Hi, there.

I am a Postdoctoral Fellow at the Johns Hopkins Whiting School of Engineering working with Dr. Paul Ferraro. From 2019 to 2021, I was a Harvard Data Science Initiative Postdoctoral Fellow at the Harvard TH Chan School of Public Health. I earned a PhD in Biology and MS in Statistics from Auburn University.

I focus on the development and application of robust statistical and causal methods to estimate the consequences of environmental change, evaluate the role of interventions in modifying long-term human and animal behavior, and determining the efficacy of such behavioral changes to persisting outcomes for ecological, human, and planetary health.

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An overview of my research

Robust estimation for non- and semi-parametric models

Plots of coefficient functions of a single-index varying coefficients regression model

Coefficient functions of a single-index varying coefficients regression model estimated by RSGLASSO under CN(0.95) error distribution.

Spatiotemporal effects of climate change on population dynamics

Pacific halibut swimming near the bottom of the ocean; Eurasian reindeer grazing in snow in Tromsø, Norway

Modeling predator-prey dynamics of adult groundfish (e.g. Pacific halibut, left) in the Gulf of Alaska. Spatial and temporal effects of plant productivity and herd condition on juvenile body mass of Eurasian reindeer (right) in Norway.

Exploring multiple paternity across clades

Estimated frequency of multiple paternity across litter sizes ranging from 2 to 10 for mammals

Bayesian MCMC estimated frequency of multiple paternity across litter sizes for mammalian species (red points). The solid red line is the predicted frequency of multiple paternity using a zero-truncated binomial distribution.

Causal inference in ecology

Plot of maximum correlation of cross-mapped versus observed values as a function of time series length

Maximum correlation of cross-mapped versus observed values as a function of time series length for comparisons between environment, halibut, and cod (processes A, as columns) and sablfish CPUE (process B). Significant forcing of A on B is represented as red horizontal bars, while significant forcing of B on A are blue vertical bars. Non-significant relationships are indicated as dark grey horizontal bars (A→B) and vertical bars (B→A). Spearman’s rank correlation coefficient is represented as open circles. Empty plots indicate that there were insufficient observations from a complete time series for the multispatial CCM algorithm to begin. Plots with only Spearman’s indicate that the predictive ability of one or both processes did not significantly decrease with increasing time distance, so CCM was not performed.