Kelvin Yuxiang Huang
Undergraduate Researcher | CS & Stats @ UC Berkeley
yxkelvinhuang[at]berkeley.edu
I am a senior undergraduate student at the University of California, Berkeley, double majoring in Computer Science and Statistics.
I will join the University of Toronto in Fall 2026 as a Ph.D. student in the Computer Science PhD Program, advised by Prof. Gururaj Saileshwar.
My research interests lie broadly in Artificial Intelligence, with a specific focus on Trustworthy AI, Vision-Language Models (VLMs), and Agentic Systems. My current goal is to build AI systems that are robust against adversarial attacks and transparent through structured observability.
Currently, I am a Researcher on Trustworthy Agentic AI at UC Berkeley, advised by Prof. Dawn Song, focusing on structured observability for agent systems. Simultaneously, I conduct independent research on adversarial robustness for LVLMs at the Blender Lab (UIUC), advised by Prof. Qingyun Wang (now at William & Mary).
Previously, I was selected for the Summer Research Programme (SRP) at HKU-EEE, where I developed a physics-aware 3D generation pipeline advised by Prof. Xihui Liu. I also served as a Lead Developer at the Levi Lab (UC Berkeley), engineering computational frameworks for vision and eye movement modeling under Prof. Dennis Levi.
news
| Mar 30, 2026 | Iām very excited to share that I will be joining the University of Toronto this fall as a PhD student in Computer Science. š |
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| Nov 19, 2025 | Thrilled to share that our paper on Trustworthy Agentic AI has been accepted to the AAAI 2026 TrustAgent Workshop! š |
| Nov 05, 2025 | Thrilled to share that our paper on VLM adversarial robustness has been accepted to the AAAI 2026 AIGOV Workshop! š |
| Sep 21, 2025 | Excited to start as a researcher advised by Prof. Dawn Song to work on Trustworthy Agentic AI. |
| Apr 14, 2025 | Excited to kick off my research journey with Prof. Qingyun Wang at College of William & Mary, where I will be working on VLM robustness. |
selected publications
- AAAIw
ATLAS: Shielding Geolocation in LVLMs with Zero-Query Universal PerturbationsIn AAAI-26 Workshop on AI for Governance (AIGOV), 2026Accepted (Poster, Non-archival) - AAAIw
AgentTrace: A Structured Logging Framework for Agent System ObservabilityIn AAAI-26 Workshop on AI for TrustAgent, 2026Accepted (Poster, Non-archival)
* Equal contribution