Hi, there.

I am 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.

My research interests broadly cover the area of statistical ecology, particularly developing new techniques to quantify climate change effects on populations and identifying causation in ecological data. I also aim to improve statistical methods for high-dimensional and spatiotemporal data.


An overview of my research: statistical ecology


Mathematical models
Herd of elephants on a grassy plain in Amboseli, Kenya
Population dynamics of Amboseli elephants (shown) adjusted for effects of rainfall on birth rates.
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.
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.
Spatiotemporal effects of climate change on terrestrial population dynamics
Eurasian reindeer racing down a snowy street at an event in Tromsø, Norway
Spatial and temporal effects of plant productivity and herd condition on juvenile body mass of Eurasian reindeer (shown during Sami racing in Tromsø) in Norway.
Spatiotemporal effects of climate change on deepwater marine population dynamics
Pacific halibut swimming near the bottom of the ocean
Modeling predator-prey dynamics of adult groundfish in the Gulf of Alaska. Shown is the Pacific halibut, an apex predator in the northeastern Pacific Ocean.
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.