I received my Ph.D in Electrical Engineering from IIT Bombay in 2020, under the supervision of Prof. Bhaskaran Muralidharan and Prof. Ashwin Tulapurkar. My doctoral research focused on developing a simulation framework using the non-equilibrium Green’s function (NEGF) spin transport formalism to investigate the scaling effects of magnetic tunnel junction (MTJ) devices. In the latter part of my Ph.D., I worked on a micromagnetic simulation framework to design a skyrmion-based self-sustaining oscillator. After submitting my thesis in August 2019, I briefly worked as a Research Associate in the Department of Electrical Engineering at IIT Bombay.

In November 2019, I joined the National University of Singapore as a Research Fellow for my postdoctoral research under Prof. Xuanyao (Kelvin) Fong. During my postdoctoral tenure, I developed a simulation framework based on the Fokker-Planck (FP) equation to capture the impact of stochastic programming processes in emerging non-volatile memory (eNVM) devices. Subsequently, I directed my research focus toward the applications of spintronic devices in neuromorphic computing. I performed micromagnetic simulations to emulate the functionality of biological neurons and synapses for spiking neural networks (SNNs) using spintronic devices, including magnetic skyrmions and domain walls. Additionally, I collaborated with experimentalists working on spintronic devices, integrating the characteristics of fabricated devices into neuromorphic computing models.

In June 2024, I joined the Department of Electrical and Electronics Engineering at BITS Pilani, K.K. Birla Goa Campus as an Assistant Professor (Grade I). My current research interests lie at the intersection of hardware and algorithm co-design for SNN architectures, aiming to create efficient and scalable solutions for next-generation computing systems.

Sakshi Kiran Bandekar - Energy-Aware Optimization of Hardware-Friendly Spiking Neural Network Algorithms

1. Daksh Kriplani- An Evaluation Framework for SNN Hardware Accelerators Leveraging Emerging Non-Volatile Memory Technologies

2. Sanjay Sriram - Addressing Synaptic Weight Imperfections in Neural Network Training

3. Agastya Sanyal: Optimizing Neural Networks through Quantization for Resource-Constrained Devices.

4. Janav Gode: Exploration of Spintronics devices for magnetic memory.

5. Nilay Naval Toshniwal: Energy-Efficient Neuromorphic Computing with Spintronics

6. Kushagra Singh: Exploration of Spintronics devices for in-memory computing

7. Prajakta Bandgar: Energy-Efficient Hardware Implementation of Edge Detection Algorithms

8. Kaustabh Saoji: Implementation of  Couple Oscillator-based Ising machine

9. Gaurvanshu Shivran: Solving Combinatorial Optimization Problems with Ising Machines

10. Nimish Mahajan: Exploring the Potential of Vision Transformers for Image Classification

11. Anurag Saxena: Exploring the Potential of Vision Transformers for Object Detection

12. Raghav Tandon: The Impact of Attention Mechanisms on Deep Learning