Physicist specializing in spintronics, magnetic materials, oxide heterostructures, and thin film research. My work integrates experimental materials science with applied machine learning to accelerate the discovery and optimization of advanced materials for next-generation electronic and memory devices.

• Spintronics and magnetic materials
• Oxide heterostructures
• Antiferromagnetic/heavy metal heterostructures
• Spin–orbit torque optimization
• Thin film growth and characterization
• Data-driven materials science

• Magnetron sputter deposition
• Growth of metallic and oxide heterostructures
• Thin film growth of Ta and FeMn
• X-ray diffraction (XRD) analysis and phase identification
• Raman spectroscopy
• Atomic Force Microscopy (AFM)
• Transmission Electron Microscopy (TEM)
• Magnetotransport measurements

• Python
• Scikit-learn
• Data analysis and visualization
• Applied machine learning for materials science
• Data-driven optimization of experimental parameters

• AI-assisted materials discovery
• Machine learning in condensed matter physics
• Data-driven spintronics research
• Optimization of thin film growth parameters using machine learning

• Machine learning models for predicting spin–orbit torque efficiency
• AI-based XRD phase identification
• Raman spectrum peak detection using Python
• Data-driven analysis of thin film growth parameters
• Machine learning applications in spintronics and oxide heterostructures

I welcome collaborations in: