Software Implementations

I develop and maintain software implementations (mainly in Python and R) accompanying my research on Bayesian modeling, scalable inference, scientific machine learning, and the analysis of structured data.

pyJASPER

Python implementation of JASPER, a spatial basis function based count-regression framework for joint Bayesian analysis of spatial expression, for detecting spatially variable genes.

GitHub · Preprint

VaSST

Python implementation of VaSST, a variational inference method for symbolic regression using soft symbolic trees, designed for interpretable and uncertainty-aware equation discovery.

GitHub · Paper

HierBOSSS

Python implementation of HierBOSSS, a hierarchical Bayesian symbolic regression method using operator-induced and regularized symbolic forests for scientific equation discovery.

GitHub · Preprint

TAVIE-SSG

Python implementation of TAVIE-SSG, a scalable generalized tangent approximation based variational inference framework for strongly super-Gaussian likelihoods.

GitHub · Paper

AMO-GP

R implementation of AMO-GP, an additive nonparametric regression method with spatial and network-valued predictors, motivated by neuroimaging applications.

GitHub · Preprint

dame-flame

Python package for DAME and FLAME: a family of fast and interpretable almost-matching-exactly methods for scalable and efficient causal inference.

PyPI · Paper

ODIN-python

Python implementation of ODIN, an outlier-detection method for populations of network-valued observations, motivated by applications in structural connectomics.

GitHub · Paper

ODIN-R

R implementation of ODIN, an outlier-detection method for populations of network-valued observations, motivated by applications in structural connectomics.

GitHub · Paper