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Jingyuan Yang is an assistant professor of information systems and operations management at the Costello College of Business, ���ӽ紫ý. She earned her doctoral degree in Information Technology from the Business School at Rutgers, the State University of New Jersey. Her general areas of research focus on data mining, machine learning, recommender systems, and business analytics. Her research has been published as full papers in premier journals such as INFORMS Journal on Computing (��������),��IEEE Transactions on Knowledge and Data Engineering (TKDE), Neural Networks, and top conferences of knowledge discovery and data mining, including the IEEE International Conference on Data Mining (ICDM), the SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), and the IEEE International Conference on Big Data (Big Data).

Research Interests

• Data Mining and Knowledge Discovery
• Marketing Analytics
• Business Intelligence

Education

• Ph.D. - Rutgers, the State University of New Jersey
• M.S. - Rutgers, the State University of New Jersey
• B.A. - Nankai University, Tianjin, China

Research and Awards

• Published an article titled, “Iterative Prediction-and-Optimization for E-logistics Distribution Network Design” (coauthored with Junming Liu, Weiwei Chen, Hui Xiong, and Can Chen) in INFORMS Journal on Computing (IJOC), Accepted, 2021.
• Published an article titled, “Cross-Lingual Knowledge: Transferring by Structural Correspondence and Space Transfer” (coauthored with D. Wang, J. Wu, B. Jing, W. Zhang, X. He, and H. Zhang) in IEEE Transactions on Cybernetics (IEEE Trans. Cybern, impact factor: 11.079), Accepted, 2021.
• Published an article titled, “Exploiting Interpretable Patterns for Flow Prediction in Dockless Bike Sharing Systems” (coauthored with Jingjing Gu, Qiang Zhou, Yanchi Liu, Fuzhen Zhuang, and Hui Xiong) in IEEE Transactions on Knowledge and Data Engineering (TKDE, impact factor: 5.876), Accepted, 2020.
• Published an article titled, “NeuO: Exploiting the sentimental bias between ratings and reviews with neural networks, Neural Networks” (coauthored with Yuanbo Xu, Yongjian Yang, Jiayu Han, En Wang, Fuzhen Zhuang, and Hui Xiong) in Neural Networks (NEURAL NETWORKS, impact factor: 7.197), Accepted, 2019.
• Published an article titled, “Dynamic Talent Flow Analysis with Deep Sequence Prediction Modeling” (coauthored with Huang Xu, Zhiwen Yu, Hui Xiong, and Hengshu Zhu) in IEEE Transactions on Knowledge and Data Engineering (TKDE, impact factor: 5.876), Accepted, 2018.
• Published an article titled, “A Unified View of Social and Temporal Modeling for B2B Marketing Campaign Recommendation” (coauthored with Chuanren Liu, Mingfei Teng, Ji Chen, and Hui Xiong) in IEEE Transactions on Knowledge and Data Engineering (TKDE) 2017.
• Published and presented the paper titled, “Buyer Targeting Optimization: A Unified Customer Segmentation Perspective" at the 2016 IEEE International Conference on Big Data (coauthored with Chuanren Liu, Mingfei Teng, March Liao, and Hui Xiong).
• Published the paper titled, “Talent Circle Detection in Job Transition Networks" at the SIGKDD International Conference on Knowledge Discovery and Data Mining (coauthored with Huang Xu, Zhiwen Yu, Hui Xiong, and Hengshu Zhu).
• Published and presented the paper titled, “Temporal Behavior Patterns and Community Networks for B2B Marketing Campaign Recommendations" the 2015 IEEE International Conference on Data Mining (coauthored with Chuanren Liu, Mingfei Teng, March Liao, Vivian Zhu, and Hui Xiong).
• Published the paper titled, “Station Site Optimization in Bike Sharing Systems" at the 2015 IEEE International Conference on Data Mining (coauthored with Junming Liu, Qiao Li, Meng Qu, Weiwei Chen, and Hui Xiong).