I’m a Computational Physicist passionate about solving complex problems through mathematics, numerical methods, scientific computing, and data-driven approaches.
My work sits at the intersection of Computational Physics, Scientific Computing, Machine Learning, and Quantitative Modeling.
Develop numerical methods for complex physical systems:
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Build scientific computing tools and numerical solvers.
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Apply machine learning and statistical methods to scientific problems.
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Explore AI techniques for modeling, prediction, and discovery.
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Interested in quantitative research and computational finance.
I enjoy combining mathematics, computation, and data-driven approaches to understand and model complex systems.
Technical stack: Programming Languages/Scientific Computing & HPC/Machine Learning & Data Science/Development Tools
Research Interests
- Machine Learning
- Artificial Intelligence
- Scientific Machine Learning
- Data Science
- Quantitative Research
- Scientific Software Engineering
- High Performance Computing
- Computational Finance
- Numerical Methods
- Computational Physics
Current Focus
- Machine Learning for scientific applications
- Physics-informed machine learning
- AI for computational modeling
- Statistical learning and predictive analytics
- Quantitative methods for complex systems
- Large-scale numerical simulations
Featured Projects
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Scientific computing tools and numerical solvers
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Machine Learning and Data Science projects
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Spectral methods for Schrödinger-Poisson systems
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Numerical simulations in gravitational physics
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LinkedIn, Inspirehep, Email: available upon request
"Using mathematics, computation, and AI to understand and model complex systems."