Artificial Intelligence#
PTM Naming: Why “What’s in a Name” Actually Matters for AI Reuse
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)
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.
SysLLMatic: Large Language Models as Software System Optimizers (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)
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)
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
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.
AI in Hiring: Fairness or Just Automated Bias?
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)
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)
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)
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)
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)
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.
Deep Learning Model Reengineering: Challenges and Practices (EMSE 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)
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.