About
Secure and trustworthy AI. My research examines the trustworthiness of provenance methods for AI models, particularly model fingerprinting and watermarking, which can support intellectual property protection and legal accountability in generative AI. These methods aim to identify which model produced an output, but their reliability under realistic deployment and adversarial conditions remains uncertain. I evaluate their robustness against realistic adversarial attacks and develop defenses to address the resulting weaknesses. My broader interests include privacy, fairness, and general security problems in AI systems, as well as AI ethics and governance topics.
PhD advisor: Dr. Marc Juarez.
News
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Sep 2026Our paper, “What Breaks Local Watermarks? A Robustness Benchmark for Local Invisible Image Watermarking”, has been accepted to AISec 2026.
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Jul 20262 papers on AI governance in education, accepted to AIES 2026, examine what credentials should still certify and what capacities education should preserve when cognitive work is delegated to AI.
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May 2026Released SPRINT, a method for robustly attributing AI-generated images to their source models under adversarial attacks.
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Mar 2026Our SaTML 2026 paper Smudged Fingerprints was featured by Herald Scotland, DIGIT, and University of Edinburgh News.
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Mar 2026Gave a guest lecture on Image Provenance in the AI Era at the University of Edinburgh (slides).
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Apr 2025Presented SoK: What Makes Private Learning Unfair? at SaTML 2025 in Copenhagen.
View earlier updates
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Dec 2024SoK: What Makes Private Learning Unfair? accepted to SaTML 2025, providing the first causal analysis of fairness degradation induced by differential privacy.
Publications
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AISec '26
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SaTML '26
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AIES '26
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AIES '26
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arXiv
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SaTML '25
View full publication record
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Bio Protoc
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J. Cell Sci.
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J. Cell Biol.
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Sci. Rep.
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Biomed. Eng. Online
Note: * denotes equal contribution (co-first author).
Teaching and Mentoring
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LectureGuest lecture on Image Provenance in the AI Era for the graduate course Privacy and Security with Machine Learning — University of Edinburgh. The slides are available here.
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Teaching Assistant
- Privacy and Security with Machine Learning — University of Edinburgh (2023–2025)
- Mathematical Image Analysis — Johns Hopkins University (2019–2020)
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Research TutoringFelipe Takaesu (JHU), Eliana Crentsil (JHU), Lucia Sablich (JHU), Shannon Flanary (JHU), Chunhan Fang (UoE).
Academic Service
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Program Committee
- IEEE Conference on Secure and Trustworthy Machine Learning (SaTML 2027)
- ACM Workshop on Artificial Intelligence and Security (AISec 2026)
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Reviewer
- ACM Digital Threats: Research and Practice (DTRAP)
- The 40th Annual AAAI Conference on Artificial Intelligence (AAAI-26)
- Privacy Enhancing Technologies Symposium (PETS/PoPETs 2027)
Industry Experience
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2021–2023AI Frameworks Engineer, Intel
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2020–2021AI Algorithm Engineer, Huawei