Matt Baughman
Postdoctoral Computational Scientist // Princeton University & PPPL
Hi there! I recently started a postdoc at Princeton working within the DOE's Princeton Plasma Physics Laboratory as an Associate Computational Scientist. At PPPL, I work in the AI4Science group with Shantenu Jha. Prior to Princeton, I completed my Ph.D. in Computer Science with Globus Labs at the University of Chicago in August 2025. I was advised by Ian Foster and Kyle Chard. My research spans high-performance computing, distributed systems, and cost-aware computing. I completed my Bachelor's in Computer Science and Philosophy at Minerva University and have experience at Argonne National Laboratory, Opinary, and Google.
science RESEARCH link
star SELECTED PUBLICATIONS link
Ordered by most recent.
| Flight: A FaaS-based framework for complex and Hierarchical Federated Learning [Jan 2026] |
| Nathaniel Hudson, Valérie Hayot-Sasson, Yadu Nand Babuji, Matt Baughman, J. Gregory Pauloski, Ryan Chard, Ian T. Foster, Kyle Chard |
| Future Generation Computer Systems |
| TLDR | Publication | BibTeX |
|
TLDR: Flight is a federated learning framework built on function-as-a-service that supports complex hierarchical and asynchronous topologies, decoupling FL logic from infrastructure so the same workflow runs from edge devices to HPC.
|
@article{hudson2026flight,
title = {Flight: {A} FaaS-based framework for complex and Hierarchical Federated Learning},
author = {Nathaniel Hudson and Val{\'{e}}rie Hayot{-}Sasson and Yadu Nand Babuji and Matt Baughman and J. Gregory Pauloski and Ryan Chard and Ian T. Foster and Kyle Chard},
doi = {10.1016/J.FUTURE.2025.107998},
journal = {Future Gener. Comput. Syst.},
pages = {107998},
url = {https://doi.org/10.1016/j.future.2025.107998},
volume = {174},
year = {2026}
}
|
| Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision [Dec 2023] |
| 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 |
| BDCAT 2023 |
| TLDR | Publication | BibTeX |
|
TLDR: We survey the infrastructure required to serve trillion-parameter AI models for science and lay out a vision for integrating such models with HPC facilities.
|
@inproceedings{hudson2023trillion,
title = {Trillion Parameter {AI} Serving Infrastructure for Scientific Discovery: {A} Survey and Vision},
author = {Nathaniel Hudson and J. Gregory Pauloski and Matt Baughman and Alok Kamatar and Mansi Sakarvadia and Logan T. Ward and Ryan Chard and Andr{\'{e}} Bauer and Maksim Levental and Wenyi Wang and Will Engler and Owen Price Skelly and Ben Blaiszik and Rick Stevens and Kyle Chard and Ian T. Foster},
booktitle = {Proceedings of the {IEEE/ACM} 10th International Conference on Big Data Computing, Applications and Technologies, {BDCAT} 2023, Taormina (Messina), Italy, December 4-7, 2023},
doi = {10.1145/3632366.3632396},
pages = {15:1--15:10},
publisher = {{ACM}},
url = {https://doi.org/10.1145/3632366.3632396},
year = {2023}
}
|
| Accelerating Communications in Federated Applications with Transparent Object Proxies [Nov 2023] |
| J. Gregory Pauloski, Valérie Hayot-Sasson, Logan T. Ward, Nathaniel Hudson, Charlie Sabino, Matt Baughman, Kyle Chard, Ian T. Foster |
| SC 2023 |
| TLDR | Publication | BibTeX |
|
TLDR: ProxyStore introduces transparent object proxies that decouple bulk data movement from control flow in federated applications, accelerating communications across distributed executors.
|
@inproceedings{pauloski2023proxystore,
title = {Accelerating Communications in Federated Applications with Transparent Object Proxies},
author = {J. Gregory Pauloski and Val{\'{e}}rie Hayot{-}Sasson and Logan T. Ward and Nathaniel Hudson and Charlie Sabino and Matt Baughman and Kyle Chard and Ian T. Foster},
booktitle = {Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, {SC} 2023, Denver, CO, USA, November 12-17, 2023},
doi = {10.1145/3581784.3607047},
pages = {59:1--59:15},
publisher = {{ACM}},
url = {https://doi.org/10.1145/3581784.3607047},
year = {2023}
}
|
| Rural AI: Serverless-Powered Federated Learning for Remote Applications [Mar 2023] |
| 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 Computing |
| TLDR | Publication | BibTeX |
|
TLDR: We make the case for serverless-powered federated learning to bring AI to remote and rural applications, outlining an architecture and open challenges for intermittently connected environments.
|
@article{patros2023ruralai,
title = {Rural {AI:} Serverless-Powered Federated Learning for Remote Applications},
author = {Panos Patros and Melanie Ooi and Victoria Huang and Michael Mayo and Chris Anderson and Stephen Burroughs and Matt Baughman and Osama Almurshed and Omer F. Rana and Ryan Chard and Kyle Chard and Ian T. Foster},
doi = {10.1109/MIC.2022.3202764},
journal = {{IEEE} Internet Comput.},
number = {2},
pages = {28--34},
url = {https://doi.org/10.1109/MIC.2022.3202764},
volume = {27},
year = {2023}
}
|
| Balancing Federated Learning Trade-Offs for Heterogeneous Environments [Mar 2023] |
| Matt Baughman, Nathaniel Hudson, Ian T. Foster, Kyle Chard |
| PerCom Workshops 2023 |
| TLDR | PDF | Publication | BibTeX |
|
TLDR: We characterize the accuracy, cost, and time trade-offs inherent to federated learning across heterogeneous devices and show how informed configuration improves outcomes.
|
@inproceedings{baughman2023balancing,
title = {Balancing Federated Learning Trade-Offs for Heterogeneous Environments},
author = {Matt Baughman and Nathaniel Hudson and Ian T. Foster and Kyle Chard},
booktitle = {{IEEE} International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023, Atlanta, GA, USA, March 13-17, 2023},
doi = {10.1109/PERCOMWORKSHOPS56833.2023.10150228},
pages = {404--407},
publisher = {{IEEE}},
url = {https://doi.org/10.1109/PerComWorkshops56833.2023.10150228},
year = {2023}
}
|
| FLoX: Federated Learning with FaaS at the Edge [Oct 2022] |
| Nikita Kotsehub, Matt Baughman, Ryan Chard, Nathaniel Hudson, Panos Patros, Omer F. Rana, Ian T. Foster, Kyle Chard |
| eScience 2022 |
| TLDR | Publication | BibTeX |
|
TLDR: FLoX runs federated learning on serverless (FaaS) infrastructure at the edge, decoupling model training and inference from deployment so FL can run on heterogeneous real-world devices.
|
@inproceedings{kotsehub2022flox,
title = {FLoX: Federated Learning with FaaS at the Edge},
author = {Nikita Kotsehub and Matt Baughman and Ryan Chard and Nathaniel Hudson and Panos Patros and Omer F. Rana and Ian T. Foster and Kyle Chard},
booktitle = {18th {IEEE} International Conference on e-Science, e-Science 2022, Salt Lake City, UT, USA, October 11-14, 2022},
doi = {10.1109/ESCIENCE55777.2022.00016},
pages = {11--20},
publisher = {{IEEE}},
url = {https://doi.org/10.1109/eScience55777.2022.00016},
year = {2022}
}
|
book THESES link
| Corralling the Computing Continuum: Enabling Multi-System Workflows with Serverless Computing [Aug 2025] link |
| Abstract | Committee | PDF | Slides | Doctoral Dissertation |
|
ABSTRACT: The computing continuum describes the convergence of global compute infrastructure as network bandwidths increase. To mobilize that infrastructure, we need to create a system that ties these diverse resources together—we need to corral the computing continuum. This effort began with task-wise solutions, addressing different components of task placement—profiling, predicting, and provisioning. These individual solutions enabled an early system that took into account compute costs, workload execution profiles, and the ability to move compute between systems. We combined and extended these works into a more robust task scheduling system called DELTA and its successor DELTA+. These systems incorporated notions of task execution time, data transfer costs, and machine performance but could not be used on batch scheduled systems or in multi-node environments. While compute is the currency of the future, there is no unified way to access that currency. To fill this gap, we introduce Adaptive Task Management (ATM)—a framework that acts as a multi-system task manager, mapping tasks to the many resources that comprise the continuum. ATM is designed on top of the Globus Compute framework, using existing infrastructure from edge devices to batch-scheduled HPC systems. ATM includes a novel placement algorithm and novel monitoring and task management systems designed to accommodate both large batches of tasks as well as more complex DAG-based workflows. To ground and evaluate the development of these frameworks, we explore the application of cost-aware principles in federated learning, material design and protein docking science applications, and in the performance optimization of serverless computing benchmarks.
|
|
Committee: Kyle Chard, Ian Foster, and Omer Rana (Cardiff University)
|
| Profiling, Predicting, and Provisioning: Enabling Cost-Aware Computation for the Cloud and Modern Heterogeneous Environments [Aug 2021] link |
| Abstract | Committee | PDF | Slides | Master's Thesis |
|
ABSTRACT: The growing prevalence of cloud resources and specialized hardware in the form of GPUs, ASICs, and IoT devices requires increasingly efficient and intentional use of these resources. Moreover, the complexity of choice presented by these diverse resources creates an optimization problem largely intractable to manual control. Therefore, modern computation in heterogeneous environments must be executed in a cost-aware, automated fashion. This control system can be decomposed into three discrete tasks: profiling, prediction, and provisioning. We profile the execution characteristics of a range of workloads on a range of hardware. Given those characteristics, we optimize our choice of resources for workload deployment based on predicted cost. Finally, we seamlessly provision any necessary resources and deploy the workload given the optimized choice of resource. In this thesis, we integrate several projects spanning the profiling, predicting, and provisioning cycle towards a unified system for the cost-aware distribution of workloads in dynamic, heterogeneous computing environments. Specifically, we develop a modular profiling system that characterizes the execution performance of scientific workflows deployed on cloud resources, employ statistical analyses and machine learning to predict the cost of using preemptible cloud resources, examine the role of computational tradeoffs in various workloads, and build on existing Function-as-a-Service (FaaS) frameworks to demonstrate a novel, cost-aware function distribution system. Through this work, we show the significant cost and time reductions for scientific workload execution, while enabling function-based distributed computing in a cost-aware heterogeneous environment.
|
|
Committee: Kyle Chard, Ian Foster, and Hank Hoffmann
|
article ALL PUBLICATIONS link
Ordered by most recent and grouped by topic. BibTeX file available for download here.
| Sep 2024 | An Empirical Investigation of Container Building Strategies and Warm Times to Reduce Cold Starts in Scientific Computing Serverless Functions link |
| TLDR | Authors | Publication | BibTeX | eScience 2024 | |
|
TLDR: We empirically evaluate container building strategies and warm times to reduce cold starts for scientific serverless functions, offering practical guidance for FaaS deployments.
|
|
@inproceedings{bauer2024containers,
title = {An Empirical Investigation of Container Building Strategies and Warm Times to Reduce Cold Starts in Scientific Computing Serverless Functions},
author = {Andr{\'{e}} Bauer and Maxime Gonthier and Haochen Pan and Ryan Chard and Daniel Grzenda and Martin Straesser and J. Gregory Pauloski and Alok Kamatar and Matt Baughman and Nathaniel Hudson and Ian T. Foster and Kyle Chard},
booktitle = {20th {IEEE} International Conference on e-Science, e-Science 2024, Osaka, Japan, September 16-20, 2024},
doi = {10.1109/E-SCIENCE62913.2024.10678668},
pages = {1--10},
publisher = {{IEEE}},
url = {https://doi.org/10.1109/e-Science62913.2024.10678668},
year = {2024}
}
|
|
| Jul 2024 | Enabling Remote Management of FaaS Endpoints with Globus Compute Multi-User Endpoints link |
| TLDR | Authors | Publication | BibTeX | PEARC 2024 | |
|
TLDR: We introduce Globus Compute multi-user endpoints, enabling administrators to securely manage FaaS endpoints on shared HPC systems on behalf of many users.
|
|
@inproceedings{ananthakrishnan2024endpoints,
title = {Enabling Remote Management of FaaS Endpoints with Globus Compute Multi-User Endpoints},
author = {Rachana Ananthakrishnan and Yadu N. Babuji and Matt Baughman and Josh Bryan and Kyle Chard and Ryan Chard and Ben Clifford and Ian T. Foster and Daniel S. Katz and Kevin Hunter Kesling and Chris Janidlo and Reid Mello and Lei Wang},
booktitle = {Practice and Experience in Advanced Research Computing 2024: Human Powered Computing, {PEARC} 2024, Providence, RI, USA, July 21-25, 2024},
doi = {10.1145/3626203.3670612},
pages = {62:1--62:5},
publisher = {{ACM}},
url = {https://doi.org/10.1145/3626203.3670612},
year = {2024}
}
|
|
| May 2024 | Unveiling Temporal Performance Deviation: Leveraging Clustering in Microservices Performance Analysis link |
| TLDR | Authors | Publication | BibTeX | ICPE 2024 Companion | |
|
TLDR: We use clustering over microservice performance data to unveil temporal performance deviations, helping operators detect when and where service behavior drifts.
|
|
@inproceedings{bauer2024deviation,
title = {Unveiling Temporal Performance Deviation: Leveraging Clustering in Microservices Performance Analysis},
author = {Andr{\'{e}} Bauer and Timo Dittus and Martin Straesser and Alok Kamatar and Matt Baughman and Lukas Beierlieb and Marius Hadry and Daniel Grillmeyer and Yannik Lubas and Samuel Kounev and Ian T. Foster and Kyle Chard},
booktitle = {Companion of the 15th {ACM/SPEC} International Conference on Performance Engineering, {ICPE} 2024, London, United Kingdom, May 7-11, 2024},
doi = {10.1145/3629527.3651843},
pages = {72--76},
publisher = {{ACM}},
url = {https://doi.org/10.1145/3629527.3651843},
year = {2024}
}
|
|
| Dec 2023 | Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision link |
| TLDR | Authors | Publication | BibTeX | BDCAT 2023 | |
|
TLDR: We survey the infrastructure required to serve trillion-parameter AI models for science and lay out a vision for integrating such models with HPC facilities.
|
|
@inproceedings{hudson2023trillion,
title = {Trillion Parameter {AI} Serving Infrastructure for Scientific Discovery: {A} Survey and Vision},
author = {Nathaniel Hudson and J. Gregory Pauloski and Matt Baughman and Alok Kamatar and Mansi Sakarvadia and Logan T. Ward and Ryan Chard and Andr{\'{e}} Bauer and Maksim Levental and Wenyi Wang and Will Engler and Owen Price Skelly and Ben Blaiszik and Rick Stevens and Kyle Chard and Ian T. Foster},
booktitle = {Proceedings of the {IEEE/ACM} 10th International Conference on Big Data Computing, Applications and Technologies, {BDCAT} 2023, Taormina (Messina), Italy, December 4-7, 2023},
doi = {10.1145/3632366.3632396},
pages = {15:1--15:10},
publisher = {{ACM}},
url = {https://doi.org/10.1145/3632366.3632396},
year = {2023}
}
|
|
| Nov 2023 | Accelerating Communications in Federated Applications with Transparent Object Proxies link |
| TLDR | Authors | Publication | BibTeX | SC 2023 | |
|
TLDR: ProxyStore introduces transparent object proxies that decouple bulk data movement from control flow in federated applications, accelerating communications across distributed executors.
|
|
@inproceedings{pauloski2023proxystore,
title = {Accelerating Communications in Federated Applications with Transparent Object Proxies},
author = {J. Gregory Pauloski and Val{\'{e}}rie Hayot{-}Sasson and Logan T. Ward and Nathaniel Hudson and Charlie Sabino and Matt Baughman and Kyle Chard and Ian T. Foster},
booktitle = {Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, {SC} 2023, Denver, CO, USA, November 12-17, 2023},
doi = {10.1145/3581784.3607047},
pages = {59:1--59:15},
publisher = {{ACM}},
url = {https://doi.org/10.1145/3581784.3607047},
year = {2023}
}
|
|
| Nov 2022 | Assessing the Current State of AWS Spot Market Forecastability link |
| TLDR | Authors | Publication | BibTeX | SuperCompCloud 2022 | |
|
TLDR: We assess whether the post-2017 AWS spot market remains forecastable, evaluating prediction approaches on modern spot price data.
|
|
@inproceedings{caton2022spot,
title = {Assessing the Current State of {AWS} Spot Market Forecastability},
author = {Simon Caton and Matt Baughman and Christian Haas and Ryan Chard and Ian T. Foster and Kyle Chard},
booktitle = {{IEEE/ACM} International Workshop on Interoperability of Supercomputing and Cloud Technologies, SuperCompCloud@SC 2022, Dallas, TX, USA, November 13-18, 2022},
doi = {10.1109/SUPERCOMPCLOUD56703.2022.00007},
pages = {8--15},
publisher = {{IEEE}},
url = {https://doi.org/10.1109/SuperCompCloud56703.2022.00007},
year = {2022}
}
|
|
| Sep 2021 | Enhancing Automated FaaS with Cost-aware Provisioning of Cloud Resources link |
| TLDR | Authors | Publication | BibTeX | eScience 2021 | |
|
TLDR: We enhance automated FaaS with cost-aware provisioning that selects and provisions cloud resources based on predicted workload cost, reducing execution cost for scientific workloads.
|
|
@inproceedings{baughman2021faas,
title = {Enhancing Automated FaaS with Cost-aware Provisioning of Cloud Resources},
author = {Matt Baughman and Ian T. Foster and Kyle Chard},
booktitle = {17th {IEEE} International Conference on eScience, eScience 2021, Innsbruck, Austria, September 20-23, 2021},
doi = {10.1109/ESCIENCE51609.2021.00053},
pages = {267--268},
publisher = {{IEEE}},
url = {https://doi.org/10.1109/eScience51609.2021.00053},
year = {2021}
}
|
|
| Jun 2021 | Coding the Computing Continuum: Fluid Function Execution in Heterogeneous Computing Environments link |
| TLDR | Authors | Publication | BibTeX | IPDPSW 2021 | |
|
TLDR: We demonstrate fluid function execution across heterogeneous computing environments, letting functions move seamlessly between cloud, edge, and HPC systems.
|
|
@inproceedings{kumar2021continuum,
title = {Coding the Computing Continuum: Fluid Function Execution in Heterogeneous Computing Environments},
author = {Rohan Kumar and Matt Baughman and Ryan Chard and Zhuozhao Li and Yadu N. Babuji and Ian T. Foster and Kyle Chard},
booktitle = {{IEEE} International Parallel and Distributed Processing Symposium Workshops, {IPDPS} Workshops 2021, Portland, OR, USA, June 17-21, 2021},
doi = {10.1109/IPDPSW52791.2021.00018},
pages = {66--75},
publisher = {{IEEE}},
url = {https://doi.org/10.1109/IPDPSW52791.2021.00018},
year = {2021}
}
|
|
| Jun 2021 | Expanding Cost-Aware Function Execution with Multidimensional Notions of Cost link |
| TLDR | Authors | Publication | BibTeX | HiPS 2021 | |
|
TLDR: We expand cost-aware function execution beyond monetary cost to multidimensional notions of cost, such as execution time and resource constraints, for placement decisions.
|
|
@inproceedings{baughman2021cost,
title = {Expanding Cost-Aware Function Execution with Multidimensional Notions of Cost},
author = {Matt Baughman and Rohan Kumar and Ian T. Foster and Kyle Chard},
booktitle = {HiPS@HPDC 2021: Proceedings of the 1st Workshop on High Performance Serverless Computing, Virtual Event, Sweden, 25 June, 2021},
doi = {10.1145/3452413.3464790},
pages = {9--12},
publisher = {{ACM}},
url = {https://doi.org/10.1145/3452413.3464790},
year = {2021}
}
|
|
| Dec 2019 | Measuring, Quantifying, and Predicting the Cost-Accuracy Tradeoff link |
| TLDR | Authors | Publication | BibTeX | BPOD @ IEEE BigData 2019 | |
|
TLDR: We measure, quantify, and predict the cost-accuracy tradeoff in analytics workloads, enabling principled decisions about how much accuracy a dollar buys.
|
|
@inproceedings{baughman2019tradeoff,
title = {Measuring, Quantifying, and Predicting the Cost-Accuracy Tradeoff},
author = {Matt Baughman and Nifesh Chakubaji and Hong{-}Linh Truong and Krists Kreics and Kyle Chard and Ian T. Foster},
booktitle = {2019 {IEEE} International Conference on Big Data {(IEEE} BigData), Los Angeles, CA, USA, December 9-12, 2019},
doi = {10.1109/BIGDATA47090.2019.9006370},
pages = {3616--3622},
publisher = {{IEEE}},
url = {https://doi.org/10.1109/BigData47090.2019.9006370},
year = {2019}
}
|
|
| Dec 2019 | ParaOpt: Automated Application Parameterization and Optimization for the Cloud link |
| TLDR | Authors | Publication | BibTeX | CloudCom 2019 | |
|
TLDR: ParaOpt automatically parameterizes and optimizes applications for cloud deployment, searching configuration spaces to reduce cost and runtime.
|
|
@inproceedings{wu2019paraopt,
title = {ParaOpt: Automated Application Parameterization and Optimization for the Cloud},
author = {Chaofeng Wu and Ian T. Foster and Ted Summer and Zhuozhao Li and Anna Woodard and Ryan Chard and Matt Baughman and Yadu N. Babuji and Kyle Chard and Jason Pitt},
booktitle = {2019 {IEEE} International Conference on Cloud Computing Technology and Science (CloudCom), Sydney, Australia, December 11-13, 2019},
doi = {10.1109/CLOUDCOM.2019.00045},
pages = {255--262},
publisher = {{IEEE}},
url = {https://doi.org/10.1109/CloudCom.2019.00045},
year = {2019}
}
|
|
| Jun 2019 | Deconstructing the 2017 Changes to AWS Spot Market Pricing link |
| TLDR | Authors | Publication | BibTeX | ScienceCloud 2019 | |
|
TLDR: We deconstruct the 2017 changes to AWS spot market pricing, showing how the smoothed pricing regime alters preemption behavior and forecasting strategies.
|
|
@inproceedings{baughman2019spot,
title = {Deconstructing the 2017 Changes to {AWS} Spot Market Pricing},
author = {Matt Baughman and Simon Caton and Christian Haas and Ryan Chard and Rich Wolski and Ian T. Foster and Kyle Chard},
booktitle = {Proceedings of the 10th Workshop on Scientific Cloud Computing, ScienceCloud@HPDC 2019, Phoenix, AZ, USA, June 25, 2019},
doi = {10.1145/3322795.3331465},
pages = {19--26},
publisher = {{ACM}},
url = {https://doi.org/10.1145/3322795.3331465},
year = {2019}
}
|
|
| Dec 2018 | Profiling and Predicting Application Performance on the Cloud link |
| TLDR | Authors | Publication | BibTeX | UCC 2018 | |
|
TLDR: We profile applications and predict their performance across cloud resource types, supporting automated, cost-effective resource selection.
|
|
@inproceedings{baughman2018profiling,
title = {Profiling and Predicting Application Performance on the Cloud},
author = {Matt Baughman and Ryan Chard and Logan T. Ward and Jason Pitt and Kyle Chard and Ian T. Foster},
booktitle = {11th {IEEE/ACM} International Conference on Utility and Cloud Computing, {UCC} 2018, Zurich, Switzerland, December 17-20, 2018},
doi = {10.1109/UCC.2018.00011},
pages = {21--30},
publisher = {{IEEE} Computer Society},
url = {https://doi.org/10.1109/UCC.2018.00011},
year = {2018}
}
|
|
| Jun 2018 | Predicting Amazon Spot Prices with LSTM Networks link |
| TLDR | Authors | Publication | BibTeX | ScienceCloud 2018 | |
|
TLDR: We predict Amazon spot prices with LSTM networks, outperforming classical baselines and enabling cheaper cloud provisioning decisions.
|
|
@inproceedings{baughman2018lstm,
title = {Predicting Amazon Spot Prices with {LSTM} Networks},
author = {Matt Baughman and Christian Haas and Rich Wolski and Ian T. Foster and Kyle Chard},
booktitle = {Proceedings of the 9th Workshop on Scientific Cloud Computing, ScienceCloud@HPDC 2018, Tempe, AZ, USA, June 11, 2018},
doi = {10.1145/3217880.3217881},
pages = {1:1--1:7},
publisher = {{ACM}},
url = {https://doi.org/10.1145/3217880.3217881},
year = {2018}
}
|
| Jan 2026 | Flight: A FaaS-based framework for complex and Hierarchical Federated Learning link |
| TLDR | Authors | Publication | BibTeX | Future Generation Computer Systems | |
|
TLDR: Flight is a federated learning framework built on function-as-a-service that supports complex hierarchical and asynchronous topologies, decoupling FL logic from infrastructure so the same workflow runs from edge devices to HPC.
|
|
@article{hudson2026flight,
title = {Flight: {A} FaaS-based framework for complex and Hierarchical Federated Learning},
author = {Nathaniel Hudson and Val{\'{e}}rie Hayot{-}Sasson and Yadu Nand Babuji and Matt Baughman and J. Gregory Pauloski and Ryan Chard and Ian T. Foster and Kyle Chard},
doi = {10.1016/J.FUTURE.2025.107998},
journal = {Future Gener. Comput. Syst.},
pages = {107998},
url = {https://doi.org/10.1016/j.future.2025.107998},
volume = {174},
year = {2026}
}
|
|
| Aug 2024 | QoS-aware edge AI placement and scheduling with multiple implementations in FaaS-based edge computing link |
| TLDR | Authors | Publication | BibTeX | Future Generation Computer Systems | |
|
TLDR: We present QoS-aware placement and scheduling of edge AI tasks that selects among multiple model implementations in FaaS-based edge computing, trading accuracy and latency to meet quality-of-service goals.
|
|
@article{hudson2024qos,
title = {QoS-aware edge {AI} placement and scheduling with multiple implementations in FaaS-based edge computing},
author = {Nathaniel Hudson and Hana Khamfroush and Matt Baughman and Daniel E. Lucani and Kyle Chard and Ian T. Foster},
doi = {10.1016/J.FUTURE.2024.03.035},
journal = {Future Gener. Comput. Syst.},
pages = {250--263},
url = {https://doi.org/10.1016/j.future.2024.03.035},
volume = {157},
year = {2024}
}
|
|
| Dec 2023 | Measurement and Applications: Exploring the Challenges and Opportunities of Hierarchical Federated Learning in Sensor Applications link |
| TLDR | Authors | Publication | BibTeX | IEEE Instrumentation & Measurement Magazine | |
|
TLDR: We explore the challenges and opportunities of hierarchical federated learning for sensor applications from a measurement perspective.
|
|
@article{ooi2023measurement,
title = {Measurement and Applications: Exploring the Challenges and Opportunities of Hierarchical Federated Learning in Sensor Applications},
author = {Melanie Po{-}Leen Ooi and Shaleeza Sohail and Victoria Guiying Huang and Nathaniel Hudson and Matt Baughman and Omer F. Rana and Annika Hinze and Kyle Chard and Ryan Chard and Ian T. Foster and Theodoros Spyridopoulos and Harshaan Nagra},
doi = {10.1109/MIM.2023.10328671},
journal = {{IEEE} Instrum. Meas. Mag.},
number = {9},
pages = {21--31},
url = {https://doi.org/10.1109/MIM.2023.10328671},
volume = {26},
year = {2023}
}
|
|
| Nov 2023 | Tournament-Based Pretraining to Accelerate Federated Learning link |
| TLDR | PDF | Authors | Publication | BibTeX | SC 2023 Workshops | |
|
TLDR: We propose tournament-based pretraining, seeding federated learning with the best of independently pretrained candidate models to accelerate convergence.
|
|
@inproceedings{baughman2023tournament,
title = {Tournament-Based Pretraining to Accelerate Federated Learning},
author = {Matt Baughman and Nathaniel Hudson and Ryan Chard and Andr{\'{e}} Bauer and Ian T. Foster and Kyle Chard},
booktitle = {Proceedings of the {SC} '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, {SC-W} 2023, Denver, CO, USA, November 12-17, 2023},
doi = {10.1145/3624062.3626089},
pages = {109--115},
publisher = {{ACM}},
url = {https://doi.org/10.1145/3624062.3626089},
year = {2023}
}
|
|
| Mar 2023 | Rural AI: Serverless-Powered Federated Learning for Remote Applications link |
| TLDR | Authors | Publication | BibTeX | IEEE Internet Computing | |
|
TLDR: We make the case for serverless-powered federated learning to bring AI to remote and rural applications, outlining an architecture and open challenges for intermittently connected environments.
|
|
@article{patros2023ruralai,
title = {Rural {AI:} Serverless-Powered Federated Learning for Remote Applications},
author = {Panos Patros and Melanie Ooi and Victoria Huang and Michael Mayo and Chris Anderson and Stephen Burroughs and Matt Baughman and Osama Almurshed and Omer F. Rana and Ryan Chard and Kyle Chard and Ian T. Foster},
doi = {10.1109/MIC.2022.3202764},
journal = {{IEEE} Internet Comput.},
number = {2},
pages = {28--34},
url = {https://doi.org/10.1109/MIC.2022.3202764},
volume = {27},
year = {2023}
}
|
|
| Mar 2023 | Balancing Federated Learning Trade-Offs for Heterogeneous Environments link |
| TLDR | PDF | Authors | Publication | BibTeX | PerCom Workshops 2023 | |
|
TLDR: We characterize the accuracy, cost, and time trade-offs inherent to federated learning across heterogeneous devices and show how informed configuration improves outcomes.
|
|
@inproceedings{baughman2023balancing,
title = {Balancing Federated Learning Trade-Offs for Heterogeneous Environments},
author = {Matt Baughman and Nathaniel Hudson and Ian T. Foster and Kyle Chard},
booktitle = {{IEEE} International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023, Atlanta, GA, USA, March 13-17, 2023},
doi = {10.1109/PERCOMWORKSHOPS56833.2023.10150228},
pages = {404--407},
publisher = {{IEEE}},
url = {https://doi.org/10.1109/PerComWorkshops56833.2023.10150228},
year = {2023}
}
|
|
| Dec 2022 | Hierarchical and Decentralised Federated Learning link |
| TLDR | Authors | Publication | BibTeX | Cloud Continuum 2022 | |
|
TLDR: We survey hierarchical and decentralised federated learning approaches, organizing aggregation topologies across cloud, edge, and device tiers.
|
|
@inproceedings{rana2023hierarchical,
title = {Hierarchical and Decentralised Federated Learning},
author = {Omer F. Rana and Theodoros Spyridopoulos and Nathaniel Hudson and Matt Baughman and Kyle Chard and Ian T. Foster and Aftab Khan},
booktitle = {2022 Cloud Continuum},
doi = {10.1109/CloudContinuum57429.2022.00008},
pages = {1--9},
publisher = {{IEEE}},
year = {2022}
}
|
|
| Oct 2022 | FLoX: Federated Learning with FaaS at the Edge link |
| TLDR | Authors | Publication | BibTeX | eScience 2022 | |
|
TLDR: FLoX runs federated learning on serverless (FaaS) infrastructure at the edge, decoupling model training and inference from deployment so FL can run on heterogeneous real-world devices.
|
|
@inproceedings{kotsehub2022flox,
title = {FLoX: Federated Learning with FaaS at the Edge},
author = {Nikita Kotsehub and Matt Baughman and Ryan Chard and Nathaniel Hudson and Panos Patros and Omer F. Rana and Ian T. Foster and Kyle Chard},
booktitle = {18th {IEEE} International Conference on e-Science, e-Science 2022, Salt Lake City, UT, USA, October 11-14, 2022},
doi = {10.1109/ESCIENCE55777.2022.00016},
pages = {11--20},
publisher = {{IEEE}},
url = {https://doi.org/10.1109/eScience55777.2022.00016},
year = {2022}
}
|
|
| Oct 2022 | Exploring Tradeoffs in Federated Learning on Serverless Computing Architectures link |
| TLDR | Authors | Publication | BibTeX | eScience 2022 | |
|
TLDR: We systematically explore accuracy, cost, and time tradeoffs of federated learning on serverless architectures, quantifying configuration effects across deployment scenarios.
|
|
@inproceedings{baughman2022tradeoffs,
title = {Exploring Tradeoffs in Federated Learning on Serverless Computing Architectures},
author = {Matt Baughman and Ian T. Foster and Kyle Chard},
booktitle = {18th {IEEE} International Conference on e-Science, e-Science 2022, Salt Lake City, UT, USA, October 11-14, 2022},
doi = {10.1109/ESCIENCE55777.2022.00074},
pages = {433--434},
publisher = {{IEEE}},
url = {https://doi.org/10.1109/eScience55777.2022.00074},
year = {2022}
}
|
|
| Jul 2022 | Adaptive Edge-Cloud Environments for Rural AI link |
| TLDR | Authors | Publication | BibTeX | IEEE SCC 2022 | |
|
TLDR: We present adaptive edge-cloud environments for rural AI applications, orchestrating workloads across constrained, intermittently connected resources.
|
|
@inproceedings{almurshed2022adaptive,
title = {Adaptive Edge-Cloud Environments for Rural {AI}},
author = {Osama Almurshed and Panos Patros and Victoria Huang and Michael Mayo and Melanie Ooi and Ryan Chard and Kyle Chard and Omer F. Rana and Harshaan Nagra and Matt Baughman and Ian T. Foster},
booktitle = {{IEEE} International Conference on Services Computing, {SCC} 2022, Barcelona, Spain, July 10-16, 2022},
doi = {10.1109/SCC55611.2022.00023},
pages = {74--83},
publisher = {{IEEE}},
url = {https://doi.org/10.1109/SCC55611.2022.00023},
year = {2022}
}
|
| Apr 2026 | Implications of Grid-Forming Inverter Parameters on Disturbance Localization and Controllability link |
| TLDR | Authors | Publication | BibTeX | IEEE Control Systems Letters | |
|
TLDR: We study how grid-forming inverter parameter choices shape disturbance localization and controllability in power systems, providing guidance for tuning inverter-dominated grids.
|
|
@article{baughman2026inverters,
title = {Implications of Grid-Forming Inverter Parameters on Disturbance Localization and Controllability},
author = {Matt Baughman and Marena Trujillo and Bri{-}Mathias Hodge and Emily Jensen},
doi = {10.1109/LCSYS.2026.3688759},
journal = {{IEEE} Control. Syst. Lett.},
pages = {319--324},
url = {https://doi.org/10.1109/LCSYS.2026.3688759},
volume = {10},
year = {2026}
}
|
|
| May 2024 | RuralAI in Tomato Farming: Integrated Sensor System, Distributed Computing, and Hierarchical Federated Learning for Crop Health Monitoring link |
| TLDR | Authors | Publication | BibTeX | IEEE Sensors Letters | |
|
TLDR: We deploy an integrated sensor system with distributed computing and hierarchical federated learning for tomato crop health monitoring, demonstrating end-to-end rural AI in the field.
|
|
@article{devaraj2024ruralai,
title = {{RuralAI} in Tomato Farming: Integrated Sensor System, Distributed Computing, and Hierarchical Federated Learning for Crop Health Monitoring},
author = {Harish Devaraj and Shaleeza Sohail and Melanie Ooi and Boyang Li and Nathaniel Hudson and Matt Baughman and Kyle Chard and Ryan Chard and Enrico Casella and Ian Foster and Omer Rana},
doi = {10.1109/LSENS.2024.3384935},
journal = {{IEEE} Sensors Letters},
number = {5},
pages = {1--4},
volume = {8},
year = {2024}
}
|
|
| Dec 2018 | CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research link |
| TLDR | Authors | Publication | BibTeX | BMC Bioinformatics | |
|
TLDR: CANDLE/Supervisor provides a workflow framework for large-scale hyperparameter exploration of deep learning models in cancer research on HPC systems.
|
|
@article{wozniak2018candle,
title = {CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research},
author = {Justin M. Wozniak and Rajeev Jain and Prasanna Balaprakash and Jonathan Ozik and Nicholson T. Collier and John Bauer and Fangfang Xia and Thomas S. Brettin and Rick Stevens and Jamaludin Mohd{-}Yusof and Cristina Garcia{-}Cardona and Brian Van Essen and Matthew Baughman},
doi = {10.1186/S12859-018-2508-4},
journal = {{BMC} Bioinform.},
number = {18},
pages = {59--69},
url = {https://doi.org/10.1186/s12859-018-2508-4},
volume = {19-S},
year = {2018}
}
|