Research
The lab builds mathematical, statistical, and computational tools to answer questions in ecology, evolution, and public health. Most projects are collaborations with experimental, clinical, and public health partners.
Microbiome dynamics and health
Microbial communities change through interactions among their members and with their hosts. We develop and evaluate models of these dynamics, including generalized Lotka–Volterra models, their identifiability and resilience, and network-based methods for high-throughput sequencing data. We apply them to systems ranging from the human vaginal microbiome to aging marmosets and dairy calves.
Selected papers
- Modeling time-series data from microbial communities.
- Structural identifiability of the generalized Lotka–Volterra model for microbiome studies.
- Resilience of a stochastic generalized Lotka–Volterra model for microbiome studies.
- Filtering ASVs/OTUs via mutual information-based microbiome network analysis.
- Developing the common marmoset as a translational geroscience model to study the microbiome and healthy aging.
Infectious disease epidemiology and public health
We use mathematical and statistical models to forecast outbreaks, quantify risk factors, and evaluate public health strategies. Recent work includes forecasting COVID-19 in rural communities from wastewater surveillance, modeling sociodemographic risk factors for COVID-19, and studying how trust and risk perception shape health behavior. Earlier work at the CDC focused on influenza vaccine effectiveness and the burden of disease.
Selected papers
- Epidemiological model can forecast COVID-19 outbreaks from wastewater-based surveillance in rural communities.
- Predictive spatial modeling of sociodemographic risk for COVID-19 mortality.
- Applications of elastic net regression for modeling COVID-19 sociodemographic risk factors.
- Effects of trust, risk perception, and health behavior on COVID-19 disease burden: Evidence from a multi-state US survey.
- Unraveling R0: Considerations for public health applications.
Plasmids and antibiotic resistance
Plasmids carry antibiotic resistance genes between bacteria. With Eva Top, Thibault Stalder, and colleagues, we study how plasmids persist in biofilms and how to detect the hosts of resistance plasmids in complex communities.
Selected papers
- Detection of rare plasmid hosts using a targeted Hi-C approach.
- Precision of Hi-C-based metagenome-assembled genome reconstruction across binning pipelines.
- Biofilms preserve the transmissibility of a multi-drug resistance plasmid.
- Persistence of antibiotic resistance plasmids in bacterial biofilms.
Evolutionary ecology and coevolution
My training is in evolutionary ecology, including the coevolution of garter snakes and their toxic newt prey and quantitative-genetic theory of coevolving species. These foundations continue to shape how we model interacting populations.
Selected papers
- Identification of selective sources: partitioning selection based on interactions.
- A quantitative genetic approach to predicting change in biological communities.
- Stability of equilibria in quantitative genetic models based on modified-gradient systems.
Funding
| Years | Sponsor | Project |
|---|---|---|
| 2025–2028 | Department of Defense / Uniformed Services University | The long-term effect of HIV on medical readiness, duty-limiting medical conditions, and service retention |
| 2025–2027 | Department of Energy (EPSCoR) | Revealing the causes and consequences of microbial non-genetic variability in energy-relevant processes |
| 2020–2025 | National Institutes of Health | Microbiome-mediated therapies for aging and healthspan in marmosets |
| 2020–2024 | NIH COBRE (Center for Modeling Complex Interactions) | Center for Modeling Complex Interactions, including a COVID-19 modeling supplement |
| 2018–2023 | USDA NIFA | Tracking the spread of antibiotic resistance genes and plasmids in agricultural soils |
Earlier support came from NIH, NSF (including BEACON and a Doctoral Dissertation Improvement Grant), Johnson & Johnson, the CDC, and University of Idaho IMCI pilot grants.
People
Current graduate students
- Tristan Moxley, PhD, Bioinformatics and Computational Biology
- Alorah Grossman, PhD, Bioinformatics and Computational Biology (since 2026)
- Bibek Sharma, MS, Bioinformatics and Computational Biology (since 2026)
Former graduate students
- Edmund Ampofo, MS Statistics, 2025
- Xia Liu, MS Statistics, 2024
- Andrei Kulumbetov, MS Statistics, 2024
- Raee Bhagat, MS Statistics, 2023
- Tristan Moxley, MS Statistics, 2022
- Jessica Kowalik, MS Biology, 2014
Recent undergraduate researchers
- Tamsen Farris, 2025
- Austin Smith, 2024
- Riley Kouns, 2023
- Leah Davidson, 2022
- Trevor Griffin, 2021
- Kellie Rich, 2020