Eva Murphy
Postdoctoral Fellow
Wake Forest University
Hi there!
I’m a Postdoctoral Fellow in the Statistics department at Wake Forest University. My mentor is Dr. Staci Hepler. I received my Ph.D. at Clemson University under the advisement of Dr. Whitney Huang. My research interests include spatio-temporal modeling, extreme value theory, and data fusion, focusing on environmental data. Specifically, I work on developing statistical methods that decompose complex structures into smaller parts that are easier to estimate, ensuring accurate complex system modeling. I am also interested in effectively exploiting circularity to analyze and interpret data exhibiting circular behavior accurately. These methods have practical applications in areas such as health science, climate change studies, and the creation of stochastic weather generators.
Interests
- Environmental science
- Spatio-Temporal statistics
- Machine Learning
- Data fusion
Education
-
PhD in Mathematical and Statistical Sciences, 2023
Clemson University
-
Master's in Mathematical Sciences, 2016
University of West Florida
-
Bachelor's in Mathematical Sciences, 2006
Babes-Bolyai University
Current Research
Integrating data at multiple spatial scales to estimate the local burden of the opioid syndemic
with David Kline, Erin McKnight, Andrea Bonny, William C. Miller, Lance Waller, Staci A. Hepler
-
Integrate spatially misaligned data from counties and ZIP codes to analyze the complex interactions of five opioid-related outcomes.
-
Apply GIS methods to align ZIP codes with ZIP Code Tabulation Areas (ZCTAs) for a more detailed exploration of the opioid epidemic, revealing critical localized impacts.
-
Apply GIS methods to align ZIP codes with ZIP Code Tabulation Areas (ZCTAs) for a more detailed exploration of the opioid epidemic, revealing critical localized impacts.
-
Emphasize the need for both granular and county-level data to avoid misinterpretations, particularly in rural and urban regions.
Understanding the Opioid Syndemic in North Carolina: A Novel Approach to Modeling and Identifying Factors
with David Kline, Kathleen L. Egan, Kathryn E. Lancaste, William C. Miller, Lance Waller, Staci A. Hepler
-
Examine trends and relationships among different outcomes believed to reflect opioid misuse.
-
Employ a Baysian dynamic spatial factor model to capture the interrelated dynamics whithin six different county level outcomes related to opioid misuse in North Carolina.
-
Investigating trends and relationships among illicit opioid overdose deaths, emergency department visits, opioid use disorder treatments, buprenorphine prescriptions, and hepatitis C and HIV cases.
-
Develop a novel technique within a Markov chain Monte Carlo algorithm to overcome challenges in loadings matrix estimation, enhancing model identifiability.
-
Provide a deeper understanding of the opioid epidemic's dynamics across time and space to inform public health strategies.
- Poster presentation /A brief video presentation
Spatio-temporal modeling of wind speed
with Dr. Whitney Huang, Dr. Ting Fung Ma
-
Decompose the complex structure of the spatio-temporal wind speed process into smaller components that can be estimated more easily. Then combine these components to obtain an estimate of the overall process.
-
A smooth space-time function is used to capture the first-order mean structure, taking into account periodicity in time, and a combination of empirical orthogonal functions (EOFs) and a first-order dynamical Gaussian process is employed to characterize the potentially complex second-order covariance structure.
-
A crucial aspect of the proposed model is its utilization of the annual "circularity" concept, which introduces spatio-temporal replicates allowing for a flexible nonstationary space-time modeling.
Modeling Directional Extreme Wind Speeds Using an Extreme Value Regression Approach
with Dr. Whitney Huang, Dr. Brook Russell
-
Utilize methods from extreme value theory, namely the block maxima method and peaks-over-threshold method to investigate the potential enhancement of estimating extreme wind speeds.
-
Block maxima, peaks-over-thresholds, and point process methods, are utilized to model the upper tail of the conditional distribution of the extreme wind speed given wind direction.
-
Simulation studies, analysis of output from climate model simulation, and model comparisons are discussed.
Modeling of wind speed and wind direction through a conditional approach
with Dr. Whitney Huang, Dr. Julie Bessac, Dr. Jiali Wang, Dr. Rao Kotamarthi
-
Develop a directional wind speed distribution using Weibull distribution in such way that that the parameters of the distribution depend on wind direction.
-
Construct the dependence of the parameters of the Weibull distribution on wind direction using harmonic regression via weighted least square.
-
Analyze the changes in wind speed and wind direction from present to future climate scenarios.
- Poster presentation /A brief video presentation
Experience
Postdoctoral Fellow
Wake Forest University
Graduate Research Assistant
Clemson University
- Outstanding Graduate in Research Award
- ENVR Student Paper Award
- Dr. Kenyon Fairey Graduate Fellowship
- Call Me Doctor Dissertation Completion Fellowship