I am a third-year Ph.D. student in the Department of Computer Science at University of Maryland, College Park, advised by Prof. Tianyi Zhou. My research interests are data-centric AI, computer vision (CV), vision-language models (VLMs), especially exploring synthetic data generation to improve model training and evaluation process.

I got my M.Eng. degree in Industrial Engineering and Operations Research at University of California, Berkeley. Before joining UCB, I obtained my B.Eng. degree in Industrial Engineering at Tsinghua University.

Publications

1. Yijun Liang* , Ming Li, Chenrui Fan, Ziyue Li, Dang Nguyen, Kwesi Adu Cobbina, Shweta Bhardwaj, Jiuhai Chen, Fuxiao Liu, Tianyi Zhou, *“ColorBench: Can VLMs See and Understand the Colorful World? A Comprehensive Benchmark for Color Perception, Reasoning, and Robustness“, Neurips2025, [Paper].
2. Yijun Liang, Shweta Bhardwaj, Tianyi Zhou, “Diffusion Curriculum: Synthetic-to-Real Generative Curriculum Learning via Image-Guided Diffusion“, ICCV2025, [Paper].
3. Ming Li, Pei Chen, Chenguang Wang, Hongyu Zhao, Yijun Liang, Yupeng Hou, Fuxiao Liu, Tianyi Zhou, “Mosaic-IT: Free Compositional Data Augmentation Improves Instruction Tuning“, arXiv, [Paper].
4. Ming Li, Chenguang Wang, Yijun Liang, Xiyao Wang, Yuhang Zhou, Xiyang Wu, Yuqing Zhang, Ruiyi Zhang, Tianyi Zhou. “CaughtCheating: Is Your MLLM a Good Cheating Detective? Exploring the Boundary of Visual Perception and Reasoning“, arXiv, [Paper].
5. Zongxia Li, Ishani Mondal, Yijun Liang, Huy Nghiem, Jordan Lee Boyd-Graber, “PEDANTS: Cheap but Effective and Interpretable Answer Equivalence“, EMNLP 2024, [Paper].
6. Shuai Peng, Di Fu, Yijun Liang, Liangcai Gao, Zhi Tang, “Geodrl: A self-learning framework for geometry problem solving using reinforcement learning in deductive reasoning“, ACL 2023, [Paper].
7. Shuai Peng, Di Fu, Yong Cao, Yijun Liang, Gu Xu, Liangcai Gao, Zhi Tang, “Compute Like Humans: Interpretable Step-by-step Symbolic Computation with Deep Neural Network“, KDD 2022, [Paper].