About Me

I'm an associate professor in the University Libraries and School of Information Studies at Purdue, with a joint appointment in the School of Applied and Creative Computing. I'm also affiliated with the Institute for Physical Artificial Intelligence (IPAI) and the Applied AI Research Center. My research centers on foundation model adaptation, representation learning, and trustworthy AI for real-world, information-rich tasks. Before joining Purdue, I was an Assistant Professor in the Department of Computer Engineering at the Rochester Institute of Technology. Over the years I've had the privilege of mentoring a group of sharp students. Sometimes they even listen to me. Outside of work, I love traveling the world and playing tennis (3.0–3.5), and I share my home with two cats, Tiger (小虎) and Meimei (妹妹), who keep the place lively.

Research Interests

My work is in Applied AI, mostly about the post-training and adaptation of foundation models. I've worked on vision, robotics, medicine, reasoning, and science problems. I'm especially interested in getting AI to work with hard-to-reach knowledge, the kind that's one-of-a-kind, restricted, and rarely written down more than once. The goal is to make these models genuinely useful and reliable in places where mistakes are costly. Two questions drive most of my work.

How can we adapt large foundation models efficiently? I work on post-training methods, like parameter-efficient fine-tuning, prompt and representation tuning, and model fusion, that let a pretrained model pick up new tasks with very little data and compute, and few trainable parameters.

How can we make them reliable in the real world? I build robust, multimodal systems using fast-and-slow reasoning, calibration, and safety methods, so they stay dependable under distribution shift and adversarial conditions, and hold up when they're used for decisions that matter.

Prospective Students

I take 1–2 incoming PhD students each year from Computer and Information Technology. Before a funded offer, I usually start with a 4–6 month internship alongside one of my senior PhD students, targeting a flagship conference, so we can both see whether it's a good fit. I'm demanding most of the time and occasionally short-tempered, but I'm working on it.

Alumni

Services

  • Area Chair, The Association for Computational Linguistics (ACL), since 2026
  • Area Chair, The Conference on Computer Vision and Pattern Recognition (CVPR), since 2023
  • Senior PC, The International Joint Conference on Artificial Intelligence (IJCAI), since 2023
  • Senior PC, The Association for the Advancement of Artificial Intelligence (AAAI), since 2023
  • Senior Associate Editor, IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), since 2026
  • Associate Editor, Neurocomputing, since 2024

Recent Publications

  • Changyu Liu, James Chenhao Liang, Wenhao Yang, Yiming Cui, Jinghao Yang, Tianyang Wang, Qifan Wang, Dongfang Liu, Cheng Han. A-SelecT: Automatic Timestep Selection for Diffusion Transformer Representation Learning. CVPR 2026. [paper]
  • Shirou Jing, Chunshu Wu, Chuan Liu, Arghavan Bahadorinejad, Feitong Qiao, Dongfang Liu, Tong Geng. InfoDLM: an Information-Adaptive Framework for Discrete Diffusion Language Model Pretraining. ICML 2026. [paper]
  • Feng He, Zhenting Wang, Qifan Wang, Qiang Guan, Dongfang Liu*, Ruixiang Tang, Qiankun Li. HIVE: Understanding Post-Hallucination Reasoning in Vision Language Models. ECCV 2026. [paper]
  • Yiyang Liu, James Chenhao Liang, Heng Fan, Wenhao Yang, Yiming Cui, Xiaotian Han, Lifu Huang, Dongfang Liu, Qifan Wang, Cheng Han. All You Need is One: Capsule Prompt Tuning with a Single Vector. NeurIPS 2026. [paper]
  • Runjia Zeng, James Chenhao Liang, Cheng Han, Zhiwen Cao, Jiahao Liu, Xiaojun Quan, Yingjie Victor Chen, Lifu Huang, Tong Geng, Qifan Wang, Dongfang Liu*. Probabilistic Token Alignment for Large Language Model Fusion. NeurIPS 2026. [paper]
  • Yijun Hu, Bing Fan, Xin Gu, Haiqing Ren, Dongfang Liu, Heng Fan, Libo Zhang. Robust Ego-Exo Correspondence with Long-Term Memory. NeurIPS 2026. [paper]
  • Changyu Liu, Yiyang Liu, Taowen Wang, Qiao Zhuang, James Chenhao Liang, Wenhao Yang, Renjing Xu, Qifan Wang, Dongfang Liu*, Cheng Han. On-the-Fly VLA Adaptation via Test-Time Reinforcement Learning. ACL 2026. [paper]
  • Tingxu Han, Wei Song, Ziqi Ding, Ziming Li, Chunrong Fang, Yuekang Li, Dongfang Liu, Zhenyu Chen, Zhenting Wang. Debiasing LLMs by Masking Unfairness-Driving Attention Heads. ACL Findings 2026. [paper]
  • Yanshu Li, Jianjiang Yang, Ziteng Yang, Bozheng Li, Ligong Han, Hongyang He, Zhengtao Yao, Yingjie Victor Chen, Songlin Fei, Dongfang Liu, Ruixiang Tang. Make LVLMs Focus: Context-Aware Attention Modulation for Better Multimodal In-Context Learning. AAAI 2026. [paper]
  • Pegah Ahadian, Wei Xu, Dongfang Liu, Qiang Guan. Ethics of trustworthy AI in healthcare: Challenges, principles, and practical pathways. Neurocomputing, 2026. [paper]

* denotes corresponding author.

For the full list, please refer to my Google Scholar