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abhisekpraharaj/README.md

Hi there 👋, I'm Abhisek Praharaj!

Researcher & Developer | Bridging Science and Code 🚀

Profile Views GitHub Followers GitHub Stars

Current Focus 🧑‍🔬

  • Physics Research with AI: Leveraging AI to uncover complex physical phenomena.
  • Deep Learning Models: Developing advanced machine learning algorithms.

Learning Path 🌱

  • Machine Learning: Algorithms like PINN and AI for solving scientific problems.

Connect with Me 📫

Languages & Tools 🛠️

Python C++ TensorFlow Keras
CERN ROOT VS Code Linux Windows

Technologies 🌐

  • ML Frameworks: TensorFlow, Keras, PyTorch
  • Scientific Computing: SciPy, NumPy, CERN ROOT
  • Operating Systems: Linux, Ubuntu, Windows
  • Version Control: GitHub

“I believe in the power of combining science and technology to create solutions that make a difference.” 💡

Last updated on October 1, 2024

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  1. Analysis_Data Analysis_Data Public

    In this repository, I've meticulously detailed analysis techniques used in High Energy Physics, focusing on Au-Au collisions at 200 GeV. I've explored kaon spectra and extracted Phi-Meson Invariant…

    C++ 1

  2. Root_Basics Root_Basics Public

    This project has basic knowledge about the ROOT Framework and how to use the C++ language to work with root files.

    C 2

  3. Numerical_Methods_Projects Numerical_Methods_Projects Public

    Various Numerical Method are used to solve some real world Problems in a very sophisticaed and graphically way. This repository contains some of the numerical methods which is used to solve some su…

    Jupyter Notebook 1

  4. HEP_ML_ HEP_ML_ Public

    Deep Learning Applications in High Energy Physics

    Jupyter Notebook 1

  5. Higgs_Data_Check Higgs_Data_Check Public

    This Project contains code to check and analyse Higgs background and signal data.

    C 2

  6. PINN_Quantum_Harmonic_Oscillator PINN_Quantum_Harmonic_Oscillator Public

    In this repository, I tried to create a physics informed neural network to train on few datapoints of a quantum harmonic oscillator and see how the model learns about the problem.

    Jupyter Notebook 1