Forskningsprofil

Peter researches machine learning methods for Finance. More specifically, he develops fast and accurate methods for option valuation and calibration. In his research, he combines classical option theory methods with modern methods such as neural networks, tree regression, and differential machine learning to produce powerful tools for trading and risk management. He hopes these tools can improve solutions to complex, real-world problems, changing the world.