Nikolay Aristov works on applying machine learning, AI, and optimization to real-world supply chain and logistics systems. His focus is on turning advanced methods — including reinforcement learning, graph neural networks, simulation, and large-scale forecasting — into tools that support operational and planning decisions in complex environments such as maritime logistics and global trade networks.
His work explores port congestion dynamics, berth scheduling behavior, and network resilience, combining data-driven modeling with system-level understanding of how supply chains actually operate. He is also interested in practical uses of generative AI and NLP in procurement and logistics processes.
Before shifting into applied AI research, Nikolay spent over a decade implementing Advanced Planning Systems for global metals and industrial companies, integrating optimization and scheduling tools with enterprise ERP and MES systems. His career bridges mathematical modeling, software systems, and operational supply chain execution.
He holds a master’s degree in Supply Chain Management from MIT and master and bachelors degrees in Applied Mathematics and Physics from Moscow Institute of Physics and Technology.
