Research Focus

My research focus is in Topological Data Analysis, and specifically in addressing aspects of the field that make it unapproachable by outsiders. My efforts have been geared towards expanding usability and interpretability of existing methods, developing easy to use tools to make the existing techniques more accessible, and exploring new applications to expand the possible use cases of TDA.

Topological Data Analysis in Python

In support of my research and many others, I develop and maintain the Scikit-TDA Python package. This has been a monumental undertaken and has completely consumed my research and development time. My role has ranged from implementing and incorporating new algorithms to developing documentation and build systems to managing social media and promotion.

This project grew from an implementation of Mapper to an all encompassing system for nearly all common methods used in Topological Data Analysis. The project has had overwhelmingly positive support from the TDA community and has a growing list of contributors.

If you’d to learn more about the project, I will begin giving talks on the project in the Summer of 2019. Please reach out!

Explainable Machine Learning with Mapper

While visiting Pacific Northwest National Laboratory, I collaborated with Dustin L. Arendt to develop new ways Mapper could be applied for explaining machine learning models.

Initial progress in this direction will be a part of the IEEE VIS 2018 Workshop on VISxAI. You can read more about the work here.

Sewing with Topology

In collaboration with Bei Wang and Bala Krishnamoorthy, I am developing ways of composing Mappers built from different filter functions together, and developing techniques for quantifying the topological correlation and complexity inherent in particular filter functions.

I presented preliminary results of this work at the Young Researchers Forum at SoCG 2018 and at SIAM CSE 2019. You can find the abstract here.