Name:Shenglin Zhang

Bio:

Shenglin Zhang is an Associate Professor at the College of Software, Nankai University. He is also the vice dean of this college. His research primarily focuses on AIOps, encompassing anomaly detection, failure diagnosis, root cause analysis, and failure prediction in software and network service management. He has published over 100 papers at prestigious international conferences such as ATC, WWW, VLDB, KDD, FSE, SIGMETRICS, CoNEXT, INFOCOM, IJCAI, ISSRE, and IWQOS, as well as in peer-reviewed journals including JSAC, TON, TOSEM, TC, and TSC. It is noteworthy that he was awarded the “Young Academic Leaders Training Program” at Nankai University and received the Best Paper Awards at ISSRE in 2024, 2023, and 2018. As the principal investigator, he has led 20+ projects funded by entities such as the NSFC, China Postdoctoral Science Foundation, Huawei, ByteDance, Alibaba, Tencent, ZTE, and CERNET. From 2018 to 2019, he served as a visiting scholar at Alibaba.

Before joining Nankai University, Shenglin Zhang earned his Ph.D. in Computer Science and Technology from Tsinghua University, where he was honored with the “Outstanding Doctoral Dissertation of Tsinghua University” award. His advisors were Professors Dan Pei and Ying Liu. In 2016, he was a visiting scholar at the School of Computer Science, Georgia Institute of Technology, under the supervision of Professor Jun (Jim) Xu. Prior to his studies at Tsinghua, he received his B.S. in Network Engineering from Xidian University in 2012. Between 2014 and 2017, he completed an internship at Baidu, one of the world’s leading search engines. From November 2017 to May 2018, alongside Professor Dan Pei, he initiated the CCF International AIOps Challenge, marking the first competition in the field of AIOps. They also successfully organized the inaugural AIOps Workshop on May 19, 2018. In 2023, he served as the TPC Chair for the 6th CCF International AIOps Challenge.

Country:China

Affiliation:Nankai University

Research interests:AIOps, anomaly detection, failure diagnosis, failure prediction, root cause localization