Bio


Hi, I'm a second year PhD student in Computational and Mathematical Engineering, Stanford University.

I'm mainly interested in theories and application of learning on data-related problems. Luckily, I have great research experiences with many marvelous researchers on data analysis, financial modeling and mathematical imaging. I'm also excited about discovering new interesting fields!

Honors & Awards


  • Total Fellowship, Total S.A., Institute for Computational and Mathematical Engineering (2018.8)
  • Total Fellowship, Total S.A., Institute for Computational and Mathematical Engineering
  • Excellent Graduate, Peking University (2017.7)
  • National Scholarship, Minister of Education, China (2016.10)
  • Finalist of the 2016 Mathematical Contest in Modeling, Consortium for Mathematics and Its Applications (2016.4)
  • Qualcomm Global Scholars Award, Qualcomm (2015.12)

Education & Certifications


  • B.S., Peking University, Applied Math (2017)

Personal Interests


Data science; Quantitative Finance; Statistical and Machine Learning

All Publications


  • Confidence intervals for policy evaluation in adaptive experiments. Proceedings of the National Academy of Sciences of the United States of America Hadad, V., Hirshberg, D. A., Zhan, R., Wager, S., Athey, S. 2021; 118 (15)

    Abstract

    Adaptive experimental designs can dramatically improve efficiency in randomized trials. But with adaptively collected data, common estimators based on sample means and inverse propensity-weighted means can be biased or heavy-tailed. This poses statistical challenges, in particular when the experimenter would like to test hypotheses about parameters that were not targeted by the data-collection mechanism. In this paper, we present a class of test statistics that can handle these challenges. Our approach is to adaptively reweight the terms of an augmented inverse propensity-weighting estimator to control the contribution of each term to the estimator's variance. This scheme reduces overall variance and yields an asymptotically normal test statistic. We validate the accuracy of the resulting estimates and their CIs in numerical experiments and show that our methods compare favorably to existing alternatives in terms of mean squared error, coverage, and CI size.

    View details for DOI 10.1073/pnas.2014602118

    View details for PubMedID 33876748

  • CT Image Reconstruction by Spatial-Radon Domain Data-Driven Tight Frame Regularization SIAM JOURNAL ON IMAGING SCIENCES Zhan, R., Dong, B. 2016; 9 (3): 1063-1083

    View details for DOI 10.1137/16M105928X

    View details for Web of Science ID 000385277200007