About Me 

I am an early-career researcher in computational condensed matter physics, with a strong interest in connecting theoretical modeling with experimental results. My research focuses on developing computational models that support, explain, and guide experiments, with the goal of building predictive frameworks to study the electronic, magnetic, and electrochemical properties of materials for nanoelectronic and energy-storage applications. I employ first-principles density functional theory (DFT), tight-binding models, kinetic Monte Carlo (KMC), and non-equilibrium Green’s function (NEGF)-based transport simulations in my work. In addition, I employ physics-based machine-learning (ML) approaches to address the challenges posed by complex and large-scale material systems.

Specifically, my research has focused on strategies to minimize contact resistance at metal-semiconductor heterointerfaces for low-resistance two-dimensional (2D) field-effect transistors (FETs) and spinFET applications. I have also developed DFT-based models for large-scale simulations.

Currently, I am designing 2D heterostructures combining ferromagnetic metals and heavy metals for field-free spin-orbit torque (SOT) switching in low-power MRAM devices. In parallel, I am developing ML-generated interatomic potentials to study complex 2D systems.  

I received my PhD in August 2025 from the Indian Institute of Technology (IIT) Bhubaneswar, India, supported by funding from the Council of Scientific & Industrial Research (CSIR). During my doctoral studies, I collaborated with research groups at IIT Delhi and IIT Jammu.

For more details about my research, please visit my [Research Page], and for a complete list of publications, see my [Google Scholar] profile.

Education

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