Blaine Hoak

Ph.D. Student in Computer Sciences

University of Wisconsin-Madison

Hello! I am a Ph.D. student in the Department of Computer Sciences at the University of Wisconsin-Madison. I work in the MadS&P Laboratory and am advised by Prof. Patrick McDaniel.

My research is centered around evaluating and advancing the trustworthiness of AI/ML systems. I am specifically interested in investigating functional differences between machine learning models and biological systems (e.g., the human visual system), and how these differences play a role in the robustness and resiliency of models. My current work aims to explain and bridge this gap to build safer, better aligned machine learning models.

Aside from AI trustworthiness, I am also interested in and have collaborated on works broadly within security and privacy, all with the goal of identifying and protecting against threats to user privacy and security in a variety of systems.

Feel free to reach out if you want to know more!

Interests

Trustworthy Machine Learning/AI
Security and Privacy
Machine Learning

Education

Ph.D. in Computer Sciences, 2026 (expected)
University of Wisconsin-Madison
B.S. in Biomedical Engineering, 2020
Pennsylvania State University

Highlighted Publications

Blaine Hoak, Ryan Sheatsley, Eric Pauley, Patrick McDaniel

August, 2023 In USENIX Security ‘23

The Space of Adversarial Strategies

We produce and evaluate 576 attacks, and study when and why components of attacks perform well.

Recent Publications

Blaine Hoak, Patrick McDaniel (2024). Generating High-Quality Sythetic Textures. In submission.

Cite

Blaine Hoak, Patrick McDaniel (2024). Explorations in Texture Learning. 12th International Conference on Learning Representations, Tiny Papers Track (ICLR).

Cite Code Poster arXiv Blog Post

Hadi Abdullah, Blaine Hoak, Ke Wang, Yizhen Wang, Sunpreet Arora, Yiwei Cai (2024). Is Memorization Actually Necessary for Generalization?. In submission.

Cite

Blaine Hoak, Ryan Sheatsley, Eric Pauley, Patrick McDaniel (2023). The Space of Adversarial Strategies. In USENIX Security ‘23.

PDF Cite Code Poster Slides Video arXiv USENIX Page

Eric Pauley, Ryan Sheatsley, Blaine Hoak, Quinn Burke, Yohan Beugin, Patrick McDaniel (2022). Measuring and Mitigating the Risk of IP Reuse on Public Clouds. IEEE Symposium on Security and Privacy (IEEE S&P).

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Recent & Upcoming Talks

Adversarial Attacks

SecureAI is a cybersecurity and privacy training program designed for AI professionals and researchers to equip them with the knowledge and skills to build AI systems that are technically sound and secure. In this talk I provide an introduction to adversarial examples and guidelines for robustness evaluations in practical systems.

AI’s Impact on the Future of Work

The UW-Madison School of Computer, Information, and Data Sciences is hosting an event at Microsoft in Chicago in partnership with the School of Business. The event will have a speaking panel focused on the future of work, specifically for those working in tech in relation to advances in AI. I am honored to be one of the speakers on the panel!

Machine Learning in Security

This talk was a guest lecture for CS 642 - Introduction to Information Security. In this lecture, I presented an introduction of machine learning techniques and applications in security domains.

The Space of Adversarial Strategies @ USENIX Security 2023

The conference presentation of our accepted work, The Space of Adversarial Strategies, at USENIX Security in Anahiem, CA.

Trust, Expectations, and Failures in AI @ The UW Now

I had the incredible opportunity of being featured on The UW Now as a panelist for the episode “Artificial Intelligence - Facts vs. Fiction”. Here, I discussed ways that AI can fail, what it means to have trustworthy AI, and what we, as a society and as experts of many of our own areas, can do to help build a future towards trustworthy AI. This talk is intended for a general audience, no computer science or security background required :)