Research Portfolio

AuraX's proprietary paradigm shift in Virtual Try-On (VTON) and product imaging. Employs a modular, domain-specific AI architecture using a Conflict-Aware Adapter Composition (C-AAC) algorithm to merge multiple LoRAs on top of the FLUX foundational model without catastrophic forgetting or feature interference.

A comprehensive framework designed to close the 'Complexity Gap' in Virtual Try-On (VTON) for diverse global garments. Utilizing a dual-stream approach combining Flux Fill for inpainting and Flux Redux for structural style transfer, with a Multi-LoRA Expert Fusion strategy.

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.