MUCHAO YE
I am an Assistant Professor in the Department of Computer Science at the University of Iowa. I completed my Ph.D. at Penn State and obtained my B.Eng. degree at South China University of Technology.
My research interests broadly lie in the fields of Artificial Intelligence and Machine Learning. My recent research interest is in the reasoning ability of vision-language models.
Email: muchao-ye@uiowa.edu
Our work using vision-language models for video anomaly detection, a challenging computer vision task where anomalies are context-dependent and varied, is accepted in CVPR '25. Please read the paper and browse the project page if interested! The code is available here.
Muchao Ye, Weiyang Liu, and Pan He. 2025. VERA: Explainable Video Anomaly Detection via Verbalized Learning of Vision-Language Models. Accepted in CVPR '25.
We organize a tutorial named Towards Safe Multi-Modal Learning: Unique Challenges and Future Directions in ICCV! The slides are here.
Talk slides on Evaluating and Certifying the Adversarial Robustness of Language Models are available! This talk is mostly about my work on the adversarial robustness of language models since 2021.
PhD Students: Mitchell Piehl and Zihao Zhao
Undergraduate Students: Brainna Bowers and Preston Yoshino (from Grinnell College)
Muchao Ye, Weiyang Liu, and Pan He. 2025. VERA: Explainable Video Anomaly Detection via Verbalized Learning of Vision-Language Models. Accepted in CVPR '25.
Muchao Ye, Quanzeng You, and Fenglong Ma. 2022. QUALIFIER: Question-Guided Self-attentive Multi-modal Fusion Network for Audio Visual Scene-Aware Dialog. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV '22).
Xingyi Yang, Muchao Ye, Quanzeng You, and Fenglong Ma. 2021. Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL '21).
Muchao Ye, Xiaojiang Peng, Weihao Gan, Wei Wu, and Yu Qiao. 2019. AnoPCN: Video Anomaly Detection via Deep Predictive Coding Network. In Proceedings of the 27th ACM International Conference on Multimedia (ACM MM '19).
Muchao Ye, Ziyi Yin, Tianrong Zhang, Tianyu Du, Jinghui Chen, Ting Wang, and Fenglong Ma. 2023. UniT: A Unified Look at Certified Robust Training against Text Adversarial Perturbation. In Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS '23).
Ziyi Yin, Muchao Ye, Tianrong Zhang, Tianyu Du, Jinguo Zhu, Han Liu, Jinghui Chen, Ting Wang, and Fenglong Ma. 2023. VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models. In Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS '23).
Muchao Ye, Jinghui Chen, Chenglin Miao, Han Liu, Ting Wang, and Fenglong Ma. 2023. PAT: Geometry-Aware Hard-Label Black-Box Adversarial Attacks on Text. In 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '23).
Muchao Ye, Junyu Luo, Guanjie Zheng, Cao Xiao, Houping Xiao, Ting Wang, and Fenglong Ma. 2022. MedAttacker: Exploring Black-Box Adversarial Attacks on Risk Prediction Models in Healthcare. In International Conference on Bioinformatics and Biomedicine 2022 (BIBM '22).
Muchao Ye, Jinghui Chen, Chenglin Miao, Ting Wang, and Fenglong Ma. 2022. LeapAttack: Hard-Label Adversarial Attack on Text via Gradient-Based Optimization. In 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22).
Muchao Ye, Chenglin Miao, Ting Wang, and Fenglong Ma. 2022. TextHoaxer: Budgeted Hard-Label Adversarial Attacks on Text. In 36th AAAI Conference on Artificial Intelligence (AAAI '22).
Muchao Ye*, Suhan Cui *, Yaqing Wang, Junyu Luo, Cao Xiao, and Fenglong Ma. 2021. MedPath: Augmenting Health Risk Prediction via Medical Knowledge Paths. In Proceedings of The Web Conference 2021. (* indicates equal contribution.)
Muchao Ye*, Suhan Cui*, Yaqing Wang, Junyu Luo, Cao Xiao, and Fenglong Ma. 2021. MedRetriever: Target-Driven Health Risk Prediction via Retrieving Unstructured Medical Text. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM '21). (* indicates equal contribution.)
Muchao Ye, Junyu Luo, Cao Xiao, and Fenglong Ma. 2020. LSAN: Modeling Long-term Dependencies and Short-term Correlations with Hierarchical Attention for Risk Prediction. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM '20).
Junyu Luo, Muchao Ye, Cao Xiao, and Fenglong Ma. 2020. HiTANet: Hierarchical Time-Aware Attention Networks for Risk Prediction on Electronic Health Records. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '20).
Xi Li, Manling Li, and Muchao Ye. 2025. Towards Safe Multi-Modal Learning: Unique Challenges and Future Directions. In ICCV 2025.
Xi Li, Ruixiang Tang, and Muchao Ye. 2025. Exploitation and Mitigation: Understanding Large-Scale Machine Learning Robustness under Paradigm Shift. In SDM 2025.
Muchao Ye. 2025. Knowledge-guided Health Risk Prediction with Electronic Health Records. In UI Healthcare Human Brain Research Laboratory.
Muchao Ye. 2024. Evaluating and Ceritifying the Adversarial Robustness of Language Models. In UI Business Analysis Seminars.
Fenglong Ma, Muchao Ye, Junyu Luo, Xiao Cao, and Jimeng Sun. 2021. Advances in Mining Heterogeneous Healthcare Data. In 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '21).
Instructor, UIowa CS:4420:0001 Artificial Intelligence, Spring 2026.
Instructor, UIowa CS:5980:0001: Topics in Computer Science II (Adversarial Machine Learning), Fall 2025.
Instructor, UIowa CS:4420:0001 Artificial Intelligence, Spring 2025.
Instructor, UIowa CS:4980:0005: Topics in Computer Science II (Adversarial Machine Learning), Fall 2024.
Teaching Assistant, Penn State IST 230: Language, Logic, and Discrete Mathematics, Fall 2023.
Teaching Assistant, Penn State DS 300: Data Privacy & Security, Spring 2023.
Teaching Assistant, Penn State IST 230: Language, Logic, and Discrete Mathematics, Spring 2023.
Teaching Assistant, Penn State IST 230: Language, Logic, and Discrete Mathematics, Fall 2022.
Applied Scientist Intern @ Amazon. Supervised by Dr. Jon Wu and Dr. Xiang Xu. May 2023 - August 2023.
Applied Scientist Intern @ Amazon. Supervised by Dr. Jon Wu, Dr. Hao Zhou and Dr. Xiang Xu. May 2022 - August 2022.
Software Engineer Intern @ NewsBreak. Supervised by Dr. Yudian Zheng and Ryan Liu. June 2021 - August 2021.
Conference Reviewer for NeurIPS '24, ICLR '25, CVPR ('25, '24), ECCV '24, ICML '24, KDD ('24, '23), AAAI ('26, '25, '24, '23, '21), IJCAI ('24, '23), AISTATS '24, ACL '23, EMNLP '23, ECML-PKDD ('24, '23), WACV ('26, '25, '24, '23), CVPR SynData4CV Workshop '24, ICLR 2024 R2-FM Workshop, ICML FM-Wild Workshop ('24), MICCAI ADSMI Workshop '24, MICCAI DALI Workshop '23, and KDD BIOKDD Workshop '23.
Journal Reviewer for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), International Journal of Computer Vision (IJCV), ACM Computing Surveys (CSUR), IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), Neural Networks, ACM Transactions on Intelligence Systems and Technology (TIST), IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI), Neurocomputing, IET Image Processing, Expert Systems With Applications, Data-centric Machine Learning Research, and Heliyon.
Conference Session Chair for SDM '25.
Guest Editor for Electronics.
Panelist for NSF.