Vega Yon Lab
Principal Investigator: George Gerald Vega Yon
Institution: University of Utah (Salt Lake City, UT)
Primary Subfield: Computational Biology
Recruitment Status: Inquire for rotation/fellowship opportunities
Research Focus
Summary/Abstract Exponential-family Random Graph Models [ERGMs] are a powerful statistical framework for analyzing com- plex networks, with applications in epidemiology, social sciences, genomics, and neuroscience. However, their broader adoption is hindered by persistent methodological challenges, particularly in estimation stability and power analysis for multi-network studies. Current estimation techniques often fail to converge when applied to real-world networks with high complexity, limiting their reliability. Additionally, there is no established framework for power analysis in multi-network ERGM studies, making study design and reproducibility difficult. This project directly addresses these critical gaps by developing (1) a novel Conditional ERGM Seeding approach to im- prove estimation stability and convergence, and (2) the first power analysis framework tailored to ERGM-based multi-network studies. We will achieve these objectives through two specific aims. Aim 1 will develop, validate, and implement Conditional ERGM Seeding to improve parameter estimation, ensuring more stable and reliable convergence in computationally challenging networks. Aim 2 will create a comprehe…
Active NIH Awards
- 1R21LM015164-01A1: Advancing Exponential-Family Random Graph Model Estimation and Power Analysis for Biomedical and Public Health Research (2026)
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