Bora Jin

Bora Jin

Statistical Science PhD Student

Duke University

About Me

I’m a 4th 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
  • Multivariate methods
  • Hierarchical framework
  • Latent variables
  • Bayesian methods


  • PhD in Statistical Science, 2022 (expected)

    Duke University

  • Master's in Statistics, 2017

    Korea University

  • Bachelor's in Statistics, 2015

    Korea University

Current Research


Bag of DAGs: Flexible & Scalable Modelling of Spatiotemporal Dependence

with Michele Peruzzi, James E. Johndrow, and David B. Dunson

Sep 2020 – Present
  • Propose a computationally efficient approach to construct a well-defined spatial Gaussian process (GP) with the nonstationary covariance 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-Gaussian process (BDAG-GP), each DAG of which is chosen to represent a different possible dependence structure, to induce nonstationarity.

  • Analyze spatiotemporal variability of fine particulate matter (PM2.5) in California in which a DAG represents a prevailing wind direction causing some associated covariance in the pollutants.


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.


Bayesian Matrix Completion for Hypothesis Testing

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

Jul 2019 – Present
  • submitted

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

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

  • Developed a matrix completion method for a latent matrix.

  • Tackled sparsity of the ToxCast data using hierarchical framework.

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

  • Broadened the definition of activity including heteroscedasticity.



Teaching Assistant

Duke University

Aug 2021 – Present Durham, NC, USA
Case Studies in the Practice of Statistics (STA440)

Graduate Mentor

Duke University

May 2021 – May 2021 Online
Undergraduate Research in Statistical Science Workshop

Guest Lecture

Harvard University

Apr 2021 – Apr 2021 Online
Spatial Statistics (STAT141)

Head Teaching Assistant

Duke University

Jan 2021 – May 2021 Online
Theory and Methods of Statistical Learning and Inference (STA432)

Teaching Assistant

Duke University

Jan 2019 – May 2019 Durham, NC, USA
Statistics (STA250)


International Atomic Energy Agency

Jan 2018 – Jul 2018 Vienna, Austria


United Nations Environment

Feb 2017 – Aug 2017 Geneva, Switzerland