across brain regions and deep learning model layers. bioRxiv, 2023.12. 01.569615

5. X. Ru, X.-Y. Zhang, Z. Liu, J. M. Moore, G. Yan. Attentive transfer entropy to exploit transient emergence of coupling effect. Advances in Neural Information Processing Systems (NeurIPS 2023).

6. X. Ru, J. M. Moore, X.-Y. Zhang, Y. Zeng, G. Yan. Inferring patient zero on temporal networks via graph neural networks. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2023), 37 (8): 9632.

7. X.-Y. Zhang, J. Sun, G. Yan. Why temporal networks are more controllable: Link weight variation offers superiority. Physical Review Research, 2021, 3 (3): L032045.