Bora Jin

Bora Jin

Statistical Science PhD Student

Duke University

About Me

I’m a 5th year PhD candidate in the Statistical Science department at Duke University under the supervision of Amy H. Herring and David Dunson. My research interests include Bayesian hierarchical methods and spatial/spatiotemporal modelling, with a focus on environmental applications. I care about computational efficiency, model flexibility, and interpretability. My current projects involve various hazardous substances such as toxic chemicals, air pollutants, and water pollutants.


  • Environmental science
  • Spatial statistics
  • Hierarchical framework
  • Latent variables
  • Bayesian methods


  • PhD in Statistical Science, 2023 (expected)

    Duke University

  • Master's in Statistics, 2017

    Korea University

  • Bachelor's in Statistics, 2015

    Korea University

Current Research


Scalable Gaussian Processes on Physically Constrained Domains

with Amy H. Herring and David B. Dunson

Sep 2020 – Present
  • Motivated by applications in point-referenced geostatistics that have measurements collected and meaningful only within a constrained domain.

  • Develop the Barrier Overlap-Removal Acyclic directed graph GP (BORA-GP), a scalable GP method that incorporates the constrained domain via sparsity-inducing DAGs.

  • Enable characterization of dependence in constrained domains by removing an edge in a DAG if a linear path between two points overlaps physical barriers.

  • Analyze levels of chlorophyll a along the east coast of the United States.


Bag of DAGs: Flexible Nonstationary Modeling of Spatiotemporal Dependence

with Michele Peruzzi and David B. Dunson

Sep 2020 – Present

  • R package bags

  • Simple example demonstration

  • Propose a computationally efficient approach to construct a class of nonstationary spatiotemporal processes using multiple yet simple directed acyclic graphs (DAGs), which leads to computational efficiency, flexibility, and interpretability in point-referenced geostatistical models.

  • Develop Bag of DAGs processes (BAGs) whose nonstationarity is induced via local mixtures of DAGs. Directed edges in DAGs are alternative and competing assumptions on directional correlation patterns in space and time.

  • Analyze spatiotemporal variability of fine particulate matter (PM2.5) in South Korea and California, US, in which a directed edge represents a prevailing wind direction causing some associated covariance in the pollutants.


Bayesian Matrix Completion for Hypothesis Testing

with David Dunson, Julia E. Rager, David Reif, Stephanie M. Engel, Amy H. Herring

Jul 2019 – Present

  • Adapt Bayesian heteroscedastic nonparametric regression to a multiple hypothesis testing framework.

  • Impose a generalized latent factor model to form a non-exchangeable prior for testing.

  • Develop a matrix completion method for a latent matrix.

  • Tackle sparsity of the ToxCast data using hierarchical framework.

  • Enable prediction for non-tested chemical’s activity.

  • Broaden the definition of activity including heteroscedasticity.



Teaching Assistant and Course Organizer

Aug 2022 – Present Duke University
Introduction to Data Science and Statistical Thinking (STA199)


May 2022 – Jun 2022 Duke University



Teaching Assistant

Aug 2021 – Dec 2021 Duke University
Case Studies in the Practice of Statistics (STA440)

Guest Lecture

Apr 2021 – Apr 2021 Harvard University, online
Spatial Statistics (STAT141)

Teaching Assistant

Jan 2021 – May 2021 Duke University, online
Theory and Methods of Statistical Learning and Inference (STA432)

Teaching Assistant

Jan 2019 – May 2019 Duke University
Statistics (STA250)

Teaching Assistant

Sep 2015 – Dec 2015 Korea University
  • Introduction to Probability Theory (STAT201)
  • Topics in Mathematical Statistics (STAT412)



Graduate Mentor for Undergraduate Research

Duke University

May 2021 – May 2021 Online


International Atomic Energy Agency

Jan 2018 – Jul 2018 Vienna, Austria


United Nations Environment

Feb 2017 – Aug 2017 Geneva, Switzerland