I'm working in two main areas of research: (1) theoretical condensed matter with Prof. Sethna, and (2) physics education research with Prof. Holmes.

# Theoretical Physics

Using information geometry
(an area of mathematics that combines statistics and information theory with differential geometry)
I'm investigating properties of models from all over science (such as cosmology, statistical physics, and machine learning).
By constructing a mathematical object callled a *model manifold* – the space of all possible predictions for a
model – and analyzing its geometry, properties of the model itself can be revealed.

## Visualizing Probabilistic Models

Visualizing complex, high-dimensional data in a way that captures important features can reveal emergent phenomena of a model and provide valuable insight. We
developped a new way of embedding complex, probabilisitic data that captures *intensive properties*, meaning it reveals the *information density*
contained in the data.

## Bounding Model Predictions

Bounds from approximating model predictions from *interpolation theory* translate to
geometric bounds on the *model manifold* – the space of all possible predictions for a model.

## Random Matrix Theory

Complex, nonlinear models from all over science exibit a hierarchical structure; perturbing certain parameter combinations have no little to no impact on model predictions, whereas others can cause
huge variations. Using *random matrix theory*, we can explain this hierarchy in parameter importance by expading the *Fisher Information Matrix* and
extracting low-rank matrices.

## Ising Model

In statistical physics, the *Ising Model* is used to describe
coupled atomic spins on a latice (in a magnetic field and at some temperature). By visualizing the space of all possible
Ising models, we reveal important features of the model, namely a (1) singularity near the critical point – which manifests itself as geometrically as
a cusp, and (2) how the manifold *flows* when the system is coarse-grained.

## Exponential Families

We visualize the space of exponential families using an *intensive embedding* we developped, which reveals the *information density* contained in a distribution. We illustrate the
connection between this distance and the *partition function* of exponential families, and consider two special cases:

(1) 1 Spin System, which can be interpreted as an Ising Model with uncoupled spins, and

(2) Gaussians, which are known to create a space of constant negative curvature.

# Physics Education Research

I am working on two projects with Prof. Holmes in physics education research.

The first project is exploring how gendered roles manifest in lab spaces, and the impact this has on task division.

## Cluster Analysis

By clustering the quantified behaviours of people in labs, we observe a gender-based division of tasks happening in labs which foster decision making and promote collaboration. In particular, we observe men dominating equipment use and women dominating laptop use.

## Group Dynamics

Through an qualitative analysis of individual groups working in physics labs, we explore how gendered roles manifest themselves, how they are assigned and the impact this has on participation.

The second project I'm working on is the development of the Physics Lab Inventory of Critical Thinking (PLIC), a new assessment that measures critical thinking skills in a lab setting.

## Cognitive Interviews

The PLIC aims to measure critical thinking in labs. In order to test our instrument, I conducted a series of cognitive interviews, and explore the various ways students interpret data and critique experimental methods.

## Factor Analysis

Often, when collecting complex high dimensional data, there are only a handful of underlying factors that determine the distribution. Using *factor analysis*, I explore
thousands of responses to the PLIC to determine what underlying factors influence people's interpretation of data, criticisms of experimental technique, and how they validate or
falsify a model.