Ying Tang
Scholar

Ying Tang

Google Scholar ID: -5bbqWsAAAAJ
University of Electronic Science and Technology of China
Stochastic processStatistical physicsMachine learningQuantitative biology
Citations & Impact
All-time
Citations
4,351
 
H-index
12
 
i10-index
14
 
Publications
20
 
Co-authors
21
list available
Resume (English only)
Academic Achievements
  • Proposed the first neural-network-only approach to solve the chemical master equation
  • Characterized a type of dynamical phase transition in nonequilibrium statistical mechanics
  • Learned noise-induced transitions using multi-scaling reservoir computing
  • Developed a computational framework to quantify dynamical mutual information in intracellular signaling processes
  • Discovered that free energy change via Jarzynski equality is magnetic-field-independent in classical systems but amplifiable by magnetic fields in driven quantum systems
  • Developed a scalable path-integral-based numerical method to compute transition rates for Langevin dynamics, robust to noise intensity beyond the small-noise limit
  • In quantitative biology: identified a molecular circuit controlling necroptosis decisions yielding bimodal death-time distribution; modeled how chemotaxis in nutrient-replete conditions promotes bacterial population expansion