Joel Dyer

Joel Dyer is a Senior Research Fellow at the Oxford Institute for New Economic Thinking and a Senior Research Associate at the University of Oxford's Department of Computer Science. He holds a DPhil in computational statistics and machine learning from the University of Oxford’s Mathematical Institute. His research focuses on agent-based simulation models, likelihood-free parameter inference, and simulation-based planning under uncertainty.

• Affiliations: University of Oxford (Department of Computer Science), Oxford Institute for New Economic Thinking

Education:

• DPhil in Computational Statistics and Machine Learning, Mathematical Institute, University of Oxford

Research Interests:

• Agent-based modelling
• Bayesian inference and likelihood-free methods
• Monte Carlo simulation
• Simulation-based optimization and planning

Research Contributions: His work bridges computational statistics and economic modeling, emphasizing scalable inference techniques for complex systems. Recent efforts include surrogate modeling for simulation optimization and path signatures for time-series analysis.

Labs/Teams: Part of the Complexity Economics Programme at Oxford INET and collaborates with institutions like the Alan Turing Institute and Improbable.