Below are a few of the projects I have developed throughout my research. Most of these (and more) can also be found on my GitHub.
NeuMapper. A scalable Mapper algorithm for neuroimaging data analysis. The Matlab implementation was designed specifically for working with complex, high-dimensional neuroimaging data and produces a shape graph representation that can be annotated with meta-information and further examined using network science tools.
Reciprocal Isomap. A reciprocal variant of Isomap for robust non-linear dimensionality reduction in Python. The ReciprocalIsomap transformer was inspired by scikit-learn’s implementation of Isomap, but the reciprocal variant enforces shared connectivity in the underlying k-nearest neighbors graph (i.e., two points are only considered neighbors if each is a neighbor of the other).
Landmark Cover. A Python implementation of NeuMapper’s landmark-based cover. The LandmarkCover transformer was designed for use with KeplerMapper, but rather than dividing an extrinsic space (e.g., low-dimensional projection) into overlapping hypercubes, the landmark-based approach directly partitions data points into overlapping subsets based on their intrinsic distances from pre-selected landmark points.
Partial Cluster. A partial clustering wrapper for Mapper. PartialCluster wraps around a user-specified clustering algorithm to enable globally-aware partial clustering. By pre-clustering an entire dataset, local subsets of the data can be partially clustered based on their global cluster assignments.
DyNeuSR. A Python visualization library for topological representations of neuroimaging data. The package combines visual web components with a high-level Python interface for interacting with, manipulating, and visualizing topological graph representations of functional brain activity.
DyNeuSR Notebooks. A collection of Jupyter notebook tutorials based on DyNeuSR. The tutorials introduce DyNeuSR’s Python API and highlight different aspects of analysis, including a simple trefoil knot demo and examples using real fMRI data from the Haxby 2001 visual decoding experiment.
DyNeuSR Fire. A command line interface for DyNeuSR based on the Python Fire library. The package wraps KeplerMapper and DyNeuSR into a single pipeline and automatically generates a simple command line interface that allows users to customize this pipeline.