Research
Projects
page intro: the questions behind your projects
Current
FluBNF: influenza hospitalization forecasts for CDC FluSight
Department of Biological Sciences, Northern Arizona University · 2026–27 season
Developed FluBNF (an open-source, PyBNF-based influenza forecasting tool) and now submit weekly forecasts of laboratory-confirmed influenza hospital admissions using two models to the CDC FluSight challenge for the 2026-27 season.
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- Implemented a Liu–West particle filter for sequential Bayesian parameter estimation in PyBNF (now public in the Los Alamos National Laboratory PyBioNetFit (LANL/PyBNF) repository as of October 2026).
- Developing and validating a Python curve fitting framework (PyBioNetFit/PyBNF) to forecast transmissible diseases including Influenza using custom SIR models to real-world epidemiological data.
PyBioNetFit: calibrating rule-based models
Department of Biological Sciences, Northern Arizona University · August 2024 – Present
Developed and performed benchmarks of Bayesian MCMC samplers including Adaptive Metropolis, DREAM(ZS) and preconditioned DREAM for calibrating rule-based systems biology models in PyBNF across a broad range of analytical targets and BioNetGen models of differing dimensionality/cost.
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- Automated and improved a previously manual MEK isoform signaling model (reaching faster convergence and improved fits to data). Manuscript published in Frontiers (2026).
- Built Slurm-based pipelines on the NAU Monsoon high-performance computing cluster, enabling multi-day parallel sampling runs. This includes resumable checkpoints, automated result collection, and failure diagnostics.
BNGsim benchmarking
With the Hlavacek group, Los Alamos National Laboratory
Designed and implemented a multi-engine benchmark suite for BNGsim (an open-sourced C++17 simulation engine for BioNetGen rule-based models) to evaluate and compare the performance of other simulators, including libRoadRunner, AMICI, COPASI, and SciPy, across deterministic, stochastic, network-free, steady-state, and forward-sensitivity analyses on increasing numbers of species, from several up to thousands.
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- Authored the supplementary materials for the associated BNGsim benchmarking manuscript (in preparation) with the Hlavacek group at Los Alamos National Laboratory.
Large language model agents and BioNetGen models
Department of Biological Sciences, Northern Arizona University
Developing a manuscript (in preparation) evaluating how well large language model agents rebuild published rule-based signaling models in BioNetGen.
Past
MicroHub: SARI forecasting for South America
School of Informatics, Computing, and Cyber Systems, Northern Arizona University · May 2026 – October 2026
Generated SARI (severe acute respiratory infection) forecasts for the respiratory disease seasons of Chile, Uruguay, Argentina, and Brazil with a new forecasting application, called MicroHub, written in R and Shiny (inspired by collaborative forecasting hubs like FluSight).
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- Researched and prototyped nowcasting methods for possible inclusion in MicroHub, which does not currently support such methods.
- Enhanced the MicroHub application through improvements to the user interface, increased functionality, and expanded capabilities, including automatically generated reports, producing time-series plots, supporting multiple languages, etc.
COVID-19 forecasting and social distancing
Posner Lab, Northern Arizona University · January 2020 – May 2023
Helped develop stochastic simulation algorithms for validation within PyBioNetFit (PyBNF), a Python-based software for curve fitting and parameter estimation in mechanistic models, which I applied to forecasting COVID-19 cases in the U.S. and the Navajo Nation.
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- Constructed data scraping pipelines and leveraged remote parallel computing to produce real-time estimates with four-week forecasting horizons during the SARS-CoV-2 pandemic. These were shared with public health authorities (e.g., NNDOH) and university administration (NAU) and have been published as a peer-reviewed manuscript.
- Collaborated with researchers at Los Alamos, Sandia, and Oak Ridge National Laboratories to develop transmission models in Python that implemented social distancing effects for estimating epidemiological parameters with high-performance parallel computing clusters, and Bayesian uncertainty quantification techniques, for fitting to datasets published by the Centers for Disease Control and Prevention (CDC) and the NNDOH. This work resulted in publications in Emerging Infectious Diseases, Bulletin of Mathematical Biology, and Viruses.
- Estimated the impact of social distancing policies on the spread of SARS-CoV-2 in the Navajo Nation using mechanistic models, which highlighted the differential effect of the analogous policies in adjacent states and was published in PLOS Global Public Health.
BRAF-V600E signaling in melanoma
Posner Lab, Northern Arizona University · with the Flinn Foundation and Dr. Matthew Salanga's Zebrafish laboratory
Expanded the utilization of PyBNF to rule-based systems biology modeling using BNGL, applied to signaling pathways in cancer and the role of BRAF-V600E mutations in melanoma in collaboration with the Flinn Foundation and Dr. Matthew Salanga's Zebrafish laboratory at Northern Arizona University (NAU).
AS3MT knockout zebrafish
Northern Arizona University · NAU Undergraduate Research Symposium, 2022
Generated a new Arsenic-3-methyltransferase (AS3MT) knockout zebrafish line using CRISPR-Cas9 microinjection, which I validated using PCR and husbandry strategies before demonstrating increased embryonic sensitivity and developmental defects following arsenic exposure. This work was co-presented with Zack Schmidt (former student) at the NAU Undergraduate Research Symposium in 2022.