Publications

Making AI systems safer,
one boundary at a time.

My research spans trustworthy AI, foundation model alignment, robustness, hallucinations, generative and multimodal AI, out-of-distribution detection, and data augmentation.

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2026 ICML

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models

Qilin Liao, Anamika Lochab, Ruqi Zhang

Vision-Language Models (VLMs) extend large language models with visual reasoning, but their multimodal design also introduces new, underexplored vulnerabilities. Existing multimodal red-teaming methods largely rely on brittle templates, focus on single-attack settings, and expose only a narrow subset of vulnerabilities. To address these limitations, we introduce VERA-V, a...

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2025 ICML

BOOD: Boundary-based Out-Of-Distribution Data Generation

Qilin Liao, Shuo Yang, Bo Zhao, Ping Luo, Hengshuang Zhao

Harnessing the power of diffusion models to synthesize auxiliary training data based on latent space features has proven effective in enhancing out-of-distribution (OOD) detection performance. However, extracting effective features outside the in-distribution (ID) boundary in latent space remains challenging due to the difficulty of identifying decision boundaries between...

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