Once you eliminate the impossible, whatever remains, no matter how improbable, must be the truth.

                                                                                                                                                        —— Arthur Conan Doyle 

Research Interests

My research in statistics is motivated by quantitative problems in clinical trials. This includes causal inference, semiparametric theories, high-dimensional and machine learning methods. 

Heterogeneous treatment effect estimation

• Estimation of heterogeneous treatment effect in both random controlled trial and observational data setup. 

• Methodologies involving propensity scores, including IPW, TMLE, PS matching, etc. 

• Subgroup analysis.

Estimand Framework

• Treatment switching.

• Methods for handling intercurrent events.

Causal structural learning

• Methodologies for estimating CPDAGs from observational data.

•  Multiple testing approaches in V-structure detections.

Presentation

1. Speed Session, Joint Statistical Meeting, July 2016, Chicago, IL.

2. Topic Contributed Session, ENAR Spring conference, March 2017, Washington D.C.

3. Topic Contributed Session & Best Student Paper Invited Talk, ENAR Spring conference, March 2018, Atlanta, GA.

4. Poster, Department of Medicine 2018 Research Day,  June 2018, Philadelphia, PA.

5. Topic Contributed Session, Joint Statistical Meeting, July 2018, Vancouver, CA.

6. Invited Session, ENAR Spring conference, March 2019, Philadelphia, PA.

7. Invited Session, ICSA 2019 Applied Statistics Symposium, June 2019, Raleigh, NC.

8. Topic Contributed Session, Joint Statistical Meeting, July 2019, Denver, CO.

9. Poster (student travel award winner), iBRIGHT 2019, Nov. 2019, Houston, TX.

10. Topic Contributed Session, ENAR Spring conference, March 2022, Houston, TX.

11. Invited Session, ICSA 2022 Applied Statistics Symposium, June 2022, Gainesville, FL.

12. Invited Session, ICSA China Conference, July 2022, Xi'an, China.