研究

我是一位方法論學者，專注於複雜系統和統計計算的交叉領域，重點關注高性能計算和機器學習。我工作的核心部分是開發科學軟件：作為一名專業軟件工程師，我投入大量精力確保我創建的方法和工具可靠、開放，並且研究社區可以廣泛使用。我的合作涵蓋多個領域，包括流行病學、公共衛生、社交網絡分析和基因組學，但它們都有一個共同的基礎——密集計算和先進的統計方法。

欲獲取我最新的出版物列表，請訪問我的 Google Scholar 個人資料。

Najafzadehkhoei, S., 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., Yon, G. V., & Wang, Y. (2025, July 9). A novel approach for classifying Monoamine Neurotransmitters by applying Machine Learning on UV plasmonic-engineered Auto Fluorescence Time Decay Series (AFTDS). https://doi.org/10.48550/arXiv.2507.07227

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.

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