Dr. J. S. Raj Kumar

Dr. Raj Kumar J. S. is an Assistant Professor in the Division of Artificial Intelligence and Machine Learning at Karunya Institute of Technology and Sciences, Coimbatore. He holds a Ph.D. in Electronics and Communication Engineering with specialisation in Nanoelectronics and Semiconductor Device Modelling, awarded by Karunya Institute of Technology and Sciences in 2023. He also holds an M.Tech. in VLSI Design with First Class and Distinction from Karunya Institute of Technology and Sciences, completed in 2019, and a B.Tech. in Electronics and Communication Engineering from the same institution, completed in 2016.

His research expertise encompasses Nanoelectronics, wide-bandgap semiconductor device modelling, GaN HEMTs, Edge AI, TCAD-based modelling, machine learning, and AI-enabled hardware systems. His work bridges semiconductor device physics with predictive AI systems, with particular focus on Edge AI acceleration, TCAD machine-learning surrogate modelling, and smart cyber-physical electronics. His technical expertise includes Synopsys TCAD Sentaurus, Silvaco TCAD ATLAS/ATHENA, MATLAB, Verilog/VHDL, and Python-based machine learning toolkits, along with fabrication and characterisation techniques such as Magnetron Sputtering/PVD, PLD, and XRD.

Dr. Raj Kumar J. S. has contributed to more than 35 scholarly publications, predominantly in Q1/Q2 journals published by Springer Nature and Elsevier, as well as Scopus-indexed conferences. His research profile records 200+ citations, an h-index of 7, and an i10-index of 6. He served as a Senior Research Fellow and Project Lead for the ISRO RESPOND-funded project titled “Design and Fabrication of Cascode Driver Circuit using GaN HEMT on Si for Space Applications,” sanctioned for ₹33.44 lakhs in collaboration with SCL Chandigarh. He also undertook research internship work at SSPL, DRDO, New Delhi, focusing on the simulation and design of GaN architectures.

He has completed the long-term IIT Kanpur programme on “Modeling and Simulation of Nano-Transistors” and has teaching expertise in Edge Computing and Embedded Systems, IoT Architectures, VLSI Architecture and Digital System Design, Hardware Accelerators for Deep Learning, Nanoelectronics and Semiconductor Physics, and Advanced Device Simulation. He currently guides 2 Ph.D. scholars and 2 M.Tech. researchers and has supervised more than 25 B.Tech. capstone projects. His professional contributions include serving as a Session Chair at an International Conference on Advances in Electrical, Electronics and Computational Intelligence and receiving the Best Paper Presenter recognition at the 3rd International Conference on Advances in Physical Sciences and Materials in 2026. He has also completed credentials in AWS Academic Cloud Architecting, Open-Source VLSI Tools, and IoT and Machine Learning with Embedded Systems.