Dr. Ankit Gaurav
Dr. Ankit Gaurav earned his Ph.D. from the Indian Institute of Technology Roorkee, specializing in Artificial Intelligence, Machine Learning, and Neuromorphic Computing. His research spans deep learning, predictive modeling, and brain-inspired AI systems, with applications in time-series forecasting and pattern recognition. He has developed data-driven machine learning models for emerging memory devices and, through an international collaboration with the University of Sheffield, UK, has contributed to the advancement of compute-in-memory architectures and physical reservoir computing. Supported by a prestigious SPARC grant from the Ministry of Education, his work is driving innovations in next-generation AI hardware and intelligent systems. His research has been published in high-impact journals and presented at leading IEEE conferences. He also qualified the National Eligibility Test (NET) in 2018 for the Junior Research Fellowship (JRF) and Assistant Professor.
Ph.D. in Electronics & Communication Engineering | 2025 Indian Institute of Technology Roorkee
- Research Focus: Artificial Intelligence, Machine Learning, Neuromorphic Computing
M.Tech. in Electronics & Communication Engineering | 2015 Punjab Technical University
- Specialization: Semiconductor Device Modeling
B.Tech. in Electronics & Communication Engineering | 2012 Lovely Professional University
Senior Lecturer – School of Semiconductor, NAM:TECH, Gandhinagar, India Aug 2025-Present
Journal
- Gaurav et al., “Nano-ionic Solid Electrolyte FET-based Reservoir Computing for Efficient Temporal Data Classification and Forecasting,” ACS Appl. Mater. Interfaces, 2025. (IF – 8.5)
- Gaurav et al., “Neural ordinary differential equations for predicting the temporal dynamics of a ZnO solid electrolyte FET,” J. Mater. Chem. C, vol. 13, pp. 2804-2813, 2025. (IF – 5.7)
- Gaurav et al., “Reservoir Computing for Temporal Data Classification Using a Dynamic Solid Electrolyte ZnO Thin Film Transistor,” Front. Electron., vol. 3, pp. 1–9, Apr. 2022. (IF– 1.9)
- Sahoo, A. Gaurav et al., “Transfer Learning-Based Parameter Optimization for improved 3D NAND Performance” Springer, Journal of Computational Electronics, vol. 24, pp. 1–11, Apr. 2025. (IF – 2.2)
Conferences
- Gauravet al., “Dynamical Characteristics of a Nano-Ionic Solid Electrolyte FET Using an LSTM model,” IEEE Nanotechnology Materials and Devices Conference, Salt Lake City, Utah, United States, 2024.
- Gaurav et al., “A Solid Electrolyte ZnO Thin Film Transistor for classification of spoken digits using Reservoir Computing, “IEEE Electron Devices Technology & Manufacturing Conference, Seoul, South Korea, 2023.
- Gaurav et al., “Density Gradient Quantum Corrections based Performance Optimization of Triangular TG Bulk FinFETs using ANN and GA,” IEEE 20th International Symposium on VLSI Design and Test (VDAT), 2016.
- Gaurav et al., “Performance Analysis of Rectangular and Trapezoidal TG Bulk FinFETs for 20 nm Gate Length,” IEEE INDICON Conference, India, 2015.
- Song, A. Gaurav et al., “Reservoir Computing Based on a Solid Electrolyte ZnO TFT: An Attractive Platform for Flexible Edge Computing,” IEEE International Flexible Electronics Technology Conference, San Jose, CA, USA, 2023.
- M. De Souza, X. Song, A. Gauravet al., “A delay system reservoir based on a nano-ionic Solid Electrolyte FET,” IEEE Nanotechnology Materials and Devices Conference, Paestum, Italy, 2023.
- Sahoo, A. Gaurav et al., “Investigation of Void Effect Inside Epi-plug on Electrical Characteristics of NAND Flash Memory,” Int Conf. Solid State Devices and Materials, Himeji, Japan,2024.
D. Sahoo, A. Gaurav et al., “Investigation and Optimization of Plug Height and Bottom Recess Depth of NAND Flash Memory,” IEEE INDICON Conference, India,2024.
AI & Machine Learning, Neuromorphic Computing, Emerging Memory Devices, AI-Driven Semiconductor Manufacturing, Compute-in-Memory Architectures, LLMs for Conversational AI, Time-Series Forecasting and Pattern Recognition
Leadership
