Computer architecture and software systems with special emphasis on multiprocessor and multicore systems, data center architectures, and large-scale distributed systems.

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.

Bahar Asgari is an assistant professor in the Department of Computer Science at the University of Maryland, Collage Park with a joint appointment in UMIACS. Before joining UMD in August 2022, she spent a year working at Google on its Systems and Services Infrastructure team. She received her Ph.D. in electrical and computer engineering from Georgia Tech in 2021, where she was advised by Dr. Sudhakar Yalamanchili and Dr. Hyesoon Kim. Bahar’s research interests include but are not limited to domain-specific architecture design, near memory processing, and reconfigurable computing. Her proposed low-cost hardware accelerators and hardware/software co-optimization that deal with essential challenges of sparse problems contribute to a widespread application domain from machine learning to scientific computing.

Dr. Ghazal Tashakor is a scientific staff member affiliated with multiple institutes and universities in Germany and Spain. She obtained her Ph.D. in High-Performance Computing (HPC) and advanced simulation from the Autonomous University of Barcelona in 2019. Her ongoing research endeavors primarily focus on conducting large-scale computer simulations. Additionally, she serves as a core developer in distributed and parallel architecture patterns, ranging from grid computing to data visualization/monitoring, with a specific emphasis on Big Data and advanced hierarchical models. She collaborates with various research centers in Germany, including Fraunhofer and Jülich Supercomputing Centre (JSC)