Graduate Research Assistant – ML Model Development for Experimental Physics Data
TextClassification
Developed machine learning models and conducted data-driven optimization for experimental physics datasets. Applied classification techniques to time-series and experimental data, focusing on model interpretability and detector-grade yield prediction. Integrated labeled data with experimental results for device design and material optimization.
• Labeled time-series and tabular data for machine learning model training and validation.
• Utilized interpretability methods to analyze model output and feature importance.
• Worked with experimental physics datasets related to quantum computing and sensor applications.
• Contributed to AI-powered optimization workshops and published relevant research findings.
Developed machine learning models and conducted data-driven optimization for experimental physics datasets. Applied classification techniques to time-series and experimental data, focusing on model interpretability and detector-grade yield prediction. Integrated labeled data with experimental results for device design and material optimization. • Labeled time-series and tabular data for machine learning model training and validation. • Utilized interpretability methods to analyze model output and feature importance. • Worked with experimental physics datasets related to quantum computing and sensor applications. • Contributed to AI-powered optimization workshops and published relevant research findings.
2024 - Present