🚀 ABOUT ME
Passionate about Data Science and Uncertainty Quantification | Ph.D. Graduate from Johns Hopkins University
Welcome to my GitHub profile! I'm a highly motivated Ph.D. graduate from Johns Hopkins University, specializing in Active Machine Learning. My research journey focused on developing an active learning (ML) algorithm for global sensitivity analysis in large-scale wind tunnel experiments. This involved integrating wind tunnel experiments within an automated active learning framework, streamlining processes, minimizing human error, and significantly enhancing the rate of discovery.
🔍 Key Highlights:
- Active Learning Maven: Pioneered the application of active learning algorithms to large-scale wind tunnel experiments, leveraging machine learning for global sensitivity analysis.
- Python and Git Expertise: Contributed significantly to the Python library 'UQpy' as a developer, accumulating six years of hands-on experience in Python and Git.
- Master's in Applied Mathematics and Statistics: My academic journey includes a master's degree that equipped me with a solid foundation in ML algorithms, such as SVM, random forests, and more. I specialize in Probability Theory, which forms the core of my research work in Uncertainty Quantification.
📈 Technical Proficiency:
Gaussian Process Regression: Adept in utilizing Gaussian process regression for implementing active learning algorithms, accumulating four years of hands-on experience.
🎓 Education:
- Ph.D. in Civil Engineering, Johns Hopkins University
- Master's in Applied Mathematics and Statistics, Johns Hopkins University
- B.Tech + M.Tech in Civil Engineering, IIT Kanpur
I am enthusiastic about exploring new opportunities, collaborating on innovative projects, and contributing to the dynamic world of data science and machine learning.
