publications

2025

1. Supercomputing

   

   Shortcut MixUp Policy: Toward Improving Robustness and Speed in Goal-Conditioned RL

   Matt Hyatt, Yassir Atlas^*, Hal Brynteson^*, and 11 more authors

   In Proceedings of The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC ’25), 2025

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2024

1. 

   Robust Source Attribution of Synthetically Generated Western Blot Images

   Matthew Hyatt, George K. Thiruvathukal, and Daniel Moreira

   Jan 2024

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2023

1. ICSE

   

   An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry

   Wenxin Jiang, Nicholas Synovic, Matt Hyatt, and 5 more authors

   In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE), Jan 2023

   DOI

   

2. 

   Snapshot Metrics Are Not Enough: Analyzing Software Repositories with Longitudinal Metrics

   Nicholas M. Synovic, Matt Hyatt, Rohan Sethi, and 11 more authors

   In Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, Rochester, MI, USA, Jan 2023

   Abs DOI Video Code Website

   Software metrics capture information about software development processes and products. These metrics support decision-making, e.g., in team management or dependency selection. However, existing metrics tools measure only a snapshot of a software project. Little attention has been given to enabling engineers to reason about metric trends over time—longitudinal metrics that give insight about process, not just product. In this work, we present PRIME (PRocess MEtrics), a tool to compute and visualize process metrics. The currently-supported metrics include productivity, issue density, issue spoilage, and bus factor. We illustrate the value of longitudinal data and conclude with a research agenda. The tool’s demo video can be watched at https://bit.ly/ase2022-prime. Source code can be found at https://github.com/SoftwareSystemsLaboratory/prime.

2022

1. 

   An Empirical Study of Artifacts and Security Risks in the Pre-trained Model Supply Chain

   Wenxin Jiang, Nicholas Synovic, Rohan Sethi, and 5 more authors

   In Proceedings of the 2022 ACM Workshop on Software Supply Chain Offensive Research and Ecosystem Defenses, Los Angeles, CA, USA, Jan 2022

   Abs DOI

   

   Deep neural networks achieve state-of-the-art performance on many tasks, but require increasingly complex architectures and costly training procedures. Engineers can reduce costs by reusing a pre-trained model (PTM) and fine-tuning it for their own tasks. To facilitate software reuse, engineers collaborate around model hubs, collections of PTMs and datasets organized by problem domain. Although model hubs are now comparable in popularity and size to other software ecosystems, the associated PTM supply chain has not yet been examined from a software engineering perspective. We present an empirical study of artifacts and security features in 8 model hubs. We indicate the potential threat models and show that the existing defenses are insufficient for ensuring the security of PTMs. We compare PTM and traditional supply chains, and propose directions for further measurements and tools to increase the reliability of the PTM supply chain.