Brandon Oselio
Postdoctoral Scholar, Biostatistics at the University of Michigan
Davis, CA
Ann Arbor, MI
Welcome to my site. I am a statistician and data scientist interested in implementing and improving state-of-the-art methods for a variety of challenging problems with data. In the past, I have worked extensively in the fields of statistical modeling for network data and statistical signal processing.
For information on some of the research topics I am interested in, please see here, or browse the highlighted research below. To see some of the code that I have written, please see here. A copy of my PhD thesis can be found here.
As of 2021, I am on the job market.
Highlighted Research
Statistical Models for Hierarchical Interaction Data
Network data often arises via a series of structured interactions among a population of constituent elements. E-mail exchanges, for example, have a single sender followed by potentially multiple receivers. We introduce a statistical model, termed the Pitman-Yor hierarchical vertex components model (PY-HVCM), that is well suited for structured interaction data. The proposed PY-HVCM effectively models complex relational data by partial pooling of local information via a latent, shared population-level distribution. We also establish global sparsity and power law degree distribution, which mirrors most real-world network behavior. More information can be found here.
Time-Adaptive Interaction Estimation using Adaptive Directed Information
