Broadly, my research has focused on understanding nature through numbers. A big focus has been the dynamics of infectious diseases, particularly the transmission component. My dissertation work explored the role of individual variation in animal movement for dispersal of seeds and I would like to focus on the role that these 'extreme' individual movements have in rare transmission events, probably leading to outbreaks. I get excited finding ways to incorporate new methods, or adapt old ones, when faced with data challenges. I look forward to incorporating machine learning and AI in ecology and conservation, and finding ways to overcome lack of data or high uncertainty.
My work integrates tools from biostatistics, decision science, and computational modeling to tackle pressing questions: How can we maximize conservation efforts under limited budgets? What management strategies best mitigate zoonotic spillover? How can we design effective interventions that balance ecological conservation and public health?
I am committed to open science and have promoted reproducibility in our field for a few years now. Most of my work is open and code is clearly annotated and shared (You can check my GitHub website!). Exceptions occur when I am not the owner of the work or it is regulated by government agencies, but I try.
Statistical and mathematical modeling
Over the years I have accumulated statistical and quantitative modeling experience. Starting from the very basics of probability theory to complex hierarchical models, and lately, machine learning algorithms. I consider myself, what I call model-agnostic, selecting the best tool for the question at hand rather than adhering to a specific framework.
For example, for my doctoral dissertation work, I used a range of simulation techniques, bootstrap corrections, and delved into extreme value statistics. Later, during my first postdoc I gained valuable experience with Bayesian hierarchical models, occupancy, mark recapture and the like. Now, during my second postdoc, I have learned to mix mechanistic models with machine learning, and I am focusing on AI applications.
Animal movement
It's just fascinating to me, how and why and where do they go? With new technology, this is even more exciting! But specifically, I would like to know how the odd one out moves and the ecological implications of that movement. This goes back to my interests in long distance dispersal and extreme value theory. Can we tie these extreme displacements to ecologically relevant events? Or pathogen spillover? I started to develop this work during my dissertation work and I am currently analyzing deer movements in Florida to understand how their behavior and movement links to epizootic hemorrhagic disease virus (EHDV).
Metacommunity theory
For a few years now I have been exploring the parallels between metacommunity ecology and disease ecology, to understand pathogen communities and multi-host diseases. I have garnered a lot of experience with metacommunity theory and joint species distribution models, as we published this paper. I'm currently leading a follow up on that work that explores the effects of variance in dispersal across species and tests the ability of JSDMs to pick up on those differences.
Decision Science
I am committed to bridging our research findings with real-world applications, and I think decision science is a powerful tool to accomplish this. Particularly in the absence of data and high uncertainty, decision science frameworks are essential. A good case is emerging wildlife diseases, as we make a point in this paper.
I developed mechanistic models to inform a decision process related to deer management and contributed to building an R package specifically for this process. You can see the paper and the package.
My work with WNS in Montana bats is currently under review, and I can't share details on that until it is passed government approvals and is published. But, stay tuned!
