Any fool can know. The point is to understand.
Albert EinsteinAdvancing our understanding of the universe through the synergy of physics and computational innovation. My research focuses on leveraging machine learning techniques to tackle complex problems in cosmology and particle physics, aiming to uncover new insights about the fundamental nature of reality.
Research
Machine Learning in Physics
Developing machine learning models that can learn from physics data, adapt to new information, and make predictions that drive scientific discovery. From improving particle collision simulations to enhancing cosmological data analysis techniques.
The Early Universe
By analyzing cosmic microwave background data using Machine Learning models, I aim to uncover the statistical properties of primordial fluctuations to levels never before achieved.
Putting the proton under a microscope
Understanding the structure of matter through Deep Inelastic Scattering (DIS) in the color dipole picture. In this framework, a virtual photon fluctuates into a quark-antiquark dipole that then interacts with the target proton via gluon exchange, revealing the fascinating phenomena of gluon saturation.