3 papers in the archive (2026), average empirical rigor 7.5/10, average math complexity 6.5/10. Quadrant mix: Holy Grail ×3.
Works mostly in Market Simulation & Agent-Based Models, Volatility Modeling & Forecasting, Options & Derivatives Pricing, Machine Learning for Market Forecasting.
Frequent co-authors: Abdulrahman Alswaidan (2), Julia Sun (1), Zheyu Jin (1), Jiawei Zhang (1).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
Generating realistic synthetic option prices requires implied volatility as an input, yet implied volatility is itself derived from observed option prices, creating a circular dependency that limits synthetic data for machine-learning and risk-analysis applications. We break this circularity with a
Generating synthetic financial time series that preserve the statistical properties of real market data is essential for stress testing, risk model validation, and scenario design. Existing approaches struggle to simultaneously reproduce heavy-tailed distributions, negligible linear autocorrelation,
Attention heads retrieve: given a query, they return a weighted average of stored values. We showed that this computation is one step of gradient descent on the modern Hopfield energy, and that Langevin sampling from the corresponding Boltzmann distribution yielded stochastic attention, a training-f