Peter David Fagan

I’m the founder of Saoirse Health, where I'm building AI systems that help people track and improve their mental and physical health without compromising privacy. More to come soon.

First-Author Publications

Peter David Fagan

Preprint, 2025

Project Page / arXiv

Early preprint of Quantum-Inspired evolutionary algorithm for evolving graph dynamical systems.

Peter David Fagan

Preprint, 2025

Project Page / arXiv

We introduce a framework for applying keyed chaotic dynamical systems to encrypt and decrypt tensors in machine learning pipelines. This lightweight, deterministic approach enables authenticated inference without modifying model architectures or requiring retraining. Designed for privacy-first AI, this method provides a new building block at the intersection of cryptography, dynamical systems, and neural computation.

Peter David Fagan, Subramanian Ramamoorthy

Preprint, 2024

Project Page / arXiv

We introduce a new recurrent neural network layer that incorporates fixed nonlinear dynamics models where the dynamics satisfy the Echo State Property. We show that this neural network layer is well suited to the task of overcoming compounding errors under the learning from demonstration paradigm. Through evaluating neural network architectures with/without our layer on the task of reproducing human handwriting traces we show that the introduced neural network layer improves task precision and robustness to perturbations all while maintaining a low computational overhead.

Collaborative Publications

Rimvydas Rubavicius, Peter David Fagan, Alex Lascarides, Subramanian Ramamoorthy

Preprint, 2025

Project Page / arXiv

In this work, we introduce an interactive task learning framework to cope with unforeseen possibilities by exploiting the formal semantic analysis of embodied conversation.

The DROID Dataset Team

Robotics: Science and Systems (R:SS), 2024

Project Page / arXiv

In this work, we introduce DROID (Distributed Robot Interaction Dataset), a diverse robot manipulation dataset comprising 76k demonstration trajectories or 350 hours of interaction data, collected across 564 scenes and 86 tasks by 50 data collectors in North America, Asia, and Europe over the course of 12 months.

Open X-Embodiment Team

IEEE International Conference on Robotics and Automation (ICRA), May 2024

Project Page / arXiv

In this work, we introduce Open X-Embodiment, a comprehensive collection of robotic learning datasets and RT-X models. These datasets and models facilitate research in embodied AI by providing large-scale, diverse, and realistic environments for training robotic systems. The datasets cover a wide range of tasks and scenarios, enabling robots to learn complex behaviors through interaction with their environment.

Software

Peter David Fagan

Google Summer of Code, 2022

Code

This is the official Python binding for the MoveIt 2 library.