Nathaniel Hudson

According to our database, Nathaniel Hudson authored at least 38 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

Flight: A FaaS-based framework for complex and Hierarchical Federated Learning.

Nathaniel Hudson, Valérie Hayot-Sasson, Yadu Nand Babuji, Matt Baughman, J. Gregory Pauloski, Ryan Chard, Ian T. Foster, Kyle Chard

Future Gener. Comput. Syst., 2026

2025

Addressing Reproducibility Challenges in HPC with Continuous Integration.

Valérie Hayot-Sasson, Nathaniel Hudson, André Bauer, Maxime Gonthier, Ian T. Foster, Kyle Chard

CoRR, August, 2025

AERO: An autonomous platform for continuous research.

Valérie Hayot-Sasson, Abby Stevens, Nicholson T. Collier, Sudershan Sridhar, Kyle Conroy, J. Gregory Pauloski, Yadu N. Babuji, Maxime Gonthier, Nathaniel Hudson, Dante D. Sánchez-Gallegos, Ian T. Foster, Jonathan Ozik, Kyle Chard

CoRR, May, 2025

Topology-Aware Knowledge Propagation in Decentralized Learning.

Mansi Sakarvadia, Nathaniel Hudson, Tian Li, Ian T. Foster, Kyle Chard

CoRR, May, 2025

Training Data, Models, and Moldens for CEONet.

King Daniel, Daniel Grzenda, Ruijie Zhu, Nathaniel Hudson, Ian T. Foster, Bingqing Cheng, Laura Gagliardi

Dataset, April, 2025

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow.

Xiaoli Yan, Nathaniel Hudson, Hyun Park, Daniel Grzenda, J. Gregory Pauloski, Marcus Schwarting, Haochen Pan, Hassan Harb, Sam Foreman, Chris Knight, Tom Gibbs, Kyle Chard, Santanu Chaudhuri, Emad Tajkhorshid, Ian T. Foster, Mohamad Moosavi, Logan T. Ward, Eliu A. Huerta

CoRR, January, 2025

Causal Discovery over High-Dimensional Structured Hypothesis Spaces with Causal Graph Partitioning.

Ashka Shah, Adela Frances DePavia, Nathaniel C. Hudson, Ian T. Foster, Rick Stevens

Trans. Mach. Learn. Res., 2025

Mitigating Memorization in Language Models.

Mansi Sakarvadia, Aswathy Ajith, Arham Mushtaq Khan, Nathaniel C. Hudson, Caleb Geniesse, Kyle Chard, Yaoqing Yang, Ian T. Foster, Michael W. Mahoney

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Steering an Active Learning Workflow Towards Novel Materials Discovery via Queue Prioritization.

Marcus Schwarting, Logan T. Ward, Nathaniel Hudson, Xiaoli Yan, Ben Blaiszik, Santanu Chaudhuri, Eliu A. Huerta, Ian T. Foster

Proceedings of the IEEE International Conference on eScience, 2025

2024

Thinking in Categories: A Survey on Assessing the Quality for Time Series Synthesis.

Michael Stenger, André Bauer, Thomas Prantl, Robert Leppich, Nathaniel Hudson, Kyle Chard, Ian T. Foster, Samuel Kounev

ACM J. Data Inf. Qual., June, 2024

Deadline-Aware Task Offloading for Vehicular Edge Computing Networks Using Traffic Light Data.

Pratham Oza, Nathaniel Hudson, Thidapat Chantem, Hana Khamfroush

ACM Trans. Embed. Comput. Syst., January, 2024

QoS-aware edge AI placement and scheduling with multiple implementations in FaaS-based edge computing.

Nathaniel Hudson, Hana Khamfroush, Matt Baughman, Daniel E. Lucani, Kyle Chard, Ian T. Foster

Future Gener. Comput. Syst., 2024

SoK: On Finding Common Ground in Loss Landscapes Using Deep Model Merging Techniques.

Arham Khan, Todd Nief, Nathaniel Hudson, Mansi Sakarvadia, Daniel Grzenda, Aswathy Ajith, Jordan Pettyjohn, Kyle Chard, Ian T. Foster

CoRR, 2024

TaPS: A Performance Evaluation Suite for Task-based Execution Frameworks.

J. Gregory Pauloski, Valérie Hayot-Sasson, Maxime Gonthier, Nathaniel Hudson, Haochen Pan, Sicheng Zhou, Ian T. Foster, Kyle Chard

Proceedings of the 20th IEEE International Conference on e-Science, 2024

An Empirical Investigation of Container Building Strategies and Warm Times to Reduce Cold Starts in Scientific Computing Serverless Functions.

André Bauer, Maxime Gonthier, Haochen Pan, Ryan Chard, Daniel Grzenda, Martin Straesser, J. Gregory Pauloski, Alok Kamatar, Matt Baughman, Nathaniel Hudson, Ian T. Foster, Kyle Chard

Proceedings of the 20th IEEE International Conference on e-Science, 2024

2023

Measurement and Applications: Exploring the Challenges and Opportunities of Hierarchical Federated Learning in Sensor Applications.

Melanie Po-Leen Ooi, Shaleeza Sohail, Victoria Guiying Huang, Nathaniel Hudson, Matt Baughman, Omer F. Rana, Annika Hinze, Kyle Chard, Ryan Chard, Ian T. Foster, Theodoros Spyridopoulos, Harshaan Nagra

IEEE Instrum. Meas. Mag., December, 2023

Attention Lens: A Tool for Mechanistically Interpreting the Attention Head Information Retrieval Mechanism.

Mansi Sakarvadia, Arham Khan, Aswathy Ajith, Daniel Grzenda, Nathaniel Hudson, André Bauer, Kyle Chard, Ian T. Foster

CoRR, 2023

Hierarchical and Decentralised Federated Learning.

Omer F. Rana, Theodoros Spyridopoulos, Nathaniel Hudson, Matt Baughman, Kyle Chard, Ian T. Foster, Aftab Khan

CoRR, 2023

Searching for the Ground Truth: Assessing the Similarity of Benchmarking Runs.

André Bauer, Martin Straesser, Mark Leznik, Lukas Beierlieb, Marius Hadry, Nathaniel Hudson, Kyle Chard, Samuel Kounev, Ian T. Foster

Proceedings of the Companion of the 2023 ACM/SPEC International Conference on Performance Engineering, 2023

Adversarial Predictions of Data Distributions Across Federated Internet-of-Things Devices.

Samir Rajani, Dario Dematties, Nathaniel Hudson, Kyle Chard, Nicola J. Ferrier, Rajesh Sankaran, Peter H. Beckman

Proceedings of the 9th IEEE World Forum on Internet of Things, 2023

Accelerating Communications in Federated Applications with Transparent Object Proxies.

J. Gregory Pauloski, Valérie Hayot-Sasson, Logan T. Ward, Nathaniel Hudson, Charlie Sabino, Matt Baughman, Kyle Chard, Ian T. Foster

Proceedings of the International Conference for High Performance Computing, 2023

Tournament-Based Pretraining to Accelerate Federated Learning.

Matt Baughman, Nathaniel Hudson, Ryan Chard, André Bauer, Ian T. Foster, Kyle Chard

Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, 2023

Balancing Federated Learning Trade-Offs for Heterogeneous Environments.

Matt Baughman, Nathaniel Hudson, Ian T. Foster, Kyle Chard

Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, 2023

Memory Injections: Correcting Multi-Hop Reasoning Failures During Inference in Transformer-Based Language Models.

Mansi Sakarvadia, Aswathy Ajith, Arham Khan, Daniel Grzenda, Nathaniel Hudson, André Bauer, Kyle Chard, Ian T. Foster

Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, 2023

Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision.

Nathaniel Hudson, J. Gregory Pauloski, Matt Baughman, Alok Kamatar, Mansi Sakarvadia, Logan T. Ward, Ryan Chard, André Bauer, Maksim Levental, Wenyi Wang, Will Engler, Owen Price Skelly, Ben Blaiszik, Rick Stevens, Kyle Chard, Ian T. Foster

Proceedings of the IEEE/ACM 10th International Conference on Big Data Computing, 2023

2022

Smart Edge-Enabled Traffic Light Control: Improving Reward-Communication Trade-offs with Federated Reinforcement Learning.

Nathaniel Hudson, Pratham Oza, Hana Khamfroush, Thidapat Chantem

Proceedings of the 2022 IEEE International Conference on Smart Computing, 2022

FLoX: Federated Learning with FaaS at the Edge.

Nikita Kotsehub, Matt Baughman, Ryan Chard, Nathaniel Hudson, Panos Patros, Omer F. Rana, Ian T. Foster, Kyle Chard

Proceedings of the 18th IEEE International Conference on e-Science, 2022

Communication-Loss Trade-Off in Federated Learning: A Distributed Client Selection Algorithm.

Minoo Hosseinzadeh, Nathaniel Hudson, Sam Heshmati, Hana Khamfroush

Proceedings of the 19th IEEE Annual Consumer Communications & Networking Conference, 2022

2021

Behavioral Information Diffusion for Opinion Maximization in Online Social Networks.

Nathaniel Hudson, Hana Khamfroush

IEEE Trans. Netw. Sci. Eng., 2021

QoS-Aware Placement of Deep Learning Services on the Edge with Multiple Service Implementations.

Nathaniel Hudson, Hana Khamfroush, Daniel E. Lucani

Proceedings of the 30th International Conference on Computer Communications and Networks, 2021

A Framework for Edge Intelligent Smart Distribution Grids via Federated Learning.

Nathaniel Hudson, Md. Jakir Hossain, Minoo Hosseinzadeh, Hana Khamfroush, Mahshid Rahnamay-Naeini, Nasir Ghani

Proceedings of the 30th International Conference on Computer Communications and Networks, 2021

Joint Compression and Offloading Decisions for Deep Learning Services in 3-Tier Edge Systems.

Minoo Hosseinzadeh, Nathaniel Hudson, Xiaobo Zhao, Hana Khamfroush, Daniel E. Lucani

Proceedings of the IEEE International Symposium on Dynamic Spectrum Access Networks, 2021

2020

Smart Advertisement for Maximal Clicks in Online Social Networks Without User Data.

Nathaniel Hudson, Hana Khamfroush, Brent Harrison, Adam Craig

Proceedings of the IEEE International Conference on Smart Computing, 2020

A Proximity-Based Generative Model for Online Social Network Topologies.

Emory Hufbauer, Nathaniel Hudson, Hana Khamfroush

Proceedings of the International Conference on Computing, Networking and Communications, 2020

Improving the Accuracy-Latency Trade-off of Edge-Cloud Computation Offloading for Deep Learning Services.

Xiaobo Zhao, Minoo Hosseinzadeh, Nathaniel Hudson, Hana Khamfroush, Daniel E. Lucani

Proceedings of the IEEE Globecom Workshops, 2020

2019

Click Maximization in Online Social Networks Using Optimal Choice of Targeted Interests.

Nathaniel Hudson, Hana Khamfroush, Brent Harrison, Adam Craig

CoRR, 2019

Influence spread in two-layer interdependent networks: designed single-layer or random two-layer initial spreaders?

Hana Khamfroush, Nathaniel Hudson, Samuel Lofumbwa Iloo, Mahshid Rahnamay-Naeini

Appl. Netw. Sci., 2019

On the Effectiveness of Standard Centrality Metrics for Interdependent Networks.

Nathaniel Hudson, Matthew Turner, Asare Nkansah, Hana Khamfroush

Proceedings of the International Conference on Computing, Networking and Communications, 2019