Under Development
Please note: This section is a work in progress. Information and features are subject to change and may be incomplete.
Exploring the frontiers of AI, machine learning, and emerging technologies

Advanced scene generation using conflict-aware adapter composition techniques. Developed at AuraX (CIE @IIIT-H) with comprehensive benchmark comparisons.

A proprietary framework designed to address persistent challenges in diffusion-based generative models. ReNU (Regularized Neural Units) integrates enhanced penalty functions into the reverse diffusion process to significantly improve the preservation of fine texture details and ensure faithful color reproduction in generated images. [cite: 4, 5] The system is particularly effective for applications requiring high-fidelity image synthesis, such as virtual try-on.

A self-directed research project advancing the thesis that the Mixture of Experts (MoE) architecture is a compelling computational analogue to the human brain's principle of functional specialization. The work deconstructs the neuroscientific foundations of brain organization, provides a technical analysis of MoE models, and synthesizes these domains into a novel, brain-inspired hierarchical MoE architecture.

This research presents a quantum-resistant encryption algorithm that leverages a high-dimensional lattice framework (n=1000) based on the Learning With Errors (LWE) problem[cite: 290, 302]. [cite_start]The scheme is designed to counter threats from quantum algorithms like Shor's and Grover's, which undermine classical cryptosystems such as RSA and AES[cite: 288, 296]. [cite_start]The parameters are carefully chosen to align with NIST's post-quantum security standards (Category 5, 256-bit quantum security)[cite: 292, 302].

Complete implementation of the 'Attention is All You Need' paper, building a GPT-style language model from scratch with detailed documentation and a training pipeline.

A hand-crafted neural network built from the ground up in Python with NumPy for MNIST digit recognition. This project was undertaken to demonstrate and solidify an understanding of the fundamental concepts of machine learning without relying on high-level frameworks like TensorFlow or PyTorch.