Ph.D. Student, Computer Science
I am a Ph.D. student in the Computer Sciences Department at the University of Wisconsin-Madison, advised by Prof. Patrick McDaniel, and a research assistant in the Security and Privacy Research Group. I am grateful to have been mentored by Dr. Mantas Mazeika and Dr. Dan Hendrycks at CAIS. I received my B.S. in Computer Science and Mathematics (Honors, advised by Adwait Nadkarni) from William & Mary.
I am interested in building trustworthy AI systems that remain resilient in complex, adversarial environments. Specifically, I have studied how model architectures and training strategies affect adversarial robustness. I have also worked on AI functional wellbeing and its implications for safety. My current research centers on the security properties of multi-agent systems. As AI agents become more capable and collaborative, their interactions create new, emergent vulnerabilities. My goal is to develop methods and frameworks to ensure these systems are reliable and secure in real-world deployments. I am always open to discussing new ideas, feel free to reach out!
In my free time, I enjoy being outdoors—biking 🚴, hiking 🏔️, and snowboarding 🏂—as well as playing table tennis 🏓.
Report AI Wellbeing: Measuring and Improving the Functional Pleasure and Pain of AIs website paper code
Technical Report, 2026
Conference On the Robustness Tradeoff in Fine-Tuning paper code poster
IEEE/CVF International Conference on Computer Vision (ICCV), 2025
Workshop Alignment and Adversarial Robustness: Are More Human-Like Models More Secure? paper code
The European Conference on Artificial Intelligence (ECAI) - Workshop on Security and Privacy-Preserving AI/ML (SPAIML), 2025
Preprint Robustness Under Texture Transformations: Exploiting Natural Texture Backdoors in Vision Models paper
In submission, 2025
Thesis On Adversarial and Common Robustness of Parameter-Efficient Fine-Tuning Strategies paper
Master Thesis, 2024
Workshop ParTEETor: A System for Partial Deployments of TEEs within Tor paper
Workshop on Privacy in the Electronic Society (WPES), 2024
Workshop The Efficacy of Transformer-based Adversarial Attacks in Security Domains paper
IEEE Conference on Military Communications (MILCOM) - AI for Cyber Workshop, 2023
Conference The Trade-off between Label Efficiency and Universality of Representations from Contrastive Learning paper
International Conference on Learning Representations (ICLR), 2023 Spotlight (Acceptance Rate: 7.95%)
Thesis Static and Dynamic Analysis in Cryptographic-API Misuse Detection of Mobile Application paper
Undergraduate Honors Thesis, 2022