On the Effectiveness of Standard Centrality Metrics for Interdependent Networks. - Nathaniel Hudson 001; Matthew Turner; Asare Nkansah; Hana Khamfroush (ICNC, 2019)

Influence spread in two-layer interdependent networks: designed single-layer or random two-layer initial spreaders? - Hana Khamfroush; Nathaniel Hudson 001; Samuel Lofumbwa Iloo; Mahshid Rahnamay-Naeini (Appl. Netw. Sci., 2019)

Click Maximization in Online Social Networks Using Optimal Choice of Targeted Interests. - Nathaniel Hudson 001; Hana Khamfroush; Brent Harrison; Adam Craig (CoRR, 2019)

Improving the Accuracy-Latency Trade-off of Edge-Cloud Computation Offloading for Deep Learning Services. - Xiaobo Zhao; Minoo Hosseinzadeh; Nathaniel Hudson 001; Hana Khamfroush; Daniel E. Lucani (GLOBECOM (Workshops), 2020)

A Proximity-Based Generative Model for Online Social Network Topologies. - Emory Hufbauer; Nathaniel Hudson 001; Hana Khamfroush (ICNC, 2020)

Smart Advertisement for Maximal Clicks in Online Social Networks Without User Data. - Nathaniel Hudson 001; Hana Khamfroush; Brent Harrison; Adam Craig (SMARTCOMP, 2020)

Joint Compression and Offloading Decisions for Deep Learning Services in 3-Tier Edge Systems. - Minoo Hosseinzadeh; Nathaniel Hudson 001; Xiaobo Zhao; Hana Khamfroush; Daniel E. Lucani (DySPAN, 2021)

A Framework for Edge Intelligent Smart Distribution Grids via Federated Learning. - Nathaniel Hudson 001; Md. Jakir Hossain; Minoo Hosseinzadeh; Hana Khamfroush; Mahshid Rahnamay-Naeini; Nasir Ghani (ICCCN, 2021)

QoS-Aware Placement of Deep Learning Services on the Edge with Multiple Service Implementations. - Nathaniel Hudson 001; Hana Khamfroush; Daniel E. Lucani (ICCCN, 2021)

Behavioral Information Diffusion for Opinion Maximization in Online Social Networks. - Nathaniel Hudson 001; Hana Khamfroush (IEEE Trans. Netw. Sci. Eng., 2021)

Communication-Loss Trade-Off in Federated Learning: A Distributed Client Selection Algorithm. - Minoo Hosseinzadeh; Nathaniel Hudson 001; Sam Heshmati; Hana Khamfroush (CCNC, 2022)

FLoX: Federated Learning with FaaS at the Edge. - Nikita Kotsehub; Matt Baughman; Ryan Chard; Nathaniel Hudson 001; Panos Patros; Omer F. Rana; Ian T. Foster; Kyle Chard (e-Science, 2022)

Smart Edge-Enabled Traffic Light Control: Improving Reward-Communication Trade-offs with Federated Reinforcement Learning. - Nathaniel Hudson 001; Pratham Oza; Hana Khamfroush; Thidapat Chantem (SMARTCOMP, 2022)

Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision. - Nathaniel Hudson 001; J. Gregory Pauloski; Matt Baughman; Alok Kamatar; Mansi Sakarvadia; Logan T. Ward; Ryan Chard; André Bauer 001; Maksim Levental; Wenyi Wang; Will Engler; Owen Price Skelly; et al. (BDCAT, 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 001; André Bauer 001; Kyle Chard; Ian T. Foster (BlackboxNLP@EMNLP, 2023)

Balancing Federated Learning Trade-Offs for Heterogeneous Environments. - Matt Baughman; Nathaniel Hudson 001; Ian T. Foster; Kyle Chard (PerCom Workshops, 2023)

Tournament-Based Pretraining to Accelerate Federated Learning. - Matt Baughman; Nathaniel Hudson 001; Ryan Chard; André Bauer 001; Ian T. Foster; Kyle Chard (SC Workshops, 2023)

Accelerating Communications in Federated Applications with Transparent Object Proxies. - J. Gregory Pauloski; Valérie Hayot-Sasson; Logan T. Ward; Nathaniel Hudson 001; Charlie Sabino; Matt Baughman; Kyle Chard; Ian T. Foster (SC, 2023)

Adversarial Predictions of Data Distributions Across Federated Internet-of-Things Devices. - Samir Rajani; Dario Dematties; Nathaniel Hudson 001; Kyle Chard; Nicola J. Ferrier; Rajesh Sankaran; Peter H. Beckman (WF-IoT, 2023)

Searching for the Ground Truth: Assessing the Similarity of Benchmarking Runs. - André Bauer 001; Martin Straesser; Mark Leznik; Lukas Beierlieb; Marius Hadry; Nathaniel Hudson 001; Kyle Chard; Samuel Kounev; Ian T. Foster (ICPE (Companion), 2023)

Hierarchical and Decentralised Federated Learning. - Omer F. Rana; Theodoros Spyridopoulos; Nathaniel Hudson 001; Matt Baughman; Kyle Chard; Ian T. Foster; Aftab Khan 001 (CoRR, 2023)

Attention Lens: A Tool for Mechanistically Interpreting the Attention Head Information Retrieval Mechanism. - Mansi Sakarvadia; Arham Khan; Aswathy Ajith; Daniel Grzenda; Nathaniel Hudson 001; André Bauer 001; Kyle Chard; Ian T. Foster (CoRR, 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 001; Matt Baughman; Omer F. Rana; Annika Hinze; Kyle Chard; Ryan Chard; Ian T. Foster; Theodoros Spyridopoulos; Harshaan Nagra (IEEE Instrum. Meas. Mag., 2023)

An Empirical Investigation of Container Building Strategies and Warm Times to Reduce Cold Starts in Scientific Computing Serverless Functions. - André Bauer 001; Maxime Gonthier; Haochen Pan; Ryan Chard; Daniel Grzenda; Martin Straesser; J. Gregory Pauloski; Alok Kamatar; Matt Baughman; Nathaniel Hudson 001; Ian T. Foster; Kyle Chard (e-Science, 2024)

TaPS: A Performance Evaluation Suite for Task-based Execution Frameworks. - J. Gregory Pauloski; Valérie Hayot-Sasson; Maxime Gonthier; Nathaniel Hudson 001; Haochen Pan; Sicheng Zhou; Ian T. Foster; Kyle Chard (e-Science, 2024)

SoK: On Finding Common Ground in Loss Landscapes Using Deep Model Merging Techniques. - Arham Khan; Todd Nief; Nathaniel Hudson 001; Mansi Sakarvadia; Daniel Grzenda; Aswathy Ajith; Jordan Pettyjohn; Kyle Chard; Ian T. Foster (CoRR, 2024)

QoS-aware edge AI placement and scheduling with multiple implementations in FaaS-based edge computing. - Nathaniel Hudson 001; Hana Khamfroush; Matt Baughman; Daniel E. Lucani; Kyle Chard; Ian T. Foster (Future Gener. Comput. Syst., 2024)

Deadline-Aware Task Offloading for Vehicular Edge Computing Networks Using Traffic Light Data. - Pratham Oza; Nathaniel Hudson 001; Thidapat Chantem; Hana Khamfroush (ACM Trans. Embed. Comput. Syst., 2024)

Thinking in Categories: A Survey on Assessing the Quality for Time Series Synthesis. - Michael Stenger; André Bauer 001; Thomas Prantl; Robert Leppich; Nathaniel Hudson 001; Kyle Chard; Ian T. Foster; Samuel Kounev (ACM J. Data Inf. Qual., 2024)

Steering an Active Learning Workflow Towards Novel Materials Discovery via Queue Prioritization. - Marcus Schwarting; Logan T. Ward; Nathaniel Hudson 001; Xiaoli Yan; Ben Blaiszik; Santanu Chaudhuri; Eliu A. Huerta; Ian T. Foster (eScience, 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 (ICLR, 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)

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow. - Xiaoli Yan; Nathaniel Hudson 001; Hyun Park; Daniel Grzenda; J. Gregory Pauloski; Marcus Schwarting; Haochen Pan; Hassan Harb; Sam Foreman; Chris Knight 001; Tom Gibbs; Kyle Chard; et al. (CoRR, 2025)

Training Data, Models, and Moldens for CEONet. - King Daniel; Daniel Grzenda; Ruijie Zhu 004; Nathaniel Hudson 001; Ian T. Foster; Bingqing Cheng; Laura Gagliardi (Zenodo, 2025)

Topology-Aware Knowledge Propagation in Decentralized Learning. - Mansi Sakarvadia; Nathaniel Hudson 001; Tian Li; Ian T. Foster; Kyle Chard (CoRR, 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 001; Dante D. Sánchez-Gallegos; Ian T. Foster; Jonathan Ozik; et al. (CoRR, 2025)

Addressing Reproducibility Challenges in HPC with Continuous Integration. - Valérie Hayot-Sasson; Nathaniel Hudson 001; André Bauer 001; Maxime Gonthier; Ian T. Foster; Kyle Chard (CoRR, 2025)

Flight: A FaaS-based framework for complex and Hierarchical Federated Learning. - Nathaniel Hudson 001; Valérie Hayot-Sasson; Yadu Nand Babuji; Matt Baughman; J. Gregory Pauloski; Ryan Chard; Ian T. Foster; Kyle Chard (Future Gener. Comput. Syst., 2026)