activity
20172026
most citedTransfer learning to improve streamflow forecasts in data sparse regions

11 citations · 27 across the 11 of their papers we have counts for

collaborators

15 papers

cs.LG2026★ 6 cited

Optimal Transport for Efficient, Unsupervised Anomaly Detection on Industrial Data

Abigail Langbridge, Fearghal O'Donncha, James T Rayfield +1

Effective anomaly detection frameworks are a central pillar of the Industry 4.0 paradigm. In this paper, we introduce an Optimal Transport (OT)-based framework for anomaly detectio…

cs.LG2026

Machine Learning in Fish Farming

Fearghal O'Donncha, Nikos Papandroulakis, Jennie Korus +10

This chapter explores how machine learning (ML) is transforming aquaculture, with a particular focus on enhancing decision-making processes and improving operational efficiency. Th…

cs.AI2025

AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial Asset Operations and Maintenance

Dhaval Patel, Shuxin Lin, James Rayfield +7

AI for Industrial Asset Lifecycle Management aims to automate complex operational workflows, such as condition monitoring and maintenance scheduling, to minimize system downtime. W…

cs.AI2024

Towards Automated Solution Recipe Generation for Industrial Asset Management with LLM

Nianjun Zhou, Dhaval Patel, Shuxin Lin +1

This study introduces a novel approach to Industrial Asset Management (IAM) by incorporating Conditional-Based Management (CBM) principles with the latest advancements in Large Lan…

cs.LG2023★ 1 cited

Causal Temporal Graph Convolutional Neural Networks (CTGCN)

Abigail Langbridge, Fearghal O'Donncha, Amadou Ba +3

Many large-scale applications can be elegantly represented using graph structures. Their scalability, however, is often limited by the domain knowledge required to apply them. To a…

cs.LG2023★ 5 cited

A SWAT-based Reinforcement Learning Framework for Crop Management

Malvern Madondo, Muneeza Azmat, Kelsey Dipietro +5

Crop management involves a series of critical, interdependent decisions or actions in a complex and highly uncertain environment, which exhibit distinct spatial and temporal variat…