group

research topics

• inverse problems and Bayesian inference

• risk analysis and rare events simulation

• statistical computing and Monte Carlo methods

• scientific machine learning

• data assimilation

current members

Ph.D students

• Robert Millar (2020-present)

• Jingye Li (2022-present)

alumni

• Hongqiao Wang (Ph.D in Mathematics, SJTU, 2014-2018)
  Current placement: Central South University
  Dissertation: Application of Gaussian Process regression to Uncertainty Quantification
  

• Qingping Zhou (Ph.D in Mathematics, SJTU, 2015-2019)
  Current placement: Central South University
  Dissertation: Bayesian inference and Uncertainty Quantification for Medical Image Reconstruction
  

• Tengchao Yu (Ph.D in Statistics, SJTU, 2016-2021)
  Current placement: Institute of Applied Physics and Computational Mathematics
  Dissertation: On some Monte Carlo sampling methods in uncertainty quantification

• Xin Cai (Ph.D in Statistics, SJTU, 2015-2021)
  Current placement: Shanghai Lixin University of Accounting and Finance
  Dissertation: Supervised dimension reduction and its applications in uncertainty quantification

• Linjie Wen (Ph.D in Statistics, SJTU, 2016-2021)
  Current placement: Peking University
  Dissertation: Filtering algorithms in nonlinear dynamical systems

• Jiangqi Wu (Ph.D in Statistics, SJTU, 2016-2021)
  Current placement: Shanghai Nuclear Power Operation Research Institute
  Dissertation: Sampling methods for sequential Bayesian inference

• Junda Xiong (Ph.D in Mathematics, SJTU, 2016-2021)
  Current placement: Shanghai Luoshu Investments
  Dissertation: Gaussian Process regression for high dimensional uncertainty quantification problems

• Chen Cheng (Ph.D in Statistics, SJTU, 2018-2023)
  Current placement: University of Michigan
  Dissertation: Data-driven methods for crowd dynamics modelling

• Ziqiao Ao (Ph.D in Statistics, UoB, 2019-2024)
  Current placement: Huawei Technologies
  Dissertation: Entropy Estimation and Optimization with Machine Learning and their Applications in Bayesian Experimental Design