Jie Cao

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Jie Cao

Assistant Professor

Email: jie.cao@ou.edu
Office: Devon Energy Hall, 205
Personal Website

Education

PhD, Computer Science
University of Utah

MS, Computer Science
Huazhong University of Science and Technology

BS, Information Security
Huazhong University of Science and Technology

Research Focus

Natural Language Processing
Machine Learning
Dialogue and Discourse
Structured Prediction
Trustworthy AI for Education and Healthcare

Experience

Assistant Professor, School of Computer Science, University of Oklahoma, 2024 - present
Postdoctoral Research Associate, NSF AI Institute for Student-AI Teaming (iSAT) at the University of Colorado Boulder, 2022-2024
Research Scientist Interns at WeChat AI, Amazon, Software Engineer at Baidu, Alibaba, Sohu

About

Dr. Jie Cao is an assistant professor at the School of Computer Science at the Gallogly College of Engineering, University of Oklahoma. Before joining OU, he was a post-doctoral researcher at the NSF AI Institute for Student-AI Teaming (iSAT) at the University of Colorado Boulder. He obtained his Ph.D. from the Kahlert School of Computing at the University of Utah.

Dr. Cao's research primarily focuses on natural language processing and machine learning, particularly in dialogue and structured prediction. His research aims to facilitate modularized AI systems tailored for future large-scale AI projects that demand increasing collaboration and adaptability, which covers fundamental research on formal language representation, neuro-symbolic methods, latent discrete structures, multimodal/multiagent modeling, etc. His research has been applied to multiple interdisciplinary projects beyond NLP, such as representation learning on database workloads, psychotherapy observers for motivational interviews, AI partners in collaborative learning, and math teaching/tutoring settings.

Selected Publications

Baptiste Moreau-Pernet, Yu Tian, Sandra Sawaya, Peter Foltz, Jie Cao, Brent Milne, and Thomas Christie. 2024. Classifying Tutor Discursive Moves at Scale in Mathematics Classrooms with Large Language Models. In Proceedings of the Eleventh ACM Conference on Learning @ Scale, pages 361–365. Association for Computing Machinery.
Jie Cao, Ananya Ganesh, Jon Cai, Rosy Southwell, Magerate Perkoff, Michael Regan, Katharina Kann, James Martin, Martha Palmer, and Sidney D’Mello. 2023. A Comparative Analysis of Automatic Speech Recognition Errors in Small Group Classroom Discourse. In Proceedings of the 31st ACM Conference on User Modeling Adaptation and Personalization, pages 250-262.
Jie Cao, Rachel Dickler, Marie Grace, Alessandro Roncone, Leanne Hirshfield, Marilyn Walker, and Martha Palmer. 2023. Designing an AI Partner for Jigsaw Classrooms. Workshop on Language-Based AI Character Interaction with Children.
E. Margaret Perkoff, Abhidip Bhattacharyya, Jon Cai, and Jie Cao. 2023. Comparing Neural Question Generation Architectures for Reading Comprehension. 18th Workshop on Innovative Use of NLP for Building Educational Applications, 2023.
Jon Cai, Brendan D. King, Margaret Perkoff, Shiran Dudy, Jie Cao, Marie Grace, Natalia Wojarnik, Ganesh Ananya, James Martin, Martha Palmer, Marilyn Walker, and Jeffrey Flanigan. 2022. Dependency Dialogue Acts — Annotation Scheme and Case Study. The 13th International Workshop on Spoken Dialogue Systems Technology.
Debjyoti Paul*, Jie Cao*, Feifei Li, and Vivek Srikumar. 2021. Database workload characterization with query plan encoders. In Proceedings of the VLDB Endowment, 15(4):923–935.
Jie Cao and Yi Zhang. 2021. A Comparative Study on Schema-Guided Dialogue State Tracking. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 782–796.
Jie Cao, Yi Zhang, Adel Youssef, and Vivek Srikumar. 2019. Amazon at MRP 2019: Parsing Meaning Representations with Lexical and Phrasal Anchoring. In Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the Conference on Natural Language Learning(CoNLL), pages 138–148.
Jie Cao, Michael Tanana, Zac Imel, Eric Poitras, David Atkins, and Vivek Srikumar. 2019. Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5599-5611.