Dr. Min Chi is an Assistant Professor in the Department of Computer Science at NC State University. She joined the department in August 2013 as a Chancellor’s Faculty Excellence Program cluster hire in the Digital Transformation of Education. Dr. Chi earned her Ph.D. and M.S. in the Intelligent System Program from the University of Pittsburgh, and B.E. in the Information Science and Technology from Xi'an Jiaotong University, China. She was a Post Doctoral Fellow in the Machine Learning Department at Carnegie Mellon University, and Human Sciences and the Technologies Advanced Research Institute at Stanford University. She received the Best Paper Award at the Intelligent Tutoring Systems Conference and Best Student Paper Award at the User Modeling, Adaptation and Personalization Conference in 2010, and Best Student Paper Award at the Intelligent Tutoring Systems Conference in 2008. She also received Best Poster Award at the Educational Data Mining Conference in 2008. Her specialty is applied machine learning and data mining, and her research lies at the intersection of educational data mining and human-computer interaction. Dr. Chi research primarily focuses on applying machine learning and data mining methods to improve human learning and exploring new machine learning and data mining challenges posed by learning and social science.
- Advanced Learning Technologies
- Artificial Intelligence and Intelligent Agents
- Data Sciences and Analytics
- Graphics, Human Computer Interaction, & User Experience
- Ph.D. 2009 University of Pittsburgh Intelligent Systems
- M.S. 2006 University of Pittsburgh Intelligent Systems
- B.E. 1999 Xi’an Jiaotong University, China Information Science and Technology
- Best Paper Award - 10th International Conference on Intelligent Tutoring Systems, 2010
- James Chen Best Student Paper Award - 18th International Conference on UMAP, 2010
- Best Student Paper Award - 9th International Conference on Intelligent Tutoring Systems, 2008
- Best Poster Award - 1st International Conference on Educational Data Mining, 2008
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