Matthew Pancia

Mathematician and Data Scientist

Mathematician and data scientist. Interested in topology, machine learning, their intersection, and opportunities for effecting social good.


Software Engineer

I worked as a software engineer on a team building an always-on debugger.

Machine Learning Software Engineer

I worked as a software engineer, implementing and developing differential privacy techniques.

Data Scientist

I worked as a data scientist, analyzing housing data for price prediction and to understand human-machine interaction with predictive algorithms.

Data Scientist

I worked as a data scientist, cleaning, organizing, and analyzing a variety of medical-related data sources in order to try and understand the relationships between physicians, patients, conditions, and medical institutions.

Associate Assistant Adjunct Professor

I taught an upper-division Computer Science course on Machine Learning.

Teaching Assistant

I worked as a Teaching Assistant for the General Assembly Data Science course.


Code for San Francisco

Data Scientist

I was the Lead Data Scientist for the Data Science Working Group at the Code For San Francisco chapter of the Code For America. We provide resources and assistance to other projects in the chapter that require data analysis and visualization, as well as providing a collaborative learning environment.

Democratic Socialists of America

Tech Committee Member
– Present


University of Texas at Austin



University of Texas at Austin



Stony Brook University




Persistent homology for metric measure spaces, and robust statistics for hypothesis testing and confidence intervals

Published by Foundations of Computational Mathematics

We studied distributions of persistent homology barcodes associated to taking subsamples of a fixed size from metric measure spaces. We showed that such distributions provide robust invariants of metric measure spaces, and illustrate their use in hypothesis testing and providing confidence intervals for topological data analysis.

Isoperimetric inequalities for wave fronts and a generalization of Menzin's conjecture for bicycle monodromy on surfaces of constant curvature

Published by Advances in Geometry

We proved generalizations of the isoperimetric inequality for both spherical and hyperbolic wave fronts (i.e. piecewise smooth curves which may have cusps).


Technical Skills

  • Python
  • R
  • SQL
  • GIS
  • Data Modeling and Database Design

Machine Learning

Mathematics and Statistics



Native speaker


Too Much Coffee


Live Music