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).
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Online MEM

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

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

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

Accepted & Published

(2023). Statistical inference in high-dimensional generalized linear models with streaming data. Electronic Journal of Statistics, 17(2), 3443–3471.

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(2023). Online inference with debiased stochastic gradient descent. Biometrika, asad046.

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(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.

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(2022). Online Two-Way Estimation and Inference via Linear Mixed-Effects Models. Statistics in Medicine, 41(25), 5113-5133.

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(2022). Parallel-and-Stream Accelerator for Computationally Fast Supervised Learning. Computational Statistics and Data Analysis, 177, 107587.

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(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.

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(2021). Multivariate Online Regression Analysis with Heterogeneous Streaming Data. The Canadian Journal of Statistics.

DOI

(2020). Renewable Estimation and Incremental Inference in Generalized Linear Models with Streaming Datasets. Journal of the Royal Statistical Society: Series B, 82, Part 1, 69-97.

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(2019). Detection and Prediction of Ovulation from Body Temperature Measured by An In-Ear Wearable Therometer. IEEE Transactions on Biomedical Engineering.

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(2017). Identification of gene pairs through penalized regression Subject to constraints. BMC Bioinformatics.

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(2013). Novel EDA p.Ile260Ser Mutation Linked to Non-syndromic Hypodontia. Journal of Dental Research.

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Grants&Awards

R21: Transfer Learning and Uncertainty Quantification in Epigenetic Clocks

Principal Investigator, NIH (R21AG083364) Sep 2023

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

Co-Investigator, NIH (R01HD111550), PI: Stephanie Shiau Jun 2023

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

SLDS 2020 Student Paper Competition Committee Mar 2020

Winning Proposal in the 2020 Joint Shark Tank Retreat

Department of Biostatistics and Department of Statistics at UMICH Feb 2020

2019 Excellence in Research Honorable Mention

Department of Biostatistics, School of Public Health Nov 2019

Rackham Predoctoral Fellowship for Academic Year 2019-2020

Rackham Graduate School, University of Michigan Sep 2019

2019 ENAR Distinguished Student Paper Award

The International Biometric Society Eastern North American Region (ENAR) Dec 2018

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)