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ajeetkbhardwaj/README.md

Hi πŸ‘‹, I'm Ajeet Kumar

πŸŽ“ Applied Mathematician | Data Scientist | Machine Learning Enthusiast | LLM Applications | Open-Source Contributor

🌟 Passionate about modelling and solving of real world problems using data and mathematical data driven methods. I'm specialize in

  1. Data Science and Machine Learning - Feature extraction from high dimentional data to the building AI agentic workflow.
  2. Discrete Differential Geometry and Its Applications like Geometric Processing, Physical Simulation and Geometric Deep Learning
  3. Modelling with Dynamics and Controls using Differential Equations, Integral Equations and Variational Calculus etc.

Academics Experiance

πŸ“– I have done my

  1. Masters in Mathematics and Computing(BHU, Varanasi) - Score : 8.41 CGPA
  2. B.Sc(Hons) Applied Mathematics(JMI, New Delhi) - Score : 8.9 CGPA

Office/Research Work Experiance

πŸ§‘β€πŸ’Ό I have worked at

  1. Machine Learning Intern(Remote)
  1. Quantum Research Intern(Remote)
  1. AI Research Intern(IIT Delhi)

Featured Projects

πŸš€ I have worked on the following projects

  1. Lean4Agent - A multi-agentic workflow for mathematical problems
  2. DDG - Discrete Differential Geometry Framework for Geometric Processing and Physical Simulations
  3. Data Driven Approach to Solve PDEs
  4. LLM + OpenAPI Specification(411 Lab, IIT Delhi) - Building an LLM based tool for the open api specification generation for the backend server api code(python, java)

πŸ”­ I’m currently working on

  1. AI for Engineers
  2. Lab for Applied Mathematics
  3. AI from First Principle
  4. Lean4Agent

Skills

  • Feature Extraction, Modelling for Prediction
  • LLM Post Training - LLM Finetuning and Inferencing

πŸ› οΈπŸ’» Technologies & Tools

I have strongly used the following tools and frameworks during my academic, research and works journey

  • Computer Programming Language : C/C++, Python, MATLAB, Julia, SQL.
  • ML and DL frameworks : Scikit-learn, PyTorch, TensorFlow.
  • Scientific Computing : SymPy, NumPy, SciPy, Pandas, Eigen(C++), igl
  • Data Visualization : Matplotlib, Seaborn.
  • Parallel and Distributed Computing : PySpark, MPI, Torch.Distributed,
  • Real-Time Streaming : PyFlink
  • Computing Machine : Linux, Window, GPU Compute Server
  • Code/Docs Writing : Overleaf, VS Code, Google Colab.
  • Production : Git, GitHub, GitHub Action, Docker, HuggingFace

πŸ“« Let's Connect

LinkedIn Twitter HuggingFace Github

Pinned Loading

  1. AI-for-Engineers AI-for-Engineers Public

    OpenAPI specification generation from ai-agents

    Jupyter Notebook

  2. AI-from-First-Principle AI-from-First-Principle Public

    It is the core mathematics of data driven learning model from Theory to Implementation(Scratch Se)

    Jupyter Notebook

  3. Master-Programming-DataStructure-Algorithm Master-Programming-DataStructure-Algorithm Public

    Master Programming Data Structure and Algorithm

    Python

  4. Lab-for-Applied-Mathematics Lab-for-Applied-Mathematics Public

    Lab for Applied Mathematics is the practical implementation of the resources that i learned during my B.Sc and M.Sc and some Research.

    Jupyter Notebook

  5. Modelling-Complex-Systems Modelling-Complex-Systems Public

    Jupyter Notebook

  6. Recent-Advances-Into-LLM Recent-Advances-Into-LLM Public

    Jupyter Notebook