Research Interests:
- Hierarchical Bayesian models
- Mixed models: General and generalized, linear and non-linear, uni- and multivariate
- Multilevel sources of uncertainty and heterogeneity in complex systems
- Data architecture in the context of designed experiments and observational data
- Motivating interdisciplinary problems in agriculture, veterinary medicine and biology
Teaching Interests:
- Applied hierarchical Bayesian modeling
- Mixed models: General and generalized, linear and non-linear, uni- and multivariate
- Structural equation models
- Modern experimental design
- Training of the next generation of statisticians
- The role of Statistics in research collaborations
Collaborative Interests:
- Multidisciplinary approaches to complex problems
- Agricultural systems, food production systems
- One health, global health
- Biological ecosystems
Research Interests:
- Modern quantitative methods and analytic strategies for data-driven problems in the agricultural sciences, commonly known as agricultural statistics.
Specifically, my research interests are focused on statistical methods known as hierarchical models, including Bayesian implementations and their frequentist counterparts, mixed models, encompassing cutting-edge methodological developments and innovative interdisciplinary applications in the agricultural sciences (e.g. animal and crop sciences, veterinary medicine and epidemiology) to support evidence-based decision making.
Dr. Bello’s research focus is in the development and application of linear mixed models, with emphasis on hierarchical Bayesian implementations and applications motivated by problems in animal agriculture and veterinary epidemiology. Of particular interest is the hierarchical modeling of sources of heterogeneity at multiple levels, including means, variances and covariances/correlations across levels of the data structure to capture the inherently multidimensional complexity of production systems.
