Research Interests

I aspire to contribute new insights to the fields of machine learning and data mining. My research interests broadly lie in AI Safety. My research emphasizes exploring the adversarial robustness of machine learning techniques in applications related to discrete data, e.g., text in natural language processing, diagnosis codes in healthcare informatics, and image-text pairs in multi-modal learning.

Selected Works

Understanding the Robustness of Machine Learning Models (e.g., Language Models)

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).

Empowering Health Risk Prediction Models with Better Representation and Interpretability

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).

Applications Related to Multi-Modal Learning

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).

Tutorials

 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).

Teaching Experiences

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.

Industry Experiences

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.

Professional Service

Conference Reviewer for ECCV '24, ECML-PKDD '24, ICML '24, KDD '24, IJCAI '24, AISTATS '24, CVPR '24, SynData4CV '24 (CVPR Workshop), AAAI '24, WACV '24, ICLR 2024 R2-FM Workshop, EMNLP Industry Track '23, EMNLP '23, DALI '23 (MICCAI Workshop), BIOKDD '23 (KDD Workshop), ECML-PKDD '23, ACL '23, KDD '23, IJCAI '23, WACV '23, AAAI '23, and AAAI '21.

Journal Reviewer for IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Emerging Topics in Computational Intelligence, Neurocomputing, Data-centric Machine Learning Research, and Heliyon.

Student Volunteer for KDD '23, and KDD '22.

Awards

Student Travel Award by NeurIPS '23, KDD '23, CIKM '21, and WebConf '21.

Graduate Student Travel Award by College of IST, Penn State in 2022.