Jesús Arroyo

Jesús Arroyo is an Assistant Professor in the Department of Statistics at Texas A&M University, specializing in statistical network analysis, machine learning, and neuroimaging applications. He holds a PhD in Statistics from the University of Michigan (2018), advised by Professor Liza Levina, and dual B.Sc. degrees in Applied Mathematics and Computer Engineering from ITAM, Mexico.

• Education: PhD in Statistics (University of Michigan, 2018); B.Sc. in Applied Mathematics and Computer Engineering (ITAM).
• Postdoctoral Experience: Center for Imaging Science at Johns Hopkins University and Department of Mathematics at University of Maryland, College Park (2018–2021).

His research focuses on developing statistical methods for analyzing network data, particularly in neuroimaging and high-dimensional settings. Recent work spans multilayer community detection, anomaly detection in dynamic networks, and integrating covariates into network structure learning. He has secured NSF funding (DMS-2413553) for projects on statistical network integration and also explores causal inference and prediction in network data.

Key trends in his publications include applications of network analysis to neuroscience, methodological advancements in high-dimensional statistics, and leveraging machine learning for complex network structures. Collaborators include prominent researchers like Carey E. Priebe, Joshua T. Vogelstein, and Vince Lyzinski.

Jesús actively participates in academic conferences, including the International Indian Statistical Association 2025 Conference and workshops on causal inference and network learning at IMPA and other institutions. His work bridges theoretical statistics, computational methods, and real-world neuroimaging challenges.