Posted in 2025
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.
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.