Blog Posts#
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.
TLA+ for All: Running Model Checking in a Python Notebook
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.
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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 :cite:p:`ahlgren_not-so-secret_2025`, 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?
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.