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Position Title/Affiliation
Associate Professor/Wake Forest University Department of Statistical Sciences
Position Being Sought 2023
EnviBayes Treasurer
Candidate Statement 2023
I am an Associate Professor in the Department of Statistical Sciences at Wake Forest University. My research is in the development of Bayesian spatio-temporal methodology and is motivated by applications in the environmental sciences, ecology, and public health.
My recent work develops Bayesian hierarchical models to jointly model multiple sources of data, sometimes at different spatial and/or temporal scales, while accounting for various dependencies across outcomes, space, and time. I work to develop methods that are a suitable compromise between computational feasibility and scientific intricacy. Within the environmental sciences, I have contributed methodology to occupancy models to improve parameter estimation in spatio-temporal modeling of imperfectly detected binary outcomes, such as detection/non-detection data on species. I am currently working as part of an NSF-funded project to develop scalable and computationally efficient models to predict spatio-temporal ordinal data, such as drought severity levels. Much of my work is funded by the NIH to develop hierarchical models to quantify spatio-temporal trends in the opioid epidemic. And recent work is at the intersection of environmental science and public health and seeks to develop methods to understand the role that climate plays on certain infectious diseases.
It would be an honor to serve the Bayesian community by becoming treasurer of EnviBayes. Should I get elected, I will use my prior experience as treasurer for other organizations to effectively serve in this role. I will work diligently with the other officers to organize events that highlight the utility of Bayesian methodology within the environmental sciences and promote and connect the individuals within our community. I will also advocate for increased opportunities for students, so that the next generation of Bayesian environmental statisticians is well-prepared to answer the challenging statistical questions of tomorrow.
My recent work develops Bayesian hierarchical models to jointly model multiple sources of data, sometimes at different spatial and/or temporal scales, while accounting for various dependencies across outcomes, space, and time. I work to develop methods that are a suitable compromise between computational feasibility and scientific intricacy. Within the environmental sciences, I have contributed methodology to occupancy models to improve parameter estimation in spatio-temporal modeling of imperfectly detected binary outcomes, such as detection/non-detection data on species. I am currently working as part of an NSF-funded project to develop scalable and computationally efficient models to predict spatio-temporal ordinal data, such as drought severity levels. Much of my work is funded by the NIH to develop hierarchical models to quantify spatio-temporal trends in the opioid epidemic. And recent work is at the intersection of environmental science and public health and seeks to develop methods to understand the role that climate plays on certain infectious diseases.
It would be an honor to serve the Bayesian community by becoming treasurer of EnviBayes. Should I get elected, I will use my prior experience as treasurer for other organizations to effectively serve in this role. I will work diligently with the other officers to organize events that highlight the utility of Bayesian methodology within the environmental sciences and promote and connect the individuals within our community. I will also advocate for increased opportunities for students, so that the next generation of Bayesian environmental statisticians is well-prepared to answer the challenging statistical questions of tomorrow.
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