About me

Research Interests

My research is on Deep Learning Optimization, with a focus on discovering ways to understand the dynamics of how neural networks learn during training.

Bio

I’m a PhD candidate at Worcester Polytechnic Institute (WPI) in the DAISY Lab, advised by Professor Elke Rundensteiner. Prior to my PhD, I earned B.S. degrees in computer science and data science at WPI.

Recent News

• Nov 25: First author paper on how NTK-inspired data augmentations can effect neural net training accepted @ AAAI 2026.

• Dec 24: First author paper on the applicability of theory relating to infinitely-large neural nets for improving real model training accepted @ AAAI 2025.

• Oct 24: First author paper on stabilizing GANS accepted at @ IEEE Big Data 2024.

• July 24: Coauthored paper on robust anomaly detection accepted at @ SIGMOD 2025

• Sept 23: Won first place award at the GSA’s Applied AI Challenge in LLMS with Topologe for detecting long-form generated text from blackbox LLMs.

Select Publications

Neural Tangent Kernels Under Stochastic Data Augmentation
Joshua DeOliveira, Sajal Chakroborty, Walter Gerych, Elke Rundensteiner.
(To Appear In) AAAI, 2026.

The Surprising Effectiveness of Infinite-Width NTKs for Characterizing and Improving Model Training
Joshua DeOliveira, Walter Gerych, Elke Rundensteiner.
AAAI, 2025.

GAN Stabilization Under Practical Training Assumptions.
Joshua DeOliveira, Walter Gerych, Elke Rundensteiner.
IEEE Big Data, 2024.

HAR-CTGAN: A Mobile Sensor Data Generation Tool for Human Activity Recognition.
Joshua DeOliveira, Walter Gerych, Aruzhan Koshkarova, Elke Rundensteiner, Emmanuel Agu.
IEEE Big Data 4th Special Session on HealthCare Data, 2022.