Radu Marculescu
Adjunct Professor, Electrical and Computer Engineering
Pittsburgh, PA 15213
Bio
Radu Marculescu is the Kavčić-Moura Professor of Electrical and Computer Engineering at Carnegie Mellon University. He received his Ph.D. in Electrical Engineering from the University of Southern California in 1998. Radu's current research focuses on developing methods and tools for modeling and optimization of embedded systems, cyber-physical systems, social networks, and biological systems.
For his work on design automation and embedded systems design, he has received the Donald O. Pederson Best Paper Award from the IEEE Trans. of Computer-Aided Design of Integrated circuits and Systems in 2012, the Best Paper Award of IEEE Trans. on VLSI Systems in 2018, 2011, and 2005, the 10-Year Retrospective Most Influential Paper Award from the Asia and South Pacific Design Automation Conference in 2013, as well as several best paper awards in major conferences and symposia.
Radu currently serves as the Editor-in-Chief of Foundations & Trends of Electronic Design Automation and Associate Editor-in-Chief of IEEE Transactions on Multi-Scale Computing Systems. Over the years, he has served as an Associate Editor of IEEE Trans. on Computers, IEEE Trans. on Computer-Aided Design of Circuits and Integrated Systems, IEEE Trans. on VLSI, ACM Trans. on Embedded Computing Systems, ACM Trans. on Design Automation of Embedded Systems, PeerJ. He was an ACM Distinguished Speaker (2009-2012).
He has been involved in organizing many international conferences, symposia, workshops, and tutorials, as well as guest editor of several special issues in archival journals and magazines. Most recently, he was the General co-Chair of 10th edition of the Cyber-Physical Systems Week held in Pittsburgh in April 2017.
Radu is a Fellow of IEEE cited for his contributions to the design and optimization of on-chip communication for embedded multicore systems.
Education
Ph.D., 1998Electrical Engineering
University of Southern California
Research
Keywords
- Embedded systems
- Cyber-physical systems
- Data and network science
- Manycore/IoT/Edge computing
- Computational biology
- Machine learning