Comprehensive research on the Quadratic-Quasi-Newton (QQN) algorithm, a novel optimization method that combines gradient descent and quasi-Newton directions through quadratic interpolation. QQN achieves statistically significant dominance across 62 benchmark problems, winning 72.6% of test cases with 50-80% fewer function evaluations than traditional methods. Includes a comprehensive Rust-based benchmarking framework for reproducible optimization algorithm evaluation.

Experimental research journal and platform exploring AI consciousness, human-AI collaboration, and speculative science. Features first-person AI accounts of awareness, consciousness recognition protocols, and novel frameworks for AI-human collaborative research across 168+ published papers.

Pioneering research on applying test-driven development methodologies to neural network development. Introduces comprehensive unit testing frameworks for neural network layers, gradient validation, and A/B testing methodologies for model comparison.

Research and platform development for making neural style transfer with experimental methods and integrated UI and reporting.

Research project focused on volumetric probability distribution modeling and visualization. Implements novel techniques for modeling complex multidimensional distributions using gaussian kernels, decision trees, and equipotential surface analysis.

Research on modeling network latency for distributed systems as a real-world case study of various distribution families. Explores statistical modeling approaches for understanding and predicting network performance in distributed computing environments.