About Me
Update: I will soon be scheduling my defense for January/February 2024. I am currently on the job market and will be available to start employment Feburary/March 2024.
I am an NSF Graduate Research Fellow and a PhD Student in the Division of Applied Mathematics at Brown University. I am working under Dr. Lorin Crawford as a member of his lab. I expect to graduate in May 2024 and plan to pursue a career in data analytics.
My research interests include:
Shape statistics, statistical topology.
What if the data we want to analyze is not a vector, but a set of shapes? As imaging technology improves in two and three dimensions, I develop and adapt tools from statistics and machine learning to handle shapes data.
Variable Selection, Interpretable Models
Neural networks and other blackbox methods are effective tools in machine learning, but we still desire transparency both to check model performance and achieve the highest ethical standards. I build methods for determining which features are most important to a model's output. Most recently, we developed a tool that can determine a feature's importance not just on a global data set but also local subsets, which has applications for genetics and personalized medicine.
Complex, High Dimensional Data Analysis, Visualization
Linear regression is a fantastic tool, except when there are a high number of features, and those features have nonlinear relationships. In those cases, we can leverage tools from network science, topological data analysis, or machine learning to make sense of these data sets.