Exploring the frontiers of physics through computational analysis and data science
MIT Data Science for Physics Class
Explore ProjectsAdvanced computational physics projects combining theoretical understanding with practical data analysis
Advanced signal processing and analysis of LIGO data to detect gravitational wave signals within noisy datasets. This project implements sophisticated algorithms for signal identification and waveform fitting.
Analysis of W and Z boson production in LHC collisions to measure the weak mixing angle (sin²θw). This project explores particle physics through jet analysis and statistical methods.
Advanced computational tools and frameworks used in these physics projects
Data analysis and scientific computing
High energy physics data analysis
Gravitational wave observations
Particle collision data analysis
These projects represent advanced computational physics work completed for MIT's Data Science for Physics course. Each project combines theoretical physics understanding with practical data analysis skills, demonstrating the power of computational methods in modern physics research.
The projects showcase expertise in signal processing, statistical analysis, particle physics, and gravitational wave detection - key areas in contemporary physics research.
I'm a physics student passionate about computational methods and data analysis in modern physics research. These projects represent my work in applying advanced computational techniques to fundamental physics problems.
For more about my background, research interests, and other projects, visit my blog where I share insights about physics, data science, and my academic journey.
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