Author | Edited by Li Mei | Chen Caixian
Recently, the ACM SIGMOD official website announced the 2022 SIGMOD Jim Gray Doctoral Dissertation Award, which was awarded to UC Berkeley doctoral graduate Chenggang Wu .
After the first Chinese (Yao Ban Assistant Professor Zhang Huanchen ) won the award last year, this year is the second Chinese face to be awarded the award!
The ACM SIGMOD Doctoral Dissertation Award was established in 2006 and is awarded annually to recognize outstanding research achievements of doctoral candidates in the database field. In 2008, the ACM Council renamed the award in honor of 1998 Turing Award winner and database guru Jim Gray.
The winner, Chenggang Wu, is currently the co-founder and CTO of Aqueduct, a software-as-a-service (SaaS) startup building machine learning predictive infrastructure.
Chenggang Wu is an undergraduate student in the Department of Computer Science at Brown University under the supervision of Professors Stan Zdonik and Tim Kraska. In 2020, he received his Ph.D. from the University of California, Berkeley, under the tutelage of Professor Joseph M. Hellerstein. During his Ph.D., he was engaged in research work at RISE Lab (formerly known as Berkeley AMP Lab).
As can be seen from the list of previous winners, Chenggang Wu is the third doctoral student at Berkeley to receive the award, and two other doctoral students, Peter Bailis and Boon Thau Loo, won the award in 2017 and 2007, respectively. They are also doctoral students supervised by Professor Joseph M. Hellerstein. Joseph M. Hellerstein’s paper “Architecture of a Database System” in collaboration with Michael Stonebraker and James Hamilton is a classic in the database field.
Chenggang Wu’s research areas mainly include: data-centric systems, distributed systems and machine learning techniques applied to database problems. His research papers have been selected for the top conferences in the database field, VLDB 2019 (Very Large Data Bases) and ICDE 2018 (International Conference On Data Engineering), and won best-of-conference citations. He is a frequent PC member and reviewer for conferences and journals such as SIGMOD, ICDE, VLDBJ and TKDE.
He led the development of the ultra-high-speed, flexible, consistent, self-scaling and low-cost key-value storage database Anna, which was known as the “world’s fastest” KVS database at the time, and aroused heated discussions in the industry once it was launched. Anna was later used as a storage backplane to build Cloudburst, the next-generation serverless computing platform.
Chenggang Wu’s award-winning doctoral dissertation titled “The Design of Any-scale Serverless Infrastructure with Rich Consistency Guarantees” developed design principles for building serverless infrastructures that enable superior performance, smooth and seamless scalability, and rich consistency guarantee.
Paper address: https://ift.tt/udt3L8W
Specifically, this work proposes two key ideas to achieve these goals: lattice-based coordination-free consistency and LDPC (logical decomposition and physical juxtaposition). These ideas are validated through formal guarantees for consistency, and the performance and scalability achieved by the Anna key-value store database and the Cloudburst serverless computing system. Papers also demonstrate applications in areas such as machine learning model serving, social networking, and robotics. The significance of this work is that it timely addresses today’s challenging problems in distributed data management and provides lessons for researchers and practitioners.
Reference link: https://ift.tt/MeoJOqF http://cgwu.io/ 
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