Carole-Jean Wu is an Assistant Professor of Computer Science and Engineering in the School of Computing, Informatics, and Decision Systems Engineering in Arizona State University (ASU). She is the director of the Energy Efficient Computer Architecture Laboratory (EECALab) and she is also affiliated with the NSF I/UCRC Center for Embedded Systems (CES). She holds an affiliated faculty appointment in EECE at ASU. Professor Wu receives her M.A. and Ph.D. in Electrical Engineering from Princeton University in 2008 and 2012, respectively. She receives a B.Sc. degree in Electrical and Computer Engineering from Cornell University. Before joining ASU, Prof. Wu held a number of internship positions with Intel, IBM, and Google.
Prof. Wu is currently working in the area of Computer and System Architectures. In particular, her research interests include high-performance and energy-efficiency computer architectures through hardware heterogeneity, energy harvesting techniques for emerging computing devices, temperature and energy management for portable electronics, performance characterization, analysis and prediction, and memory subsystem designs. Prof. Wu is the recipient of the 2017 NSF CAREER Award, the 2017 IEEE Young Engineer Award, the 2013 Science Foundation of Arizona Bisgrove Early Career Award, and the 2011-12 Intel Ph.D. Fellowship Award. She was the recipient of the 2014 IEEE Best of Computer Architecture Letter award. Her research has been supported by both industry sources and the National Science Foundation.
Carole-Jean Wu is broadly interested in computer architecture with an emphasis on shared resource management for chip-multiprocessor (CMP) systems. In particular, she investigates software and hardware techniques to assist the management of the multi-level cache hierarchy, targeting performance throughput, quality of service (QoS), and fair sharing.
Since providing effective hardware support has a key impact on the advancement of technology, her most recent research agenda focuses on the critical challenges for improving hardware supports for energy and reliability, performance throughput, and security for CMPs and servers in the cloud.