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.
Mathematical frameworks for sequential decision-making under uncertainty. Applications span algorithmic trading, resource allocation, autonomous systems, and real-time optimization problems.
Recent work: Multi-agent optimal stopping in distributed systems
Applications of optimal transport theory to optimization problems across domains. Focus on computational methods for logistics, machine learning, computer vision, and resource allocation.
Recent work: Optimal transport for generative AI and logistics optimization
Advanced time series modeling and forecasting for complex dynamic systems. Applications include financial markets, sensor networks, supply chain optimization, and predictive maintenance.
Recent work: Multi-scale forecasting for complex systems
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.