Recent Publication
Deep Surrogates for Finance
Deep learning surrogates for high-dimensional option pricing, published in the Journal of Financial Economics (2026).
Research
My research lies at the intersection of computational macroeconomics, finance, and artificial intelligence. I build deep learning methods and scalable numerical frameworks for solving high-dimensional dynamic models that were long considered computationally intractable. These tools allow richer quantitative analysis of climate policy, macro-finance, dynamic contracting, and asset pricing.
My work has appeared in outlets including Econometrica, the Review of Economic Studies, the Journal of Financial Economics, the Economic Journal, and the Annual Review of Economics.
Featured Work
New Paper
Equilibrium World Models
Structural world models: a deep-learning method for solving dynamic stochastic economic models.
Open Research Software
The Climate in Climate Economics
Open-source code for modern climate emulation and integrated assessment work in economics.
News
November 2025
Machine Learning for Dynamic Incentive Problems (with P. Renner) accepted at Review of Economic Studies. Accepted
Past Visits & Service
- Visiting Scholar, UPenn Economics (January–June 2023)
- Visiting Assistant Professor, MIT Sloan (October–November 2019)
- Visiting Faculty, Yale Cowles Foundation (2018–2019)
- Visiting Fellow, Hoover Institution, Stanford (2015–2017)
- Associate Editor, Journal of Financial Econometrics (2023–2025)
Research Areas
- Computational macroeconomics and finance
- Machine learning for dynamic economic models
- Climate economics and integrated assessment
- High-performance computing and numerical methods