Bio:
Jing Gao is an Associate Professor in the Elmore Family School of Electrical and Computer Engineering, Purdue University. Before joining Purdue in January 2021, she was an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo (UB), State University of New York. She received her PhD from Computer Science Department, University of Illinois at Urbana Champaign in 2011.

Jing is broadly interested in data and information analysis with a focus on data mining. In particular, she is interested in information veracity analysis,  multi-source data analysis, knowledge graphs, large language model, data and model efficiency, fairness and interpretation, transfer learning, federated learning, crowdsourcing, data stream mining, and anomaly detection. Her current research focus is on AI trustworthiness, safety and efficiency. She has published over 200 papers in referred journals and conferences. Her publications have received over 20,000 citations and her H-index is 70. She is an editor of ACM Transactions on Intelligence Systems and Technology (TIST) and IEEE Transactions on Knowledge and Data Engineering (TKDE). She serves as the Program Committee Co-Chair of the 2025 IEEE BigData Conference and the 2024 SIAM Conference on Data Mining. She is a recipient of NSF CAREER award, IBM faculty award, ICDM Tao Li Award, SDM/IBM Early Career Award and UIUC CS Early Career Academic Achievement Alumni Award. She is a University Faculty Scholar.