Jianguo Wang

Assistant Professor

Department of Computer Science
Purdue University
West Lafayette, Indiana

Email: csjgwang@purdue.edu
Office: LWSN 1123H
Phone: (765) 496-0726

Biography

Jianguo Wang is currently a tenure-track assistant professor in the Department of Computer Science at Purdue University.

Prior to joining Purdue, he worked at Zilliz on Milvus, a purpose-built vector database system, which has been used in many data science applications including ChatGPT. Before that, he worked at Amazon Web Services (AWS) on Amazon Aurora, a cloud-native database system. He also interned at Microsoft Research, Oracle, and Samsung on various aspects of database systems.

He obtained his PhD degree in Computer Science from the University of California, San Diego, his MPhil degree from The Hong Kong Polytechnic University, and his Bachelor's degree from Zhengzhou University, China.

Research Interests

In general, I'm interested in new database systems for non-traditional architecture and non-traditional data. Currently, I'm leading a team of students working on:

Disaggregated Databases (for the Cloud)
- Memory & Storage Disaggregation
- Distributed Shared-Memory & Distributed Shared-Storage Databases
- [Recent Papers: VLDB'23, SIGMOD'23, and ICDE'23]
Vector Databases (for Large Language Models)
- Supporting Vector Data Management inside Relational Databases
- Serverless Vector Databases for Cost-Efficiency and Elasticity
- [Recent Papers: Vector DB Survey, SIGMOD'21 and ICDT'19]

Working Experience

Assistant Professor, Department of Computer Science, Purdue University 01/2021 - Present

Milvus Vector Database Team, Zilliz, Remote 07/2020 - 12/2020
- Project: Milvus: A Purpose-Built Vector Database System

Aurora Cloud Database Team, Amazon AWS, Palo Alto, CA 03/2019 - 07/2020
- Project: Write Forwarding in Amazon Aurora Globally-Distributed Database
- U.S. Patent Filed as Co-Inventor

Recent Publications

Ruihong Wang, Jianguo Wang, Stratos Idreos, M. Tamer Özsu, Walid G. Aref.
The Case for Distributed Shared-Memory Databases with RDMA-Enabled Memory Disaggregation.
Proceedings of Very Large Data Bases Conference (VLDB), pages 15-22, 2023.

Jianguo Wang, Qizhen Zhang.
Disaggregated Database Systems.
Proceedings of ACM Conference on Management of Data (SIGMOD), Tutorial Track (slides), pages 37-44, 2023.