Hestonlabs conducts research at the intersection of mathematics, optimization, and computational science. We develop advanced mathematical models and computational tools for complex problems across finance, technology, and industry.
Optimal transport, representation learning, and generative AI. Applications span computer vision, natural language processing, logistics optimization, and synthetic data generation.
Optimal stopping theory, stochastic control, and reinforcement-style methods. Applications include algorithmic trading, resource allocation, autonomous systems, and strategic planning.
Volatility modeling, regime detection, and forecasting across domains. Applications include financial markets, sensor networks, supply chain optimization, and predictive maintenance.
Python library for solving optimal stopping problems with various numerical methods.
GitHub →Python package implementing various stochastic volatility models and estimation methods.
GitHub →Multi-purpose optimization library for complex systems, resource allocation, and decision-making problems.
GitHub →For research collaborations, consulting inquiries, or questions about our work.