The Chief Scientist of Shenzhen Institute of Computing Sciences
- Foreign Member of the Chinese Academy of Sciences,2019.
- Fellow of the Royal Society, 2018.
- Member of the Academia Europaea , 2017.
- Fellow of the ACM , 2012.
- Fellow of the Royal Society of Edinburgh, 2011.
Wenfei Fan is a Foreign Member of the Chinese Academy of Sciences, a Fellow of the Royal Society (FRS), a Member of the Academia Europaea (MAE), a Fellow of the Royal Society of Edinburgh (FRSE), and a Fellow of the ACM. He is the second FRS from mainland China and is the only Chinese among more than 30 fellows in the field of computer science. He is also one of the two scholars who have collected major awards from all four leading international database theory and systems conferences (SIGMOD/PODS, VLDB, ICDE) in the history of database research, with more than one award from PODS, VLDB and ICDE. He is the recipient of 18 international awards, including the Roger Needham Award 2008 and the Royal Society Wolfson Fellowship 2018.
Academician Fan has made fundamental contributions to both foundations and practice of database management (cf. Royal Society). He is recognized for opening up the field of constraints for semi-structured data, reshaping the field of data quality, and initiating the formal approach to scalable querying of big data.
The theory of bounded evaluation he proposed has helped companies of limited resources overcome limitations of conventional database systems and build capacity for big data analysis.He led the design and development of GRAPE, a parallel graph query engine, which advocates a parallel model based on a simultaneous fixed-point computation in terms of partial and incremental evaluation, for parallelizing sequential graph computations.The GRAPE-based product GraphScope in collaboration with Alibaba has now become a popular open-source system on Github. At SICS, he is leading the development of three systems, namely YanshanDB, Rock and FishingTown, for big data analytics, data cleaning and association analyses, targeting the volume, variety, velocity, veracity and value of big data.