Joe Guinness
Assistant Professor

Research interests: I am interested in analyzing data observed on spatial and temporal domains, and the theoretical, methodological, and computational issues that commonly arise in environmental and meteorological applications with large datasets. My research is grounded in the spectral theory of random fields, which often provides concise characterizations of statistical models for spatial-temporal data and leads to computationally efficient methodology.


Krishna Pacifici
Assistant Professor

Research interests: My specific research interests are driven by understanding the effects of environmental stressors and disturbances on ecological populations and communities and by making conservation and management decisions in the face of such uncertainty. I use a wide variety of statistical modeling tools, but focus on hierarchical Bayesian modeling and spatiotemporal modeling to estimate the influence of biotic and abiotic factors on ecological communities.


Brian Reich
Associate Professor

Research interests: My research focuses on developing novel statistical methods to address important environmental problems. My primary applications areas include air pollution, climate and meteorology, ecology, and spatial epidemiology. Some tools I find to be particularly useful in tackling challenging problems in these fields are Bayesian methods, extreme value analysis, high-dimensional techniques, quantile regression, and spatiotemporal modeling.


Amanda Bell
PhD student (expected graduation in 2018)

Research interests: My research topic is estimation of nonstationary spatial models for very large datasets. I am developing statistical and computational tools for approximating Gaussian process likelihoods and score equations for large spatial datasets, with applications to inferring dependence structure in microscale elemental data.


Halley Brantley
PhD student (expected graduation in 2018)

Research interests: I'm interested in Bayesian spatio-temporal modeling and applications to near-source (fence line) and real-time (1 Hz) air quality measurements. My current projects include the use of Bayesian quantile regression to locate fugitive emissions within oil refineries and the investigation of land use and traffic on near-road pollutant concentrations using spatio-temporal models.


Marcela Alfaro Córdoba
PhD student (expected graduation in 2016)

Research interests: I'm interested in developing novel statistical methods to address scientific questions related to the environment. My applications areas include climate, weather forecast and veterinary. My dissertation is about spatialtemporal modeling, variable selection and Bayesian model averaging, but I have also worked in other projects applying non-spatial variable selection methods, functional data analysis and other tools to construct and evaluate forecast models.


Neal Grantham
PhD student (expected graduation in 2017)

Research interests: Hierarchical Bayesian models; Spatial & spatio-temporal statistics; Bayesian machine learning for high-dimensional data; High performance statistical computing; Data visualization


Arnab Hazra
PhD student (expected graduation in 2018)

Research interests: Analysis of large datasets; Imaging; Spatial extremes


Yen-Ning Huang
Post-doc

Research interests: I'm interested in the analysis of spatiotemporal data with applications in environmental sciences. I've worked on marked point processes for modeling of earthquakes and wildfires as well as Bayesian methods for evaluation and calibration of the physically based computer models.


An-Ting Jhuang
PhD student (expected graduation in 2017)

Research interests: I have interest in developing new statistical methods to tackle environmental problems, especially health-related. My research focuses on sparse signal detection in spatial statistics, spatiotemporal downscaler, and exposure assessment. The applications include soil science, air pollution, and birth defects.


Kimberly Kaufeld
Post-doc

Research interests: Spatio-Temporal Modeling; Generalized Linear Models; Environmental Impacts on Health; Ecological Applications in Statistics


Zhou Lan
PhD student (expected graduation in 2019)

Research interests: Zhou Lan is an Ph.D student in the Department of Statistics North Carolina State University. He received his undergraduate degree at Zhejiang University and master degree degree at Georgia Institute of Technology. His primary research interests are in statistical methods for Bayesian methods, imaging statistics, spatial statistics, bioinformatics and statistical genetics.


Alexandra Larsen
PhD student (expected graduation in 2018)

Research interests: My research interests are in Bayesian and causal inference, and spatial statistics with applications to environmental areas such as air pollution. My dissertation focus is on exploring new statistical methods for assessing the impact of wildfires on air quality.


Indranil Sahoo
PhD student (expected graduation in 2018)

Research interests: My research interests include application of Bayesian methods and spatio-temporal modeling of data. My current research involves developing statistical methods to compress large scale multivariate spatio-temporal data which arise as outputs from various global climate models. The main challenges include efficient compression of the data, ensuring fast decompression and quantifying the geostatistical uncertainty associated with compression errors.


Susheela Singh
PhD student (expected graduation in 2018)

Research interests: My research interests are in statistical computing and machine learning. My current projects are developing a framework for predicting pollutant exposure distributions and developing a method to efficiently model occupancy probabilities using microbiome data. I am also fascinated by space and hope to get involved with astrostatistics.


Yuan Tian
PhD student (expected graduation in 2019)

Research interests: I’m interested in spatial data analysis with application in environmental issues. I’m also interested in extreme value analysis, spatiotemporal modeling and large sample data analysis.


Ran (Jennifer) Wei
PhD student (expected graduation in 2017)

Research interests: I am interested in the applications of Bayesian modeling of health effect studies with multiple exposures. I am currently working on applications and theoretical properties of continuous shrinkage priors in nonparametric models for prediction and variable selection.


Colin Peterson, PhD, 2016
Thesis title : Mean-Dependent Spatial Prediction Methods with Applications to Materials Sciences
Current position: Post-doc, US EPA


Samuel Morris, PhD, 2016
Thesis title : Spatial Methods for Modeling Extreme and Rare Events
Current position: Statistician, Google


Yan (Dora) Zhang, PhD, 2016
Thesis title : Bayesian Methods for High-dimensional Data
Current position: Post-doc, Johns Hopkins University


Alfredo Farjat, PhD, 2015
Thesis title : Optimal Seed Deployment under Climate Change using Spatial Models and Prediction of Genetic Merit in Loblolly Pine
Current position: Biostatistician, Duke University


Ryan Parker, PhD, 2015
Thesis title : Efficient Computational Methods for Large Spatial Data
Current position: Senior Research Statistician Developer, JMP; Statistician, Portland Trail Blazers


Earvin Balderama, Post-doc, 2014-2015
Current position: Assistant Professor, Loyola University


Elizabeth Mannshardt, Post-doc, 2012-2015
Current position: Statistician, US EPA


Ander Wilson, PhD, 2014
Thesis title : Advances in Bayesian Methods for High-Dimensional Environmental Data
Current position: Assistant Professor, Colorado State University


Luke Smith, PhD, 2014
Thesis title : Bayesian Quantile Regression in Biostatistical Applications
Current position: Research Scientist, Amazon


Laura Boehm Vock, PhD, 2013
Thesis title : Bridge Models and Variable Selection Methods for Spatial Data
Current position: Assistant Professor, Gustavus Adolphus College