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).

Publications

  1. Espinosa Dice N, Kaye M, Ahmed H, Montanez G, “A Probabilistic Theory of Abductive Reasoning.” 13th International Conference on Agents and Artificial Intelligence (ICAART 2021), Online, Feb 4-6, 2021.
  2. Paper under review at the 33rd International Conference on Algorithmic Learning Theory (ALT 2022).