Published multiple papers, including one highlighted at CVPR 2024 titled 'ANDA: Strong Transferable Adversarial Attacks via Ensembled Asymptotically Normal Distribution Learning'; developed the TransferAttack framework to boost adversarial transferability for image classification.
Research Experience
Involved in several research projects on trustworthy AI, including MMTrustEval (a toolbox for benchmarking the trustworthiness of multimodal large language models) and MLA-Trust (a toolbox for benchmarking the trustworthiness of Multimodal LLM Agents across dimensions of truthfulness, controllability, safety, and privacy). Also worked on strong transferable adversarial attacks.
Background
Research interests include, but are not limited to, trustworthy artificial intelligence.