🎓 Applied Mathematician | ML/AI Engineer | AgenticAI | Open-Source Contributor

I am an ML/AI Engineer & Applied Mathematician focused on building data-driven decision making systems(texts*, vision, signals).

🌟 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, AI model pre/post training, building ml systems to the building agentic AI 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.

📖 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

🧑‍💼 I have worked at

1. Machine Learning Intern(Remote)

2. Quantum Research Intern(Remote)

3. AI Research Intern(IIT Delhi)

🚀 I have worked on the following projects

1. Lean4Agent - A multi-agentic workflow for mathematical problems(411 Lab, IIT Delhi)
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)

🔭 I’m currently working on

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

• Feature Extraction, ML Modelling for Prediction
• LLM Pre/Post Training - Transformers : BERT, tinyLlamma, GPT-2, CLIP(multi-model)

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

• Computer Programming Language : C/C++¹, Python², MATLAB, Julia, SQL.
• ML and DL frameworks : Scikit-learn, PyTorch, TensorFlow.
• Scientific Computing³ : SymPy, NumPy, SciPy, Pandas, Eigen(C++), igl
• Data Visualization : Matplotlib, Seaborn.
• Parallel and Distributed Computing : PySpark⁴, 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 : Datasets, Tokenizers, Transformers, Accelerate, Evaluate, PEFT etc.