Visit ShanghaiTech University | 中文
HOME> People> Faculty
Prof. Qifeng Liao / 廖奇峰 助理教授、研究员

Tel:  (021) 20685400
Email: liaoqf@@shanghaitech.edu.cn
Office: Room 1A-404B, SIST Building, No.393 Huaxia Middle Road, Pudong Area Shanghai
Website:

RESEARCH INTERESTS

  • Model Order Reduction
  • Uncertainty Quantification
  • Big Data Algorithms
  • Numerical Methods for Partial Differential Equations 
  • Finite Element Methods
  • Domain Decomposition Methods

BIOGRAPHY

I obtained my PhD degree in applied numerical computing from the School of Mathematics of the University of Manchester in December 2010. During January 2011 to June 2012, I was a postdoc at the Department of Computer Science of the University of Maryland, College Park. During July 2012 to February 2015, I was a postdoc at the Department of Aeronautics and Astronautics of Massachusetts Institute of Technology. I joined the faculty of the School of Information Science and Technology at ShanghaiTech University as an assistant professor, PI in March 2015.

SELECTED PUBLICATIONS

1. Liao, Qifeng ; Lin, Guang, Reduced basis ANOVA methods for partial differential equations with high-dimensional random inputs, Volume 317, Pages 148–164, Journal of Computational Physics, 2016.
2. Qifeng Liao and Karen Willcox, A domain decomposition approach for uncertainty analysis, SIAM Journal on Scientific Computing, 37 (2015), pp. A 103–A133.
3. Howard Elman and Qifeng Liao, Reduced basis collocation methods for partial differential equations with random coefficients, SIAM/ASA Journal on Uncertainty Quantification, 1 (2013), pp. 192–217.
4. Qifeng Liao and David Silvester, Implicit solvers using stabilized mixed approximation, International Journal for Numerical Methods in Fluids, 71 (2013), pp. 991–1006.
5. Qifeng Liao and David Silvester, Robust stabilized Stokes approximation methods for highly stretched grids, IMA Journal of Numerical Analysis, 33 (2013), pp. 413–431.
6. Qifeng Liao and David Silvester, A simple yet effective a posteriori estimator for classical mixed approximation of Stokes equations, Applied Numerical Mathematics, 62 (2012), pp. 1242–1256.