Research Interests

I am highly motivated to develop novel statistical methodology for complex structured data with great emphasis on real world application and computational performance. My current research projects as a postdoc include data analytic and methodological research on multi-omics data, spatial transtriptomics and generative AI. Previously, during my PhD, I worked on models for brain structural connectomes.

Bioinformatics

As a postdoctoral researcher, I am working on bioinformatics projects involving multi-omics data analysis at Chapkin lab. I am also working on methods for spatial transcriptomics and single cell RNA sequencing data with mentors Dr. Yang Ni and Dr. Bani Mallick.

Artificial Intelligence (AI)

Another direction of my current research work involves developing methods at the interface of artificial generative intelligence and Bayesian analysis motivated by applications in bioinformatics and material sciences under the mentorship of Dr. Debdeep Pati and Dr. Bani Mallick.

Brain Connectomics

Brain connectomics is the study of connections in the brain. In particular, structural connectomics is the study of physical connections in the brain. My PhD dissertation involved developing novel methods for outlier detection and modeling in the context of structural connectomes.

  1. Outlier Detection for Multi-Network Data: Dey, P., Zhang, Z., & Dunson, D. B. Bioinformatics(2022). Preprint: arXiv. Code: R and Python.
  2. Fast Scalable Density Estimation for Continuous Structural Connectomics: Dey, P., Zhang, Z., & Dunson, D. B. (Work in progress)
  3. Hierarchical Muliple Density Estimation using Mondrian Processes: Dey, P., Zhang, Z., & Dunson, D. B. (Work in progress)

Other projects

  1. dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference: Gupta, N.R., Orlandi, V., Chang, C., Wang, T., Morucci, M., Dey, P., Howell, T.J., Sun, X., Ghosal, A., Roy, S., Rudin, C., & Volfovsky, A. Preprint: arXiv (2021)