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.
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.