Dr. Debanjan Konar earned a Bachelor of Engineering in Computer Science and Engineering (CSE) from the University of Burdwan , in 2010, an MTech in CSE from the National Institute of Technical Teachers' Training and Research (NITTTR), Kolkata , in 2012, and a PhD from the Indian Institute of Technology Delhi in New Delhi , in 2021. He is currently working as a postdoctoral researcher at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Germany. Prior to this, Dr. Konar served as an Assistant Professor at Sikkim Manipal Institute of Technology, Sikkim , and SRM University-AP, Andhra Pradesh . His research interests include quantum machine learning (QML), hybrid classical-quantum neural networks, deep learning, and computer vision. He has authored several papers in prestigious computer science journals, conference proceedings, book chapters, and internationally renowned books. Dr. Konar received a National Scholarship in 2001 and a GATE Postgraduate Fellowship in 2010. He is an IEEE senior member and an ACM member. He also serves as an editor and a reviewer for several esteemed journals and international conferences.

Recently, Quantum Computing (QC) has been leveraged for machine learning with the expectation that the uncertainty inherent in QC may be used to great advantage in stochastic-based modelling , spurring new research on Noisy Intermediate-Scale Quantum (NISQ) devices. To exploit the advantages of stochastic-based modelling in QML research, Dr. Konar has proposed Spiking Quantum Neural Networks using hybrid classical-quantum algorithms with the merits of superposition states and amplitude encoding. Within this Fulbright-Nehru Postdoctoral Research Fellow ship, the proposed models will be extensively validated on various computer vision applications, including disguised facial recognition using the PennyLane Quantum Simulator with limited quantum hardware and supercomputing resources available at Purdue University, USA.