Postdoc Research Associate
Postdoc Research Associate

Biography

Peishi Jiang has broad research expertise in data-driven studies in Earth science, particularly in machine learning, information theory/causal inference, frequency analysis, and inverse modeling. 

Research Interest

  • Information theory and causal inference applications to ecosystems
  • Application of machine learning techniques to improve prediction ability and physical understanding in Earth science
  • Frequency analysis of hydrological extremes

Education

  • PhD in Civil Engineering, University of Illinois at Urbana-Champaign, 2019
  • MPhil in Civil Engineering, Hong Kong University of Science and Technology, 2013
  • BE in Water Resources and Ocean Engineering, Zhejiang University, 2011

Publications

2020

  • Jiang P and P Kumar. 2020. “Bundled Causal History Interaction.” Entropy, 22 (3), 360, doi: 10.3390/e22030360.
  • Goodwell A, P Jiang, B Ruddell, and P Kumar. 2020. “Debates—Does information theory provide a new paradigm for Earth science? Causality, interaction, and feedback.” Water Resources Research, 56 (2), doi: 10.1029/2019WR024940.

2019

  • Jiang P and P Kumar. 2019. “Using Information Flow for Whole System Understanding from Component Dynamics.” Water Resources Research, 55, 8305– 8329, doi: 10.1029/2019WR025820. 
  • Jiang P and P Kumar. 2019. “Information Transfer from Causal History in Complex System Dynamics.” Physical Review E, 99(1), 012306, doi: 10.1103/PhysRevE.99.012306. 

2018

  • Jiang P and P Kumar. 2018. “Interactions of Information Transfer Along Separable Causal Paths.” Physical Review E, 97(4), 042310, doi: 10.1103/PhysRevE.97.042310. 

2017

  • Jiang P, M Elag, P Kumar, SD Peckham, L Marini, and L Rui. 2017. “A Service-oriented Architecture for Coupling Web Service Models Using the Basic Model Interface (BMI).” Environmental Modelling and Software, 92, 107-118, doi: 10.1029/2019WR025820. 

2015

  • Jiang P and YK Tung. 2015. “Incorporating daily rainfalls to derive rainfall DDF relationships at ungauged sites in Hong Kong and quantifying their uncertainty.” Stochastic environmental research and risk assessment, 29, 45-62, doi: 10.1007/s00477-014-0915-2. 

2013

  • Jiang P and YK Tung. 2013. “Establishing Rainfall Depth-Duration-Frequency Relationships at Daily Raingauge Stations in Hong Kong.” Journal of Hydrology 504, 80-93, doi: 10.1016/j.jhydrol.2013.09.037.