All Posts
PTM Naming: Why “What’s in a Name” Actually Matters for AI Reuse
- 27 December 2025
I’m thrilled to share some recent work led by Wenxin Jiang, a PhD student at Purdue University. Wenxin is supervised by James C. Davis, and I have had the pleasure of serving as a key external supervisor and PhD committee member on this project as part of my ongoing collaboration with Dr. Davis. This research was recently accepted for publication in Journal of Empirical Software Engineering and it tackles a problem that anyone working in AI has likely grumbled about: how we name our models.
Advancing HPC Education with an Agentic Tutoring System (EduHPC 2025)
- 29 November 2025
This post highlights a recent EduHPC 2025 paper doi:10.1145/3731599.3767386 led by my PhD student Erik Pautsch and co-supervised by me and Silvio Rizzi at Argonne National Laboratory.
How Loyola Is Bringing Artificial Intelligence and Ethics into the Classroom
- 11 August 2025
Loyola News published a piece by Jeff Link, “How Loyola Is Bringing Artificial Intelligence and Ethics into the Classroom”, that quoted me at some length on how I think about AI’s place in higher education. I wanted to expand a bit on the points I made there.
SysLLMatic: Large Language Models as Software System Optimizers (2025)
- 02 June 2025
This paper was led by Huiyun Peng and Akhil Gupte (Purdue University), working with James C. Davis, Yung-Hsiang Lu, and others including Ryan Hasler and Nicholas J. Eliopoulos. I am one of the key leaders of this research project. The work is available as an arXiv preprint (arXiv:2506.01249).
Improving Deep Learning Reproducibility: A Case Study Investigation (2025)
- 06 May 2025
This paper was led by Nadia Ravi and Aditya Goel (Purdue University), working with James C. Davis. I am one of the key leaders of this research project. The work is available as an arXiv preprint (arXiv:2505.03165).
Pruning One More Token Is Enough: Efficient Vision Transformers on the Edge (WACV 2025)
- 28 February 2025
This paper was led by Nicholas J. Eliopoulos (Purdue University), working with James C. Davis, Guoqing Liu, and Yung-Hsiang Lu. I am one of the key leaders of this research project. The work appeared at WACV 2025 (IEEE/CVF Winter Conference on Applications of Computer Vision).
Special Issue on Low-Code/No-Code + Metaverse in IEEE Computer
- 23 February 2025
Low-code/no-code is increasingly converging with the metaverse to reshape how we create and experience technology. As AI-driven tools automate significant portions of coding and power immersive virtual environments, organizations can achieve new efficiencies—provided that security, ethics, and user empowerment remain central.
TLA+ for All: Running Model Checking in a Python Notebook
- 05 February 2025
TLA+ has long been a powerful tool for designing and verifying complex systems. However, many students and practitioners have felt excluded by the ecosystem’s complexity, the need to install multiple tools, or the misconception that formal methods are only for specialists. This project aims to change that.
A Signal Injection Attack Against Zero Involvement Pairing and Authentication for the Internet of Things
- 28 December 2024
Zero Involvement Pairing and Authentication (ZIPA) is a technique for automatically provisioning large networks of Internet-of-Things (IoT) devices with no user involvement. Prior ZIPA work generally assumes that the environment used for pairing is sufficiently isolated from external, adversarial signals. In our DESTION 2024 paper (see Citation below), we present the first signal-injection attack capable of influencing ZIPA-based key generation, demonstrating that these assumptions can fail in realistic settings.
AI in Hiring: Fairness or Just Automated Bias?
- 18 November 2024
Artificial intelligence has become increasingly embedded in modern hiring systems. From résumé screening to candidate scoring, automated tools promise efficiency, objectivity, and scale. Yet these promises often obscure important risks: when AI models inherit biased historical data, they can reinforce or even amplify inequities in hiring.
What Do We Know About Hugging Face? A Systematic Literature Review (ESEM 2024)
- 24 October 2024
This paper was led by Jason Jones and Wenxin Jiang (Purdue University, working with James C. Davis). I am one of the key leaders of this research project and contributed to the analysis and synthesis. The work appeared at ESEM 2024 (ACM/IEEE International Symposium on Empirical Software Engineering and Measurement).
LLMs for Energy-Efficient Code: Emerging Results and Future Directions (2024)
- 12 October 2024
This paper was led by Huiyun Peng and Akhil Gupte (Purdue University), working with James C. Davis and Yung-Hsiang Lu. I am one of the key leaders of this research project. The work is available as an arXiv preprint (arXiv:2410.09241).
When ONNX Converters Fail: Interoperability Risks in Deep Learning (ISSTA 2024)
- 16 September 2024
This paper was led by Purvish Jajal and Wenxin Jiang (Purdue University, working with James C. Davis and Yung-Hsiang Lu). I am one of the key leaders of this research project. The work appeared at ISSTA 2024 (ACM International Symposium on Software Testing and Analysis).
PeaTMOSS: Mining Pre-Trained Models in Open-Source Software (MSR 2024)
- 15 April 2024
This paper was led by Wenxin Jiang (Purdue University, working with James C. Davis). I am one of the key leaders of this research project and contributed to its design and analysis. The work appeared at MSR 2024 (International Conference on Mining Software Repositories).
SingleAdv: Targeted Adversarial Attacks on Interpretable Deep Learning (IEEE TIFS 2024)
- 01 March 2024
This paper was led by Elmurod Abdukhamidov and Mohammed Abuhamad (Hanyang University, Korea), with Hyoungshick Kim and Tamer Abuhmed as co-leads. I am one of the key leaders of this research project, contributing to the threat modeling and evaluation design. The work appeared in IEEE Transactions on Information Forensics and Security.
Intermediate C Programming, 2nd Edition
- 09 February 2024
I’m happy to share the second edition of Intermediate C Programming, co-authored with Yung-Hsiang Lu (Purdue University) and published by Routledge / Taylor & Francis Group. This edition expands the software engineering coverage that much of my own teaching in the Software Systems Laboratory (COMP 310) draws on.
Deep Learning Model Reengineering: Challenges and Practices (EMSE 2024)
- 30 January 2024
This paper was led by Wenxin Jiang (Purdue University, working with James C. Davis), with contributions from Vishnu Banna, Nikhil Vivek, Aditya Goel, and Nicholas Synovic. I am one of the key leaders of this research project. The work appeared in the Empirical Software Engineering journal.
Pre-Trained Model Reuse in Hugging Face: An Empirical Study (ICSE 2023)
- 14 May 2023
This paper was led by Wenxin Jiang, a PhD student at Purdue University working with James C. Davis. I am one of the key leaders of this research project, contributing to the study design, analysis, and framing. The work appeared at ICSE 2023, the flagship conference in software engineering.
An Empirical Study of Artifacts and Security Risks in the Pre-Trained Model Supply Chain (SCORED 2022)
- 07 November 2022
This paper was led by Wenxin Jiang (Purdue University, working with James C. Davis), with contributions from Nicholas Synovic, Rohan Sethi, Aryan Indarapu, Matt Hyatt, and Taylor R. Schorlemmer. I am one of the key leaders of this research project. The work appeared at SCORED ‘22 (ACM Workshop on Software Supply Chain Offensive Research and Ecosystem Defenses), co-located with ACM CCS 2022.
Low-Power Computer Vision: Improve the Efficiency of Artificial Intelligence
- 23 February 2022
I’m glad to share Low-Power Computer Vision: Improve the Efficiency of Artificial Intelligence, an edited volume I co-edited with Yung-Hsiang Lu, Jaeyoun Kim, Yiran Chen, and Bo Chen, published by Chapman & Hall/CRC (Routledge/CRC Press). This post also covers the companion survey that laid the groundwork for the book, “A Survey of Methods for Low-Power Deep Learning and Computer Vision”, led by Abhinav Goel and Caleb Tung (Purdue University), with Yung-Hsiang Lu and myself, presented at WF-IoT 2020.
Codename Revolution: The Nintendo Wii Platform
- 24 February 2012
Long before most of my posts here on empirical software engineering and pre-trained models, I co-authored Codename Revolution: The Nintendo Wii Platform with Steven E. Jones (then at Loyola University Chicago), published by MIT Press as part of its Platform Studies series.