Daniel Iong

PhD candidate in the Department of Statistics at the University of Michigan, Ann Arbor

My broad research interests are in applying interpretable statistical and machine learning methods to gain insight from scientific data in a wide range of fields. In particular, I am interested in Bayesian models, latent variable models, and computational algorithms to fit these complex models. Read about what I have been recently working on below!

Website still in progress!

Preprint - A Latent Mixture Model for Heterogeneous Causal Mechanisms in Mendelian Randomization

Daniel Iong, Qingyuan Zhao, Yang Chen

Mendelian Randomization (MR) is a popular method in epidemiology and genetics that uses genetic variation as instrumental variables for causal inference. Existing MR methods usually assume most genetic variants are valid instrumental variables that identify a common causal effect. There is a general lack of awareness that this effect homogeneity assumption can be violated when there are multiple causal pathways involved, even if all the instrumental variables are valid. In this article, we introduce a latent mixture model MR-PATH that groups instruments that yield similar causal effect estimates together. We develop a Monte-Carlo EM algorithm to fit this mixture model, derive approximate confidence intervals for uncertainty quantification, and adopt a modified Bayesian Information Criterion (BIC) for model selection. We verify the efficacy of the Monte-Carlo EM algorithm, confidence intervals, and model selection criterion using numerical simulations. We identify potential mechanistic heterogeneity when applying our method to estimate the effect of high-density lipoprotein cholesterol on coronary heart disease and the effect of adiposity on type II diabetes.


PDF Poster

July 2020


R package for investigating heterogeneous causal mechanisms in Mendelian Randomization



As a graduate student at UMich, I have had the privilege to be a graduate student instructor (GSI) for several courses (listed below).

Advanced graduate course in Bayesian statistics.


Advanced undergraduate course in Bayesian data analysis.

Website Student Evaluations


University of Michigan, Ann Arbor

Ph.D in Statistics
Doctoral Advisor: Yang Chen
2017 - 2022 (expected)

University of California, Davis

BS Statistics, BA Economics


Programming Languages
  • R
  • Python
  • C++
  • Rcpp
Data Science Tools
  • Numpy
  • Pandas
  • Scikit-learn
  • Tensorflow
  • ggplot
  • Matplotlib
Computing tools
  • linux
  • bash
  • git
  • VS Code


More to come soon…

Graduate school presentation at UC Davis

In November 2019, I had the wonderful opportunity to return to my alma mater to speak about graduate programs in Statistics at UM and my personal journey to graduate school in Statistics. I also gave advice to prospective graduate students from my former department on how to make yourself competitive for graduate programs in Statistics (what courses to take, how to get involved with research, etc). The slides from the presentation can be found here.


November 8, 2019