Lan Liu

Lan Liu Ph.D. Associate Professor Director of the Consulting Center School of Statistics University of Minnesota at Twin Cites

Education

• Harvard University, Boston, MA, USA, Aug 2015
  

  • Postdoctoral Fellow, Causal Inference Program Biostatistics & Epidemiology

    • Topic: Causal Inference with Instrumental Variable

    • Advisor: Eric Tchetgen Tchetgen, PhD

• University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA, Jul 2013

  • Ph.D., Biostatistics

    • Dissertation Topic: Causal Inference in the Presence of Interference

    • Advisor: Michael G. Hudgens, PhD

• University of Science and Technology of China, Hefei, Anhui, China, Jul 2010

  • B.S., Mathematics

Research Interests

• Causal inference, missing data analysis, clinical trials, doubly robust inference, Bayesian analysis, surrogate outcomes, measurement error, mediation analysis, social network, personalized medicine, unmeasured confounder, statistical consulting.

Honors & Awards

• ICSA Student Paper Award, 2013

  • International Chinese Statistics Association

• ORISE Fellowship, 2012 Summer

  • Food and Drug Administration

• Outstanding Students Scholarship

  • University of Science and Technology of China, 2006-2009

Selected Publication

• L. Liu, M. G. Hudgens, “Large Sample Randomization Inference of Causal Effects in the Presence of Interference”, (2014) Journal of the American Statistical Association (JASA) Theory and Methods Section, 109(505):288-301.

• Y. Yin*, L. Liu † , Z. Geng, P. Luo (2020) “Novel criteria to exclude the surrogate paradox and their optimalities”, Scandinavian Journal of Statistics, 47, 84-103

• W. Li*, Y. Gu, L. Liu † (2020) “Demystifying a Class of Multiple Robust Estimators”, Biometrika, 207, 919-933

• L. Liu, W. Li*, Z. Su, D. R. Cook, L. Vizioli, E. Yacoub, (2021) “The Efficiency Boosting via Envelope Chain for task-invoked fMRI study”, in press at Scandianvian Journal of Statistics

Course taught

• Statistical Machine Learning, Spring 2021, 2022

• Advanced Regression Techniques: linear, nonlinear and nonparametric methods, Fall 2019, 2020

• Introduction to Statistical Learning, Spring 2017–2020

• Applied Statistics, Fall 2016, Spring 2020, 2021

• Statistical Analysis, Spring 2016

• Statistical Consulting, Spring 2022

• Literature Seminar, Fall 2016, Spring 2018

Slides

Introduction to Causal Inference

CV

CV

Contact

224 Church St
Minneapolis 55455, USA
email me
liux3771“at”umn“dot”edu