My past research in PhD and PostDoc was focused on supporting user-centered analytical interfaces at scale by reshaping modern data analytical stacks on three aspects:
- Interactivity: how do we help end-users consume visual results with desirable properties and performance preserved?
- Scalability: how do we scale the execution of user-centered analytical interfaces to multiple machines?
- Cost: how do we reduce resource usage while not sacrificing performance?
Please check out this
video for an overview of my past research. The major projects I worked on include:
- Transactional Panorama: a conceptual framework for user perception in analytical
visual interfaces
- Taco: efficient and compact spreadsheet formula graphs
- Modin: a scalable dataframe system
- Lux:
a visualization recommendation library for data scientists to perform easy data exploration in dataframe workflow
- CrocodileDB:
a new database architecture that exploits time slackness to enable new resource-efficient query execution (video)
- ACC:
a high-performance main-memory database that adaptively choosees and mixes multiple concurrency control protocols