My research interest lies at the intersection of data, ML and system. At Meta AI, I'm exploring limits of scaling for many SOTA models.

I am a PhD candidate in the Department of Computer Science & Engineering at the University of California, Riverside. I work with Distinguished Prof. Laxmi Narayan Bhuyan and Prof.Daniel Wong and am a member of SoCal Lab at UCR. My interest lies in Computer Architecture , GPGPU Architecture design, High Performance Computing, Fault-Tolerance systems. I have worked on multiple projects on Data- Dependent Applications on GPGPU, Low power Design of GPGPU Execution units and have achieved notable improvements in terms of Performance gain and Power and Area saving.

My research interests are in Computer Architecture and Systems and my current research focuses on optimizing GPU architectures for Deep Learning applications, specifically Recurrent Neural Networks. I have been a part of the effort to augment GPGPU-Sim, a widely used GPU simulator, to execute Deep Learning kernels written using NVIDIA's cuDNN and cuBLAS libraries. My research advisor is Prof. Matthew D. Sinclair.

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.