# Research

## Geometric Deep Learning

Department of Mathematics, Harvey Mudd College

*September 2021 - Present*

Advisor: Weiqing Gu

I built a temporal graph convolutional network (T-GCN) model to detect seizures in electroencephalogram (EEG) data. T-GCN models can detect the time of a seizure and its region in the brain by combining graph convolutional network (GCN) and recurrent neural network (RNN) architectures. Unlike convolutional neural networks (CNNs), T-GCNs can handle the non-Euclidean signals in the EEG data. Professor Weiqing Gu and I are currently working to integrate EEG and gait data with geometric deep learning methods to diagnose Parkinson’s disease and analyze its progression.

## Statistical Learning

AMISTAD Lab

*October 2020 - Present*

Advisor: George D. Montañez

Our work focused on generalization bounds in statistical learning. Because the paper is currently under review, I have removed detailed information about it. The paper is currently under review at the 33rd International Conference on Algorithmic Learning Theory (ALT 2022), and I am the second of four authors on the submission.

## Graphical Models

AMISTAD Lab

*May 2020 - October 2020*

Advisor: George D. Montañez

We worked on a probabilistic theory of abductive reasoning. Specifically, we developed a model that unifies selective and creative abduction by focusing on common cause abduction. Our model incorporates principles of causation by modeling abductive reasoning through a Bayesian network. I integrated selective and creative abduction with causal principles by developing two algorithms, which allow the model to compute novel and common-cause explanations for observations. I also developed one of the two similarity metrics we used, derived from the Jaccard index and edit-distance, in order to compute the similarity of graph nodes. I am the first of four authors on the paper published at the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021).