Ying Tang (汤迎)

Professor in Physics

Introduction

Our research topics include stochastic process, machine learning, nonequilibrium statistical physics, information theory and quantitative biology. Combining analytical and numerical approaches, the ultimate goal is to uncover simple theoretical principles that could help understand complex nonequilibrium dynamical processes.

Check our recent publications in Google Scholar. To discuss any interesting science, feel free to contact us at jamestang23@gmail.com

About PI: Since 2024, I am a Professor at Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu. From 2021 to 2024, I was an Associate Researcher at International Academic Center of Complex Systems, Beijing Normal University, Zhuhai. From 2018 to 2021, I was a Postdoc fellow at Signaling Systems Laboratory in University of California, Los Angeles, supported by Prof. Alexander Hoffmann. I obtained my Ph.D. from Department of Physics, Shanghai Jiao Tong University in 2018, mentored by Prof. Ping Ao. During 2016 to 2018, I was an exchange graduate student in Department of Physics, University of California, San Diego, where I got quantitative training from Prof. Terence Hwa. I completed my B.S. in honored class of Zhiyuan College, Shanghai Jiao Tong University in 2013.

Interests

Stochastic process
Machine learning
Nonequilibrium statistical physics
Information theory

Education

Ph.D. in Physics, 2018
Shanghai Jiao Tong University
B.S. in Applied Mathematics, 2013
Shanghai Jiao Tong University

Research

Machine learning stochastic dynamics:

Machine learning and stochastic dynamics have deep connections and cross-feed each other. We recenetly have developed machine-learning approaches to investigate the time evolution of stochastic dynamics: (1) propose the first approach of using the neural network alone to solve the chemical master equation; (2) characterize a type of dynamical phase transition in nonequilibrium statistical mechanics; (3) learning noise-induced transitions by multi-scaling reservoir computing.

Dynamical information theory:

Inferring mutual information from time series data remained challenging as the possible trajectory configurations increases exponentially with the number of time points. We develop a computational framework to quantify the dynamical mutual information of intracellular signaling process, and summarize the recent progresses in this review on quantifying information by machine learning.

Nonequilibrium and quantum thermodynamics:

As a remarkable advance in nonequilibrium thermodynamics during the last 20 years, Jarzynski equality connects free energy changes to nonequilibrium work fluctuations. We found that the free energy change through the Jarzynski equality is independent of magnetic field in the classical regime, but can be amplified by magnetic field in driven quantum system. The magnetic field can also be generated by the coupling to the heat bath.

Stochastic process without detailed balance:

Stochastic transitions are ubiquitous in nature. Based on path integral approach, we develop a scalable numerical approach to calculate transition rates for a class of Langevin dynamics. The computational cost is robust to varying noise intensity, beyond small noise limit. The efficient computations on transition rates enable a broader use of stochastic modeling in complex dynamics, such as cell state transitions.

Quantitative biology:

To understand the complex behaviors in biological systems across scales, we have attempted to construct minimum models to describe the data and make predictions: (a) we identified computationally a molecular circuit that control necroptosis decisions such that a bimodal death-time distribution can be produced; (b) we demonstrated that chemotaxis in nutrient-replete conditions promotes the expansion of bacterial populations by modeling the physiological effect on bacterial chemotaxis.