Matt Baughman
PhD // Computer Scientist
Hi there! I recently completed my Ph.D. in Computer Science with Globus Labs at the University of Chicago, where I was co-advised by Ian Foster and Kyle Chard. My research spans high-performance computing, distributed systems, and cost-aware computing. I completed my Bachelors in Computer Science and Philosphy at Minerva University and have experience at Argonne National Lab, 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.
book DISSERTATIONS link
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| Coralling the Computing Continuum [Jul 2025] link |
| Abstract | Committee | PDF | Slides | Poster | Doctoral Dissertation |
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Committee:
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| PROFILING, PREDICTING, AND PROVISIONING: ENABLING COST-AWARE COMPUTATION FOR THE CLOUD AND MODERN HETEROGENEOUS ENVIRONMENTS [Aug 2021] link |
| Abstract | Committee | PDF | Slides | Masters Thesis |
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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.
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Committee: Kyle Chard, Ian Foster, and Hank Hoffman
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engineering PROJECTS link
Check out all of my projects on GitHub.
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star SELECTED PUBLICATIONS link
Ordered by most recent.
article ALL PUBLICATIONS link
Ordered by most recent and grouped by topic. Bibtex file available for download here.
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DISTRIBUTED SYSTEMS
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SCALABLE DEEP LEARNING
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AI FOR SCIENCE
co_present PRESENTATIONS link
Ordered by most recent.