Research

I am a methodologist working at the intersection of complex systems and statistical computing, with a focus on high-performance computing and machine learning. A central part of my work is the development of scientific software: as a professional software engineer, I dedicate significant effort to ensuring that the methods and tools I create are reliable, open, and widely accessible to the research community. My collaborations span diverse fields, including epidemiology, public health, social network analysis, and genomics, but they all share a common foundation in heavy computation and advanced statistical methods.

For the most up-to-date list of my publications, please visit my Google Scholar profile.

Vega Yon, G. G., Thornton, D., Redd, A., Pulsipher, A., Toth, D., Dorsan, E., Rennert, L., Johnson, K., White, L. F., Gruninger, R., Nolen, L. D., & Samore, M. H. (2026). Practical Guidelines and Reflections on Building Public Health Software: A Measles Case Study (Preprint). JMIR Public Health and Surveillance. https://doi.org/10.2196/preprints.94240

Milando, C. W., Vega Yon, G. G., Johnson, K., Urbinati, A., St-Onge, G., Klein, B., Cori, A., & White, L. F. (2026). A vision for estimation of the instantaneous reproductive number. Epidemics, 100885. https://doi.org/10.1016/j.epidem.2026.100885

Najafzadehkhoei, S., Vega Yon, G. V., Modenesi, B., & Meyer, D. S. (2025, September 6). Machine Generalize Learning in Agent-Based Models: Going Beyond Surrogate Models for Calibration in ABMs. https://doi.org/10.48550/arXiv.2509.07013

Mohammadi, M., Najafzadehkhoei, S., Vega Yon, G. G., & Wang, Y. (2025). A novel approach for classifying monoamine neurotransmitters by applying machine learning on UV plasmonic-engineered auto fluorescence time decay series (AFTDS). Nanoscale Advances, 10.1039.D5NA00416K. https://doi.org/10.1039/D5NA00416K

Piombo, S., Vega Yon, G. G., & Valente, T. W. (2025). The impact of social norms on diffusion dynamics: A simulation of e-cigarette use behavior. Health Education & Behavior.

Sargent, M., Matthews, L. J., Vega Yon, G. G., Storholm, E. D., Ober, A. J., & Green, H. D. (2024). Assessing the dynamics of PrEP adoption in a national-scale physician network. Social Networks, 78, 226–237. https://doi.org/10.1016/j.socnet.2024.02.001

Tanaka, K., & Vega Yon, G. G. (2024). Imaginary network motifs: Structural patterns of false positives and negatives in social networks. Social Networks, 78, 65–80. https://doi.org/10.1016/j.socnet.2023.11.005

Meyer, D., & Vega Yon, G. G. (2023). epiworldR: Fast Agent-Based Epi Models. Journal of Open Source Software, 8(90), 5781. https://doi.org/10.21105/joss.05781

Vega Yon, G. G. (2023). Power and Multicollinearity in Small Networks: A Discussion of “Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks” by Krivitsky, Coletti, and Hens. Journal of the American Statistical Association, 118(544), 2228–2231. https://doi.org/10.1080/01621459.2023.2252041

Panahi, S., Kennedy, E., Roghani, A., Vega Yon, G. G., VanCott, A., Gugger, J. J., Raquel Lopez, M., & Jo Pugh, M. (2023). Veteran perspectives of epilepsy care: Impact of Veteran satisfaction, knowledge, and proactivity. Epilepsy & Behavior, 144, 109218. https://doi.org/10.1016/j.yebeh.2023.109218

Love, J., LaPrete, C. R., Sheets, T. R., Vega Yon, G. G., Thomas, A., Samore, M. H., Keegan, L. T., Adler, F. R., Slayton, R. B., Spicknall, I. H., & Toth, D. J. A. (2023). Characterizing spatiotemporal variation in transmission heterogeneity during the 2022 mpox outbreak in the USA [Preprint]. Epidemiology. https://doi.org/10.1101/2023.05.10.23289580

Vega Yon, G. G., Pugh, M. J., & Valente, T. W. (2022). Discrete Exponential-Family Models for Multivariate Binary Outcomes (No. arXiv:2211.00627). arXiv. https://arxiv.org/abs/2211.00627

Ouellet, M., Hashimi, S., & Vega Yon, G. G. (2022). Officer networks and firearm behaviors: Assessing the social transmission of weapon-use. Journal of Quantitative Criminology. https://doi.org/10.1007/s10940-022-09546-9

Hâncean, M.-G., Perc, M., Vega Yon, G. G., Gheorghiță, A., & Mihăilă, B.-E. (2022). The formation of political discussion networks. Royal Society Open Science, 9(1), 211609. https://doi.org/10.1098/rsos.211609

Vega Yon, G. G. (2021). Building, importing, and exporting GEXF graph files with rgexf. Journal of Open Source Software, 6, 3456. https://doi.org/10.21105/joss.03456

Vega Yon, G. G., Thomas, D. C., Morrison, J., Mi, H., Thomas, P. D., & Marjoram, P. (2021). Bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic trees. PLOS Computational Biology, 17(2), 1–35. https://doi.org/10.1371/journal.pcbi.1007948

Vega Yon, G. G., Slaughter, A., & Haye, K. de la. (2021). Exponential random graph models for little networks. Social Networks, 64, 225–238. https://doi.org/10.1016/j.socnet.2020.07.005

Vega Yon, G. G., Thomas, D. C., Morrison, J., Mi, H., Thomas, P. D., & Marjoram, P. (2021). Modeling gene functional evolution using sufficient statistics.

Valente, T. W., & Vega Yon, G. G. (2020). Diffusion/Contagion Processes on Social Networks. Health Education & Behavior, 47(2), 235–248. https://doi.org/10.1177/1090198120901497

Vega Yon, G. G., & Quistorff, B. (2019). parallel: A command for parallel computing. The Stata Journal: Promoting Communications on Statistics and Stata, 19(3), 667–684. https://doi.org/10.1177/1536867X19874242

Vega Yon, G. G., & Marjoram, P. (2019). fmcmc: A friendly MCMC framework. Journal of Open Source Software, 4(39), 1427. https://doi.org/10.21105/joss.01427

Vega Yon, G. G., & Marjoram, P. (2019). slurmR: A lightweight wrapper for HPC with Slurm. Journal of Open Source Software, 4(39), 1493. https://doi.org/10.21105/joss.01493

Bell, B. M., Spruijt-Metz, D., Vega Yon, G. G., Mondol, A. S., Alam, R., Ma, M., Emi, I., Lach, J., Stankovic, J. A., & De La Haye, K. (2019). Sensing eating mimicry among family members. Translational Behavioral Medicine, 9(3), 422–430. https://doi.org/10.1093/tbm/ibz051

Haye, K. de la, Shin, H., Vega Yon, G. G., & Valente, T. W. (2019). Smoking Diffusion through Networks of Diverse, Urban American Adolescents over the High School Period. Journal of Health and Social Behavior, 60(3), 362–376. https://doi.org/10.1177/0022146519870521

Valente, T. W., Wipfli, H., & Vega Yon, G. G. (2019). Network influences on policy implementation: Evidence from a global health treaty. Social Science and Medicine, 222, 188–197. https://doi.org/10.1016/j.socscimed.2019.01.008

Vega Yon, G. G. (2014). Capital Necesario Unitario (CNU): Cálculo e Introducción Del Módulo de Stata CNU (Working Papers No. 57). Superintendencia de Pensiones. https://ideas.repec.org/p/sdp/sdpwps/57.html

Fábrega Lacoa, J., & Vega Yon, G. G. (2013). El impacto del rating televisivo sobre la actividad en Twitter: evidencia para Chile sobre la base del evento TELETÓN 2012. Cuadernos.info, 33, 43–52. https://doi.org/10.7764/cdi.33.533

Quintanilla, X., Poblete, I., Vega Yon, G. G., et al. (2013). Estudio actuarial de los fondos del seguro de cesantía.

Repetto, A., & Vega Yon, G. G. (2013). El impacto de un alza en la cotización previsional: Pensiones, salarios y empleo. Centro de Polı́ticas Laborales, UAI.