Biography will be updated soon.

Mikhail A. Urinson is a quantitative finance professional and entrepreneur with over two decades of cross-sector leadership spanning traditional finance, real estate, construction, retail, and digital assets. He began his career in corporate finance at Ernst & Young and later managed over $300M in real estate portfolios before transitioning to entrepreneurship, successfully launching and exiting multiple ventures across industries. Holding a PhD in Statistics & Economics, a Master’s in Data Science & Programming, and a Bachelor’s in Finance & Accounting, Mikhail combines academic rigor with applied market experience. Over the past four years, he has concentrated on applying AI and machine learning to algorithmic trading, systematically developing, backtesting, and optimizing risk-adjusted trading strategies tailored for cryptocurrency markets. As Founder and CEO of Ark Quant Crypto, Mikhail leads research and development of institutional-grade trading solutions that bridge the gap between professional and retail investors. His work integrates predictive modeling, risk management, and portfolio optimization, with a focus on reducing market inefficiencies and improving risk-adjusted returns. Reference: From Market Noise to Signal: Machine Learning and Quantitative Alpha in Financial Markets   Abstract Theme: The convergence of Artificial Intelligence (AI), Quantitative Finance, and Blockchain technologies is reshaping how capital is analyzed, deployed, and optimized across both traditional and decentralized markets. Core Focus: Evolution of Trading: From structured Wall Street quant models to decentralized, data-driven Web3 ecosystems. AI/ML in Action: Transforming market “noise” into predictive signals through reinforcement learning, deep neural networks, and regime-detection models. Systematic Risk Control: Application of institutional-grade portfolio management—drawdown control, position sizing, and multi-asset allocation—to highly volatile crypto markets. Methodology & Frameworks: Integration of AI/ML models with advanced backtesting and optimization platforms such as StrategyQuant and LuxAlgo, validated through out-of-sample and Monte Carlo testing. Use of on-chain analytics, order-flow intelligence, and sentiment data to enhance predictive accuracy. Emphasis on robustness and transparency—bridging quantitative rigor and explainable AI for practical deployment. Applications: Democratization of institutional-grade tools for retail traders and family offices. Quantitative wealth-management frameworks for decentralized funds, DAOs, and algorithmic asset allocators. Emergent AI-agent and blockchain-based ventures building the next generation of autonomous trading, custody, and analytics infrastructure. Key Insight: By uniting quantitative discipline, AI innovation, and venture execution, this research proposes a scalable framework for transforming market complexity into actionable clarity—and for capturing sustainable alpha in the next era of intelligent, decentralized finance.