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:
Build scientific computing tools and numerical solvers.
Apply machine learning and statistical methods to scientific problems.
Explore AI techniques for modeling, prediction, and discovery.
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
Scientific computing tools and numerical solvers
Machine Learning and Data Science projects
Spectral methods for Schrödinger-Poisson systems
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."