Predicting Amazon Spot Prices with LSTM Networks. - Matt Baughman; Christian Haas 003; Rich Wolski; Ian T. Foster; Kyle Chard (ScienceCloud@HPDC, 2018)

Profiling and Predicting Application Performance on the Cloud. - Matt Baughman; Ryan Chard; Logan T. Ward; Jason Pitt; Kyle Chard; Ian T. Foster (UCC, 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; et al. (BMC Bioinform., 2018)

Measuring, Quantifying, and Predicting the Cost-Accuracy Tradeoff. - Matt Baughman; Nifesh Chakubaji; Hong-Linh Truong 001; Krists Kreics; Kyle Chard; Ian T. Foster (IEEE BigData, 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 (CloudCom, 2019)

Deconstructing the 2017 Changes to AWS Spot Market Pricing. - Matt Baughman; Simon Caton; Christian Haas 003; Ryan Chard; Rich Wolski; Ian T. Foster; Kyle Chard (ScienceCloud@HPDC, 2019)

Enhancing Automated FaaS with Cost-aware Provisioning of Cloud Resources. - Matt Baughman; Ian T. Foster; Kyle Chard (e-Science, 2021)

Expanding Cost-Aware Function Execution with Multidimensional Notions of Cost. - Matt Baughman; Rohan Kumar; Ian T. Foster; Kyle Chard (HiPS@HPDC, 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 (IPDPS Workshops, 2021)

Adaptive Edge-Cloud Environments for Rural AI. - Osama Almurshed; Panos Patros; Victoria Huang 001; Michael Mayo; Melanie Ooi; Ryan Chard; Kyle Chard; Omer F. Rana; Harshaan Nagra; Matt Baughman; Ian T. Foster (SCC, 2022)

Exploring Tradeoffs in Federated Learning on Serverless Computing Architectures. - Matt Baughman; Ian T. Foster; Kyle Chard (e-Science, 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 (e-Science, 2022)

Assessing the Current State of AWS Spot Market Forecastability. - Simon Caton; Matt Baughman; Christian Haas 003; Ryan Chard; Ian T. Foster; Kyle Chard (SuperCompCloud, 2022)

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 001; Maksim Levental; Wenyi Wang; Will Engler; Owen Price Skelly; et al. (BDCAT, 2023)

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

Tournament-Based Pretraining to Accelerate Federated Learning. - Matt Baughman; Nathaniel Hudson; 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; Charlie Sabino; Matt Baughman; Kyle Chard; Ian T. Foster (SC, 2023)

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

Rural AI: Serverless-Powered Federated Learning for Remote Applications. - Panos Patros; Melanie Ooi; Victoria Huang 001; Michael Mayo; Chris Anderson; Stephen Burroughs; Matt Baughman; Osama Almurshed; Omer F. Rana; Ryan Chard; Kyle Chard; Ian T. Foster (IEEE Internet Comput., 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., 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; Ian T. Foster; Kyle Chard (e-Science, 2024)

Unveiling Temporal Performance Deviation: Leveraging Clustering in Microservices Performance Analysis. - André Bauer 001; Timo Dittus; Martin Straesser; Alok Kamatar; Matt Baughman; Lukas Beierlieb; Marius Hadry; Daniel Grillmeyer; Yannik Lubas; Samuel Kounev; Ian T. Foster; Kyle Chard (ICPE (Companion), 2024)

Enabling Remote Management of FaaS Endpoints with Globus Compute Multi-User Endpoints. - Rachana Ananthakrishnan; Yadu N. Babuji; Matt Baughman; Josh Bryan; Kyle Chard; Ryan Chard; Ben Clifford; Ian T. Foster; Daniel S. Katz; Kevin Hunter Kesling; Chris Janidlo; Reid Mello; et al. (PEARC, 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)

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)