个人简介

张昕亚博士于2019年和2024年分别获得同济大学物理学学士和博士学位，博士期间在英国伦敦大学学院（University College London）进行为期一年的访问研究，2025年1月加入西湖大学交叉科学中心开展博士后研究。她的研究范围包括复杂系统和人工智能，以第一作者身份在国际顶级期刊 Nature Communications、Physical Review Letters、Communications Physics、Physical Review Research 等发表论文，并在 NeurIPS（Spotlight）、AAAI（Oral）等国际顶级AI会议上发表合作论文。她的研究成果获得学界高度认可，入选PRL年度精选“PRL Collection of the Year 2024”（由国内单位完成的9篇入选论文之一）。目前，她的研究重点是物理学与人工智能交叉领域的前沿探索。

代表论文 (*通讯作者）

1. 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. Nature Communications, in press.

2. X.-Y. Zhang, X. Ru, Z. Liu, J. M. Moore, G. Yan. Coarse-graining complex networks by optimizing mutual information estimates of random walks. Journal of Physics: Complexity, in press.

3. X.-Y. Zhang*, Y. Yao, Z. Han, G. Yan. Delayed threshold and spatial diffusion in k-core percolation induced by long-range connectivity. Communications Physics, 2025.

4. X.-Y. Zhang*, G. Yan, J. M. Moore*. Organization of a neuron-resolution central brain network: topology, geometry, and anatomy. Journal of Complex Networks, 2025.

5. X.-Y. Zhang, J. M. Moore, X. Ru, G. Yan. Geometric scaling law in real neuronal networks. Physical Review Letters, 2024 (Editors' Suggestion & Featured in Physics).

6. 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.