Research Engineer | Machine Learning & Blockchain

My research focuses on the intersection of machine learning and blockchain technologies, with particular emphasis on developing secure, scalable decentralized systems and exploring novel applications of ML in blockchain environments. I employ rigorous research methodology, emphasizing reproducibility, formal verification, and practical implementation to address fundamental challenges in distributed systems and intelligent automation.

Machine Learning

• Deep learning architectures and optimization
• Model deployment and production systems
• Research methodology and experimental design
• Machine learning in decentralized environments

Blockchain & Distributed Systems

• Smart contract security and formal verification
• Consensus mechanisms and protocol design
• Decentralized application architecture
• Scalability and interoperability solutions

Intersection Research

• ML-driven smart contract optimization
• On-chain machine learning and inference
• Decentralized AI/ML infrastructure
• Privacy-preserving ML on blockchain

Block-Barter
A blockchain-based trading platform investigating novel consensus mechanisms and smart contract architectures for high-throughput decentralized applications.

Active Investigations

• Scalable consensus algorithms for high-throughput blockchain applications
• Machine learning-driven optimization of smart contract execution
• Security analysis and formal verification of decentralized ML systems

Research & Development

• Python, PyTorch, TensorFlow, Jupyter
• Solidity, Rust, TypeScript, Go
• Web3.js, Ethers.js, Hardhat, Foundry

Infrastructure & Experimentation

• AWS, Docker, Kubernetes
• MLflow, Weights & Biases, experiment tracking
• CI/CD, GitHub Actions

[Publications, preprints, technical reports, and significant open-source contributions will be listed here]

Email: [email protected]
GitHub: @ansulx