Portrait of Matt Baughman

Matt Baughman

Postdoctoral Computational Scientist // Princeton University & PPPL

email | github | linkedin | scholar

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

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Distributed Systems: We are designing new programming paradigms which decouple system configuration and control from application design to enable multi-system workflows spanning HPC, the cloud, and the edge.
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Federated Learning: We are exploring new techniques for improving distributed deep learning using self-adaptive system design and online feedback to enable complex, hierarchical federated learning across diverse ecosystems of hardware.
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Cost-aware Computing: We are building scalable frameworks for highly distributed science workflows spanning dozens of computing sites and scaling to tens of thousands of compute nodes using novel cost-aware task placement algorithms and federated function-as-a-service infrastructure.

star SELECTED PUBLICATIONS link

Ordered by most recent.

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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
| Publication |
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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
| Publication |
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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
| Publication |
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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
| Publication |
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Balancing Federated Learning Trade-Offs for Heterogeneous Environments [Mar 2023]
Matt Baughman, Nathaniel Hudson, Ian T. Foster, Kyle Chard
PerCom Workshops 2023
| PDF | Publication |
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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
| Publication |

book THESES link

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Corralling the Computing Continuum: Enabling Multi-System Workflows with Serverless Computing [Aug 2025] link
| | PDF | Slides | Doctoral Dissertation
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Profiling, Predicting, and Provisioning: Enabling Cost-Aware Computation for the Cloud and Modern Heterogeneous Environments [Aug 2021] link
| | PDF | Slides | Master's Thesis

article ALL PUBLICATIONS link

Ordered by most recent and grouped by topic. BibTeX file available for download here.

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DISTRIBUTED SYSTEMS
Sep 2024 An Empirical Investigation of Container Building Strategies and Warm Times to Reduce Cold Starts in Scientific Computing Serverless Functions link
| | Publication | | eScience 2024
Jul 2024 Enabling Remote Management of FaaS Endpoints with Globus Compute Multi-User Endpoints link
| | Publication | | PEARC 2024
May 2024 Unveiling Temporal Performance Deviation: Leveraging Clustering in Microservices Performance Analysis link
| | Publication | | ICPE 2024 Companion
Dec 2023 Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision link
| | Publication | | BDCAT 2023
Nov 2023 Accelerating Communications in Federated Applications with Transparent Object Proxies link
| | Publication | | SC 2023
Nov 2022 Assessing the Current State of AWS Spot Market Forecastability link
| | Publication | | SuperCompCloud 2022
Sep 2021 Enhancing Automated FaaS with Cost-aware Provisioning of Cloud Resources link
| | Publication | | eScience 2021
Jun 2021 Coding the Computing Continuum: Fluid Function Execution in Heterogeneous Computing Environments link
| | Publication | | IPDPSW 2021
Jun 2021 Expanding Cost-Aware Function Execution with Multidimensional Notions of Cost link
| | Publication | | HiPS 2021
Dec 2019 Measuring, Quantifying, and Predicting the Cost-Accuracy Tradeoff link
| | Publication | | BPOD @ IEEE BigData 2019
Dec 2019 ParaOpt: Automated Application Parameterization and Optimization for the Cloud link
| | Publication | | CloudCom 2019
Jun 2019 Deconstructing the 2017 Changes to AWS Spot Market Pricing link
| | Publication | | ScienceCloud 2019
Dec 2018 Profiling and Predicting Application Performance on the Cloud link
| | Publication | | UCC 2018
Jun 2018 Predicting Amazon Spot Prices with LSTM Networks link
| | Publication | | ScienceCloud 2018
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FEDERATED LEARNING
Jan 2026 Flight: A FaaS-based framework for complex and Hierarchical Federated Learning link
| | Publication | | Future Generation Computer Systems
Aug 2024 QoS-aware edge AI placement and scheduling with multiple implementations in FaaS-based edge computing link
| | Publication | | Future Generation Computer Systems
Dec 2023 Measurement and Applications: Exploring the Challenges and Opportunities of Hierarchical Federated Learning in Sensor Applications link
| | Publication | | IEEE Instrumentation & Measurement Magazine
Nov 2023 Tournament-Based Pretraining to Accelerate Federated Learning link
| PDF | | Publication | | SC 2023 Workshops
Mar 2023 Rural AI: Serverless-Powered Federated Learning for Remote Applications link
| | Publication | | IEEE Internet Computing
Mar 2023 Balancing Federated Learning Trade-Offs for Heterogeneous Environments link
| PDF | | Publication | | PerCom Workshops 2023
Dec 2022 Hierarchical and Decentralised Federated Learning link
| | Publication | | Cloud Continuum 2022
Oct 2022 FLoX: Federated Learning with FaaS at the Edge link
| | Publication | | eScience 2022
Oct 2022 Exploring Tradeoffs in Federated Learning on Serverless Computing Architectures link
| | Publication | | eScience 2022
Jul 2022 Adaptive Edge-Cloud Environments for Rural AI link
| | Publication | | IEEE SCC 2022
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AI FOR SCIENCE & APPLICATIONS
Apr 2026 Implications of Grid-Forming Inverter Parameters on Disturbance Localization and Controllability link
| | Publication | | IEEE Control Systems Letters
May 2024 RuralAI in Tomato Farming: Integrated Sensor System, Distributed Computing, and Hierarchical Federated Learning for Crop Health Monitoring link
| | Publication | | IEEE Sensors Letters
Dec 2018 CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research link
| | Publication | | BMC Bioinformatics