Naga Kandasamy is a Professor in the Electrical and Computer Engineering Department at Drexel University where he teaches and conducts research in the area of computer engineering, with specific interests in embedded systems, self-managing systems, reliable and fault-tolerant computing, distributed systems, computer architecture, and testing and verification of digital systems. Prior to joining Drexel, he was a research scientist at the Institute for Software Integrated Systems, Vanderbilt University from 2003 to 2004. He is currently serving as the interim department head for the ECE department.
Professor Kandasamy received a 2007 National Science Foundation Early Faculty (CAREER) Award and a best paper award at the 2006 IEEE International Conference on Autonomic Computing. He is a member of the IEEE.
Degrees / Education
- PhD, University of Michigan, 2003
- MS, University of Connecticut
- BE, Guindy Engineering College, Anna University, Chennai, India
Computer architecture, parallel programming, high-performance computing, neuromorphic computing
- K. Shah, J. Shackleford, N. Kandasamy, and G. C. Sharp, "Deformable Registration Choices for Multi-Atlas Segmentation,'' Auto-Segmentation for Radiation Oncology: State of the Art, J. Yang, G. C. Sharp, and M. J. Gooding (Editors), CRC Press, 2021.
- T. Titirsha, S. Song, A. Das, J. Krichmar, N. Dutt, N. Kandasamy, and F. Catthoor, "Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware,'' IEEE Transactions on Parallel and Distributed Systems, To appear.
- S. Song, A. Das, O. Mutlu, and N. Kandasamy, "Enabling and Exploiting Partition-Level Parallelism in Phase Change Memories,'' ACM Transactions on Embedded Computing Systems, vol. 37, no. 4, August, 2019.
- S. Song, A. Das, and N. Kandasamy, "Improving Dependability of Neuromorphic Computing With Non-Volatile Memory,'' Proc. 16th European Dependable Computing Conf. (EDCC), 2020. Distinguished paper.
- S. Song, A. Balaji, A. Das, N. Kandasamy, and J. Shackleford, "Compiling Spiking Neural Networks to Neuromorphic Hardware,'' Proc. 21st ACM SIGPLAN/SIGBED Int'l Conf. Languages, Compilers, & Tools for Embedded Systems (LCTES), 2020.