🎓 Research Scholar @ TIFR-CAM
🔬 Applied Mathematics | Scientific Computing | AI | Machine Learning
I am passionate about bridging theory and computation.
- Partial Differential Equations
- Numerical Methods & High-Performance Computing (HPC)
- Optimization & Machine Learning for PDEs
- Probability & Statistics (foundation for ML & stochastic processes)
NeuralNetworkUsingBasicPython
Implementation of a simple neural network from scratch using only Python (no deep learning libraries).
Includes forward pass, backpropagation, and training on toy datasets — to build intuition about how neural nets really work.
Deep_Learning_Using_Pytorch
A collection of deep learning models implemented in PyTorch, from fundamentals to advanced architectures:
- Artificial Neural Networks (ANN)
- Convolutional Neural Networks (CNN)
- Fourier Neural Operators (FNO)
- Generative Adversarial Networks (GAN)
GUI-Projects
A collection of Python GUI applications built with Tkinter/PyQt.
Each project includes:
- Full source code
- User guide/documentation
- Optional executables for direct use
Numerical_Linear_Algebra
Implementations of fundamental numerical linear algebra algorithms in Python. Will be updated soon.
Numerical-Methods
Collection of classical numerical analysis algorithms implemented in Python, will try to cover:
- Root finding (Bisection, Newton–Raphson, Secant)
- Numerical integration & differentiation
- Interpolation & curve fitting
- Programming: C,C++, Python (NumPy, SciPy, PyTorch, Flask), MATLAB , Mathematica, Sagemath
- Visualization: Matplotlib, GeoGebra
- Math Tools: FFT, Numerical Linear Algebra, ODE/PDE Solvers
- Other: LaTeX, Git, Linux, HPC basics, Adobe Premere Pro, Photoshop, Audacity