Transforming ambitious technical ideas into scalable, efficient software solutions
Independent machine learning engineer and systems architect specializing in quantum computing, AI infrastructure, and scientific computing. Building advanced models and tools across biology, physics, and materials science.
A simplified machine learning automation framework that handles the complexities of ML workflows through configuration. The project provides an easy-to-use interface for training machine learning models while automatically managing optimization, visualization, and training processes.
View on GitHubDataVolt is an enterprise-grade framework for building and maintaining scalable data engineering pipelines. It provides a comprehensive suite of tools for data ingestion, transformation, and processing, enabling organizations to standardize their data operations and speed up development cycles.
View on GitHubLambda_Zero is a unified platform for scientific machine learning, providing the newest Nexa models for predictions in biology (protein structure), astrophysics (stellar properties), and material science (material properties). Access these models via a simple REST API, with results returned in JSON format including predictions and confidence scores (0-100%).
View on GitHubPyC compiler toolchain, explaining its architecture, file roles, features, and potential use cases. PyC aims to compile Python adult-like code into machine code using LLVM, with experimental CUDA integration for GPU acceleration.
View on GitHubA decentralized, open-access, community-governed publication platform for high-quality research in scientific machine learning, advanced optimization, computational modeling, systems engineering, and related fields.
Learn MoreResearch comparing Simulated Annealing and Adam optimization algorithms on the Himmelblau function using NVIDIA T4 GPUs and CuPy acceleration. Comprehensive analysis of non-convex optimization landscapes and algorithm performance.
View Research