Sensitivity Analysis for Quantiles of Hidden Biases in Matched Observational Studies

advised by Xinran Li, University of Illinois at Urbana-Champaign (Ongoing)

  • Generalize the conventional sensitivity analysis to deal with general quantiles of the hidden biases from all matched sets.
  • Provide a R package for our method.

Simple and Fast Algorithms for Rank Estimation of Right-censored Length-biased Data

advised by Gary Chan, University of Washington (June 2018 – Sept. 2018)

  • Implemented a fast algorithm in R to solve an over-identified set of non-smooth and non-monotone log-rank estimating equations based on the left-truncated weight-censored data and backward recurrence time.

Double/De-biased Machine Learning for Treatment and Structural Parameters

advised by Jing Tao, University of Washington (June 2018 – Sept. 2018)

  • Implemented the de-biased machine learning method that solves semi-parametric problems of inference on a low-dimensional parameter in the presence of high-dimensional nuisance parameters.

Re-constructing Z-score Calculator with Generalized Additive Model with Shape Constrain

advised by Yen-chi Chen and Gary Chan, University of Washington (June 2018 – Sept. 2018)

  • Constructed new Z-score calculators that use the expected test score of individuals obtained by several different generalized additive models.
  • Compared the binary classification results on real data to test the performance of the new calculators.

Recommendation System on Traditional Chinese Medicine Big Data 

advised by Xiaohua Zhou, Peking University (June 2017 – June 2018)

  • Implemented the recommendation systems through neighborhood-based and model-based collaborative filtering, and deep learning based techniques such as item embedding, feedforward networks, auto-encoders.
  • Constructed hybrid recommendation system based on DeepFM and neighborhood-based model and tested on real data.