Xiangyao Yu

Xiangyao Yu

Assistant Professor
Database Group
Computer Sciences Department, University of Wisconsin-Madison
yxy AT cs.wisc.edu
Room 4361, 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) cloud-native databases, (2) new hardware for databases, and (3) core DB techniques in both transaction and analytics.

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) Cloud-native databases, (II) New hardware for databases, and (III) Core DB techniques. Below are some sample projects.

Research Area I: Cloud-Native Databases

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.

Cloud-native data warehouse: Cloud-native transaction processing:

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: Core DB Techniques

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. Hybrid transactional/analytical processing (HTAP): HTAP systems have gained popularity as they combine OLAP and OLTP processing to reduce administrative and synchronization costs between dedicated systems. This brings new challenges in data freshness and performance isolation between transactional and analytical processing. Predicate Transfer: Predicate transfer is a method that optimizes multi-join queries by pre-filtering tables to reduce the join input size. Predicate transfer is inspired by the seminal theoretical results by Yannakakis but leverage Bloom Filters to become more practical.

Peer-Reviewed Publications



Current PhD Students: Master Student: Undergraduate Students:

Here is a list of my teaching experiences at UW-Madison: