Matt Baughman

According to our database, Matt Baughman authored at least 20 papers between 2018 and 2023.

Collaborative distances:

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

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

Rural AI: Serverless-Powered Federated Learning for Remote Applications.

Panos Patros, Melanie Ooi, Victoria Huang, Michael Mayo, Chris Anderson, Stephen Burroughs, Matt Baughman, Osama Almurshed, Omer F. Rana, Ryan Chard, Kyle Chard, Ian T. Foster

IEEE Internet Comput., 2023

Hierarchical and Decentralised Federated Learning.

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

CoRR, 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

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

Assessing the Current State of AWS Spot Market Forecastability.

Simon Caton, Matt Baughman, Christian Haas, Ryan Chard, Ian T. Foster, Kyle Chard

Proceedings of the IEEE/ACM International Workshop on Interoperability of Supercomputing and Cloud Technologies, 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

Exploring Tradeoffs in Federated Learning on Serverless Computing Architectures.

Matt Baughman, Ian T. Foster, Kyle Chard

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

Adaptive Edge-Cloud Environments for Rural AI.

Osama Almurshed, Panos Patros, Victoria Huang, Michael Mayo, Melanie Ooi, Ryan Chard, Kyle Chard, Omer F. Rana, Harshaan Nagra, Matt Baughman, Ian T. Foster

Proceedings of the IEEE International Conference on Services Computing, 2022

2021

Coding the Computing Continuum: Fluid Function Execution in Heterogeneous Computing Environments.

Rohan Kumar, Matt Baughman, Ryan Chard, Zhuozhao Li, Yadu N. Babuji, Ian T. Foster, Kyle Chard

Proceedings of the IEEE International Parallel and Distributed Processing Symposium Workshops, 2021

Expanding Cost-Aware Function Execution with Multidimensional Notions of Cost.

Matt Baughman, Rohan Kumar, Ian T. Foster, Kyle Chard

Proceedings of the HiPS@HPDC 2021: Proceedings of the 1st Workshop on High Performance Serverless Computing, 2021

Enhancing Automated FaaS with Cost-aware Provisioning of Cloud Resources.

Matt Baughman, Ian T. Foster, Kyle Chard

Proceedings of the 17th IEEE International Conference on eScience, 2021

2019

Deconstructing the 2017 Changes to AWS Spot Market Pricing.

Matt Baughman, Simon Caton, Christian Haas, Ryan Chard, Rich Wolski, Ian T. Foster, Kyle Chard

Proceedings of the 10th Workshop on Scientific Cloud Computing, 2019

ParaOpt: Automated Application Parameterization and Optimization for the Cloud.

Chaofeng Wu, Ian T. Foster, Ted Summer, Zhuozhao Li, Anna Woodard, Ryan Chard, Matt Baughman, Yadu N. Babuji, Kyle Chard, Jason Pitt

Proceedings of the 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), 2019

Measuring, Quantifying, and Predicting the Cost-Accuracy Tradeoff.

Matt Baughman, Nifesh Chakubaji, Hong-Linh Truong, Krists Kreics, Kyle Chard, Ian T. Foster

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2018

CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research.

Justin M. Wozniak, Rajeev Jain, Prasanna Balaprakash, Jonathan Ozik, Nicholson T. Collier, John Bauer, Fangfang Xia, Thomas S. Brettin, Rick Stevens, Jamaludin Mohd-Yusof, Cristina Garcia-Cardona, Brian Van Essen, Matthew Baughman

BMC Bioinform., 2018

Profiling and Predicting Application Performance on the Cloud.

Matt Baughman, Ryan Chard, Logan T. Ward, Jason Pitt, Kyle Chard, Ian T. Foster

Proceedings of the 11th IEEE/ACM International Conference on Utility and Cloud Computing, 2018

Predicting Amazon Spot Prices with LSTM Networks.

Matt Baughman, Christian Haas, Rich Wolski, Ian T. Foster, Kyle Chard

Proceedings of the 9th Workshop on Scientific Cloud Computing, 2018