My PhD work at Indian Institute of Technology Madras, was on Shared Cache Management. I am interested in architectures for emerging technologies, heterogeneous architectures and application specific accelerators.

McKee received her bachelor’s degree in Computer Science from Yale University, master’s from Princeton University, and doctorate from the University of Virginia. Her dissertation advisor was Bill Wulf, with whom she worked on memory systems architecture. Together they coined the now-common term the “memory wall” to describe a situation in which processors are always waiting on memory, and CPU performance is therefore entirely limited by memory performance.

Before graduate school, McKee worked for Digital Equipment Corporation and Microsoft Corporation. She has also held internships at Digital Equipment Corporation’s Systems Research Center (now HP Labs) and the former AT&T Bell Labs. McKee worked as a Post-Doctoral Research Associate in the University of Virginia Computer Science Department for a year after graduating (waiting for the chip to come back from fab) and as a Computer Architect at Intel’s Microcomputer Research Lab in Oregon for the next two years. During her time at Intel, she also taught at the Oregon Graduate Institute and Reed College. McKee was a Research Assistant Professor at the University of Utah’s School of Computing from 1998 to 2002, where she worked on the Impulse Adaptable Memory Controller project. She joined Cornell University’s Computer Systems Lab within the School of Electrical and Computer Engineering in 2002. She moved to the Department of Computer Science and Engineering at Chalmers University of Technology in 2008, and she became the C. Tycho Howle endowed chair within the Holcombe Department of Electrical and Computer Engineering at Clemson University in 2018. She spent the 2017 calendar year on sabbatical at Rambus Labs in Sunnyvale, CA.

Her research has historically focused mainly on analyzing application memory behavior and designing more efficient memory systems together with the software to exploit them. Achieving this broad objective requires developing new underpinnings for system understanding, and thus she and her students and collaborators have developed new approaches to performance analysis; built scalable tools for application analysis and system modeling; designed architectures to enable more comprehensive system introspection and analyses; designed efficient memory systems for HPC and embedded platforms; and automated memory optimizations for HPC applications.

EJ Kim is an Associate Professor in the Department of
Computer Science & Engineering, Texas A&M University, where she has been since 2003. She received the BS degree in computer science from the Korea
AdvancedInstitute of Science and Technology, Korea, in 1989, the MS degree in computer science from the Pohang University of Science and Technology, Korea, in
1994, and the PhD degree in computer science and engineering from the Pennsylvania
State University in 2003. Her research interests
include computer architecture, approximation computing, parallel/distributed systems, low-power design, secure computing, and performance evaluation.