Bang An

Bang’s research is dedicated to advancing Responsible AI, with a focus on enhancing the safety, alignment, robustness, and fairness of AI systems, particularly in Generative AI. Bang is the recipient of the Outstanding Graduate Assistant Award at UMD. She currently leads the organization of a NeurIPS’24 competition on image watermarks.

Marco Bornstein

Marco’s expertise lies in federated learning, optimization and mechanism design.

Mucong Ding

On the Academic Job Market

Mucong’s research focuses on generative AI reasoning, graph neural networks, with a keen interest in alignment and data-centric AI. Mucong also currently leads the organization of a NeurIPS’24 competition on image watermarks.

Xiaoyu Liu

Xiaoyu’s research focuses on understanding latent representations from a causal perspective and their applications in LLMs and LVLMs. Her recent works include efficient counterfactual augmentation for images, causal sequential decision making in recommendation systems and alignment in LVLMs.

Yuancheng Xu

Yuancheng’s recent research focuses on efficient test-time alignment of LLMs, addressing safety issues of (multi-modal) LLMs and LLM agents, as well as enhancing the reasoning and planning capabilities of AI systems. Additionally, he works on controllable video generation, aiming to recreate the world through representations of multimodal content and user-model interactions.

Yuhang Zhou

Yuhang’s research centers on natural language processing and computational social science, focusing on developing robust models in large language models (LLMs). He also has extensive experience working on responsbile AI/ML, designing algorithms that are fair and trustworthy.

Sicheng Zhu

Sicheng’s research focuses on trustworthy machine learning, including robustness and generalization. His recent work focuses on alignment of large language models, including jailbreak attacks and automated red-teaming for false refusals.