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 variational inference. Previously, during my PhD, I worked on models for brain structural connectomes.

Spatial Transcriptomics

I am currently working on a range of projects focusing on novel Bayesian methodology for Spatially Resolved Transcriptomics (SRT) data, where gene expression is measured across different locations in a tissue sample. Specifically I work on the Spatially Varying Gene detection problem as well as gene network discovery in SRT datasets. I am collaborating with Rajarshi Guhaniyogi, Yang Ni and Bani K. Mallick on this work.

Multi-Omic Integration

I am working on bioinformatics projects involving multi-omics data analysis at Chapkin lab. My work involves using multiple omics data for biomarker discovery.

Variational Inference

I am currently working on a project to develop a variational inference framework for generalized linear models that encompases a wide variety of distributions, is computationally efficient and has provable theoretical guarantees. This is a joint work with Somjit Roy and Dr. Debdeep Pati.

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)