Apoorva Bisht
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Researcher
Unknown Researcher
Research Areas
Biography and Research Information
OverviewAI-generated summary
Apoorva Bisht's research focuses on the application of machine learning techniques for the identification of two-dimensional quantum materials. This work involves developing and utilizing self-attention and soft-labeling methods within deep learning frameworks to analyze and classify these advanced materials. Bisht also investigates the experimental study of phase profiles in Hermite-Gauss laser beams, contributing to the understanding of optical phenomena. Collaborations with researchers at the University of Arkansas at Fayetteville, including Reeta Vyas, Xuan-Bac Nguyen, and Hugh Churchill, have resulted in shared publications in these areas. Bisht has an h-index of 2 and has published a total of 7 works with 10 citations, with recent activity in 2024.
Metrics
- h-index: 2
- Publications: 7
- Citations: 10
Selected Publications
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Experimental Studies of Phase Profile of Hermite-Gauss Laser Beams (2024)
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Two-Dimensional Quantum Material Identification via Self-Attention and Soft-Labeling in Deep Learning (2024)
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Experimental confirmation of phase profile of Hermite–Gauss beams (2024)
Collaboration Network
Top Collaborators
- Experimental confirmation of phase profile of Hermite–Gauss beams
- Experimental Studies of Phase Profile of Hermite-Gauss Laser Beams
- Experimental confirmation of phase profile of Hermite–Gauss beams
- Experimental Studies of Phase Profile of Hermite-Gauss Laser Beams
- Experimental confirmation of phase profile of Hermite–Gauss beams
- Two-Dimensional Quantum Material Identification via Self-Attention and Soft-Labeling in Deep Learning
- Two-Dimensional Quantum Material Identification via Self-Attention and Soft-Labeling in Deep Learning
- Two-Dimensional Quantum Material Identification via Self-Attention and Soft-Labeling in Deep Learning
- Two-Dimensional Quantum Material Identification via Self-Attention and Soft-Labeling in Deep Learning
- Two-Dimensional Quantum Material Identification via Self-Attention and Soft-Labeling in Deep Learning
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