Welcome to my personal research website.
Hello! My name is Mirabel Reid, and I am currently working toward my PhD in Computer Science at Georgia Tech. I am advised by Dr. Santosh Vempala.
Contact me: mreid48@gatech.edu
Connect via LinkedIn: LinkedIn
View my resume here.
My doctoral research has focused on analyzing iterative processes over random graphs. My primary focus has been studying the k-cap process, defined as follows. Given a random graph G, the π-cap process is defined as follows. At each time step π‘, there is a set π΄π‘ consisting of π vertices of G, where π is a fixed parameter of the process. This set fires, delivering a signal to its neighbors. At the next time step, π‘+1, the π vertices with the highest degree from π΄π‘ fire. This simple process has complex underlying behavior. It also has applications to the study of the behavior of firing neurons in the brain.
Given an initial firing set, a natural question to ask is how the structure of the firing set evolves over time. This question depends heavily on the structure of the graph. I studied the convergence of this process on geometric random graphs, focusing on a graph structure that had dense local subgraphs. In a geometric random graph, the probability of an edge is a function of the distance between its endpoints in a hidden variable space. Because this distance can represent physical space, this type of graph is often used to model physical transportation networks. It has been studied as a model for networks of neurons in the brain. The paper is available here. If you are interested, you can also download simulation code at this repository
In Summer 2023, I interned at the Max Planck Institute for Intelligent Systems, in the Social Foundations of Computation Group. I worked with Dr. Samira Samadi on online algorithms for Learning to Defer to human experts.
In Summer 2022, I was an intern at Los Alamos National Laboratory, where I researched the machine learning workflow in the sciences. Building ML models in the natural sciences requires a different outlook, often with a heavier focus on data exploration. Despite this, most of the literature on the ML workflow focuses soley on industry applications. I investigated this disparity and built a metadata visualization platform to meet the data exploration needs of a group at Los Alamos.
I interned at the Software Engineering Institute from January-August 2020. I worked with the Emerging Technology Center and the Software Solutions Division, and researched novel applications of Graph Neural Networks to software development.
I participated in the Civic Data Science REU at Georgia Tech in Summer 2019. I worked under Dr. Omar Asensio to help analyze the impact of federal housing policy in Albany, GA. You can learn more about the project here.
From January 2017-January 2020, I worked as a research assistant at the Geomorpohogy Lab at the University of Pittsburgh under Dr. Eitan Shelef. I studied a mathematical model for common properties of natural transportation networks.