Research

Epigenetic Clocks

A transfer learning framework to refine existing age prediction models and to construct prediction intervals.

CARA

A covariate-adjusted response adaptive (CARA) design for learning quantile treatment effects.

SLA

An online estimation and inference framework for streamed longitudinal data analysis (SLA).

Online MEM

An online two-way updating framework via mixed effects models to account for both within-site correlation and cross-site heterogeneity.

YONO

Detection and prediction of ovulation by incorporating biorhythm information in processing high-frequency basal body temperature measurements via hidden Markov models.

RenewGLM

A new framework of real-time estimation and incremental inference in generalized linear models with cross-sectional data streams.

Publications

Accepted & Published

(2023). A fast solution to the lasso problem with equality constraints. The Journal of Computational and Graphical Statistics (Accepted).

(2023). Statistical Inference for Streamed Longitudinal Data. Biometrika, 110(4), 841-858, selected as a discussion paper.

PDF Code Project

(2022). Real-time Regression Analysis of Streaming Clustered Data With Possible Abnormal Data Batches. Journal of the American Statistical Association (Theory and Methods), 118(543), 2029-2044.

PDF Code DOI

Grants&Awards

R21: Transfer Learning and Uncertainty Quantification in Epigenetic Clocks

R01: Maternal Inflammation in Relation to Offspring Epigenetic Aging and Neurodevelopment

Student Paper Competition Award for Statistical Learning and Data Science (SLDS) 2020

Winning Proposal in the 2020 Joint Shark Tank Retreat

2019 Excellence in Research Honorable Mention

2019 ENAR Distinguished Student Paper Award

Teaching

 
 
 
 
 

Biostatistics Theory I (BIST0613)

Rutgers School of Public Health

Sep 2023 – Dec 2023 Piscataway, New Jersey
This course is an introduction to probability modeling as a basis for statistical inference. It lays a foundation in statistical theory for M.S., M.PH, and Ph.D. students. Multivariate calculus is required.

  • Probability and distributions (sets, random variables, expectation, important inequalities)
  • Multivariate distributions (joint/marginal distributions, transformation, independence)
  • Some special distributions (Binomial, Negative Binomial, Geometric, Poisson, Normal)
 
 
 
 
 

Applied Linear Regression (STAT3200)

University of Iowa

Jan 2023 – May 2023 Iowa City, Iowa
This course focuses on applications and hands-on data analysis with computer software (primarily R). A list of the topics to be covered include:

  • examining and transforming data
  • simple, multiple, and dummy variable regressions
  • one-way ANOVA
  • multicollinearity
  • diagnostics of influential data
  • generalized linear models including logistic regression
  • interpretation and presentation of analysis results
 
 
 
 
 

Introduction to Data Science (STAT1015, new course)

University of Iowa

Aug 2021 – Dec 2021 Iowa City, Iowa
This course is intended for lower-level undergraduate students. The goal is to prepare students with the necessary knowledge and useful skills to tackle real-world data analysis challenges. This course will cover basic statistical concepts and computing skills in the field of data science. A list of the topics to be covered include:

  • R basics (importing data, data types, sorting, and summarizing)
  • data visualization with ggplot2, robust summarises
  • intro to probability, statistical inference, and regression models
  • intro to machine learning (classification, clustering, and prediction)
 
 
 
 
 

Mathematical Statistics II (STAT4101)

University of Iowa

Jan 2021 – May 2023 Iowa City, Iowa
This course is a continuation of STAT4100. It is intended for upper-level undergraduate students in the mathematical sciences as well as for graduate students in all disciplines. The goal is to give students a solid foundation in the theory and methods of statistical inference. The main topics include:

  • point estimation and confidence intervals
  • convergence in distribution and convergence in probability
  • maximum likelihood methods
  • sufficient statistics
  • hypothesis testing
 
 
 
 
 

Mathematical Statistics I (STAT4100)

University of Iowa

Aug 2020 – Dec 2022 Iowa City, Iowa
This is a course in mathematical statistics intended for upper-level undergraduate students in the mathematical sciences as well as for graduate students in all disciplines. The goal is to provide a solid foundation in the theory of random variables and probability distributions.

  • Probability and distributions (sets, random variables, expectation, important inequalities)
  • Multivariate distributions (joint/marginal distributions, transformation, independence)
  • Some special distributions (Binomial, Negative Binomial, Geometric, Poisson, Normal)