PNNL

Yinghui Wu is an assistant professor in the Department of Computer and Data Sciences at Case Western Reserve University’s (CWRU) School of Engineering. He works in data management and analytics with emphasis on knowledge extraction and exploration from complex and big data. The CWRU database group devises and designs novel data management and analysis paradigms and tools to support data and knowledge exploration in big data. His current research has a focus on cost-effective, scalable data systems for knowledge and data exploration. Wu’s research is supported by the National Science Foundation, Pacific Northwest National Laboratory (PNNL), the US Department of Agriculture, and other industry partners.

Wu currently holds joint position as staff scientist at PNNL and as faculty at Washington State University. He has held the positions of research scientist at the University of California, Santa Barbara’s Department of Computer Science, and visiting scholar at the Army Research Lab and Raytheon BBN Technologies. Wu has also served as a member of Network Science Collaborative Technique Alliance.

• Databases
• Data and knowledge management
• Data mining
• Information quality.

• PhD in Computer Science, The University of Edinburgh, 2011
• BS in Computer Science, Peking University, 2007
• BS in Economics, Peking University, 2007

• Distinguished Reviewer Award, The Very Large Databases (VLDB) Journal (2019)
• Special Interest Group on Management of Data Research (SIGMOD) Highlight Award, Association for Computing Machinery (ACM) (2018)
• SIGMOD Best Paper Award, ACM (2017)
• Best Demo Award, VLDB International Conference (2017)
• Faculty Research Award, Google (2015)
• Best Paper Runner Up Award, Institute of Electrical and Electronics Engineers (IEEE) International Conference on Data Engineering (2014)
• Best Poster Award, IEEE International Conference on Data Engineering (2013)

2022

Nandanoori S., S. Guan, S. Kundu, S. Pal, K. Agarwal, Y. Wu, and S. Choudhury. 2022. "Graph Neural Network and Koopman Models for Learning Networked Dynamics: A Comparative Study on Power Grid Transients Prediction." IEEE Access 10. PNNL-SA-161313. doi:10.1109/ACCESS.2022.3160710

2021

Ahmed A., K.S. Sajan, A.K. Srivastava, and Y. Wu. 2021. "Anomaly Detection, Localization and Classification using Drifting Synchrophasor Data Streams." IEEE Transactions on Smart Grid. PNNL-SA-159464. doi:10.1109/TSG.2021.3054375

Guan S., H. Ma, S. Choudhury, and Y. Wu. 2021. "GEDet: Detecting Erroneous Nodes with A Few Examples." In Proceedings of the 47th International Conference on Very Large Data Bases (VLDB 2021), August 16-20, 2021, Virtual, Online, 14, 2875 - 2878. PNNL-SA-160866. doi:10.14778/3476311.3476367

Song Q., P. Lin, H. Ma, and Y. Wu. 2021. "Explaining Missing Data in Graphs: A Constraint-based Approach." In IEEE 37th International Conference on Data Engineering (ICDE 2021), April 19-22, 2021, Chania, Greece, 1476-1487. Piscataway, New Jersey: IEEE. PNNL-SA-159919. doi:10.1109/ICDE51399.2021.00131

Song Q., H. Ma, P. Lin, and Y. Wu. 2021. "GRIP: Constraint-based Explanation of Missing Answers for Graph Queries." In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD 2021), June 20-25, 2021, Virtual, Online, edited by 2779 - 2783. New York, New York: Association for Computing Machinery. PNNL-SA-160507. doi:10.1145/3448016.3452758

2018

Choudhury S., S. Purohit, P. Lin, Y. Wu, L.B. Holder, and K. Agarwal. 2018. "Percolator: Scalable Pattern Discovery in Dynamic Graphs." In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (WSDM 2018), February 5-9, 2018, Los Angeles, California, 759-762. New York, New York: ACM. PNNL-SA-128916.  doi:10.1145/3159652.3160589