Xiangyao Yu

Xiangyao Yu

Assistant Professor
Database Group
Computer Sciences Department, University of Wisconsin-Madison
yxy AT cs.wisc.edu
Room 4385, 1210 W. Dayton St, Madison WI 53706
[C.V.] [Google Scholar]



I am an Assistant Professor in the Computer Sciences Department at University of Wisconsin-Madison.

Before joining UW-Madison, I was a Postdoctoral Associate in the database group at CSAIL, MIT working with Prof. Michael Stonebraker and Prof. Samuel Madden. I completed my Ph.D. in Computer Science at MIT in 2017 working with Prof. Srinivas Devadas. I earned my Bachelor of Science (B.S.) in 2012 from Institute of Microelectronics at Tsinghua University, Beijing, China.

I work on database systems and currently focus on (1) transactions and HTAP, (2) new hardware for databases, and (3) cloud-native databases.

I am actively looking for Postdocs and Graduate/Undergraduate students interested in database systems. Please email me your CV if you are interested in working with me.




My research actively focuses in three areas: (I) Transactions and HTAP, (II) New hardware for databases, and (III) Cloud-native databases. Below are some sample projects.

Research Area I: Transactions and HTAP

Scalable transaction processing on multicore CPUs: Computer architectures are moving towards manycore machines with dozens or even hundreds of cores on a single chip. We develop new techniques for modern database management systems (DBMSs) to make transaction processing scalable for this level of massive parallelism. Scalable distributed transaction processing: Online transaction processing (OLTP) DBMSs are increasingly deployed on distributed machines. Compared to a centralized systems, distributed DBMSs face new challenges including extra network latency, requirements of high availability and distributed commitment.

Research Area II: New Hardware for Databases

GPU database: GPU is a promising solution for data analytics, driven by the rapid growth of GPU computation power, GPU memory capacity and bandwidth, and PCIe bandwidth. We investigate techniques that can fully unleash the power of GPU in online analytical processing (OLAP) databases. Advanced network technologies: Network is a bottleneck in distributed databases. Emerging network technologies including RDMA, SmartNIC, and programmable switches support different levels of computation within the network and are promising in accelerating distributed databases.

Research Area III: Cloud-Native Databases

Cloud-native data warehouse: Databases are moving to the cloud driven by desirable properties such as elasticity, high-availability, and cost competitiveness. Modern cloud-native databases adopt a unique storage-disaggregation architecture, where the computation and storage are decoupled. This architecture brings new challenges (e.g., network bandwidth bottleneck) and opportunities in DBMS design.











Peer-Reviewed Publications

Patents

Thesis

PhD Students: Master Student: Intern: Undergraduate Students: Alumni:





Here is a list of my teaching experiences: