Mathematician and data scientist. Interested in topology, machine learning, their intersection, and opportunities for effecting social good.
I worked as a software engineer on a team building an always-on debugger.
I worked as a software engineer, implementing and developing differential privacy techniques.
I worked as a data scientist, analyzing housing data for price prediction and to understand human-machine interaction with predictive algorithms.
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.
I taught an upper-division Computer Science course on Machine Learning.
I worked as a Teaching Assistant for the General Assembly Data Science course.
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.
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.
We proved generalizations of the isoperimetric inequality for both spherical and hyperbolic wave fronts (i.e. piecewise smooth curves which may have cusps).