Kai Li
| Kai Li | |
|---|---|
| Institutions | Princeton University |
| Alma mater |
Jilin University University of Science and Technology of China, Chinese Academy of Sciences Yale University |
| Doctoral advisor | Paul Hudak, Alan Perlis |
| Doctoral students | Yuanyuan Zhou |
| Known for |
Distributed Shared Memory (DSM), Data Domain Inc. |
Kai Li is a professor at the department of Computer Science in Princeton University. He is noted for his pioneering contributions to Distributed Shared Memory (DSM) and co-founding the leading storage deduplication company Data Domain Inc. which was acquired by EMC Corporation in 2009.
Li received his Ph.D. degree from Yale University in 1986 and then joined Princeton University. Prior to that, he received his B.S. degree from Jilin University and M.S. degree from University of Science and Technology of China, Chinese Academy of Sciences.
In 1986, Kai Li published his PhD dissertation entitled "Shared Virtual Memory on Loosely Coupled Microprocessors", thus opening up the field of research that is now known as Distributed Shared Memory (or DSM) which allows users to program using a shared-memory programming model on clusters. Since this work, there has been a huge amount of work done to extend the idea to other areas (e.g., distributed object based systems and operating systems) and to improve DSM's performance. After joining Princeton, Li himself also led the Scalable High-performance Really Inexpensive MultiProcessor (SHRIMP) project which investigats how to build high-performance servers on a cluster.
During his Princeton career, Li co-led the Scalable I/O project which attacks I/O bottleneck problems for supercomputers. His work with protected user-level communication has contributed significantly to the Remote Direct Memory Access (RDMA) mechanism and Virtual Interface Architecture standard and Infiniband standard, which are the communication mechanism for the Direct Access File System (DAFS).
Li also led the Scalable Display Wall project which explores how to build and use a high-resolution, wall-size display system to visualize massive datasets. Recently, he has been working with colleagues at Stanford on a large well-labelled image dataset called Imagenet to help computer vision community develop object recognition and classification methods for large image data. More recently, he has been working with colleagues at Princeton on developing methods to efficiently manage and analyze the vast amount of data generated by increasingly sophisticated research in fields ranging from genomics to neuroscience.
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