汤迎 - 北京师范大学 - 自然科学高等研究院

个人简介

特聘副研究员，上海交通大学物理系博士，加州大学洛杉矶分校定量和计算生物科学研究所博士后。近期主要科研成果有：利用机器学习发展了时间序列动力学互信息的计算框架；发现非平衡系统中一个新的量子效应：磁场不做功，却仍能增大自由能；建立了理解细胞坏死机制、细胞状态转换的数学模型和定量分析方法。研究发表在Nat.Commun.，Rep. Prog. Phys.，Commun.Phys.，Phys.Rev.E，Nature，Nat.Methods，Mol.Sys.Biol.等。学习经历 2018年获上海交通大学物理学博士学位 2016年7月至2018年3月为加州大学圣迭戈分校物理系交换研究生 2013年获上海交通大学致远学院荣誉班，数学与应用数学学士学位获奖情况 Collaboratory fellow at UCLA Outstanding Reviewer Awards for Machine Learning Science and Technology Tang Lixin scholarship at Shanghai Jiao Tong University Outstanding student in Zhiyuan college

研究领域

统计物理、机器学习、随机过程、复杂系统

近期论文

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Y. Tang, A. Adelaja, X. Ye, E. Deeds, R. Wollman, A. Hoffmann. Quantifying information accumulation encoded in the dynamics of biochemical signaling. Nat. Commun., 12(1), 1272 (2021). Y. Tang, A. Hoffmann. Quantifying information of intracellular signaling: progress with machine learning. Rep. Prog. Phys. 85 086602 (2022). Y. Tang, D. Shi, & L. Lü, Optimizing higher-order network topology for synchronization of coupled phase oscillators. Commun.Phys. 5, 96 (2022). Y. Tang. Free energy amplification by magnetic flux for driven quantum systems. Commun. Phys., 4(1), 9 (2021). Y. Tang*, R. Yuan*, G. Wang, X. Zhu, and P. Ao. Potential landscape and stochastic transitions of high dimensional nonlinear dynamics under large noise. Sci. Rep., 7 (1), 15762 (2017). Y. Tang, R. Yuan, and P. Ao. Work relations connecting nonequilibrium steady states without detailed balance. Phys. Rev. E, 91, 042108 (2015). Y. Tang, R. Yuan, and P. Ao. Summing over trajectories of stochastic dynamics with multiplicative noise. J. Chem. Phys. 141, 044125 (2014). M. O. Metzig, Y. Tang, S. Mitchell, B. Taylor, R. Foreman, R. Wollman, A. Hoffmann. An incoherent feedforward loop interprets NFkB/RelA dynamics to determine TNF-induced necroptosis decisions. Mol. Sys. Biol., 16, 9677 (2020). J. Cremer*, T. Honda*, Y. Tang, J. Wong, M. Vergassola, T. Hwa. Chemotaxis as a navigation strategy to boost range expansion. Nature 575 (7784), 658-663 (2019). X. Qiu, Q. Mao, Y. Tang, L. Wang, R. Chawla, H. Pliner, C. Trapnell. Reversed graph embedding resolves complex single-cell developmental trajectories. Nat. Methods, 10.1038 (2017).