AI research, software engineering, and mathematical finance.
I study how numerical errors affect option prices and how evaluation feedback should guide changes to AI agents. I also build software for financial text analysis and personal research, and write about robotics and technology adoption.
My projects combine mathematical research with working software. The links below lead to the papers, implementations, and results.
Artificial Intelligence and Engineering · Mathematical Finance
| Project | Contribution |
|---|---|
| Agent Evaluation Methodology | ICFA, a method for deciding which evaluation feedback belongs in an agent's prompt update. It connects the diagnosis to what the agent knew, who produced the behavior, and what the optimizer can change. |
| Research Portfolio | Technical analysis of robotics and world models: how language becomes continuous robot action, what different model architectures learn, and what their delivery models mean for adoption. |
| BaZi Context Agent | An interactive interpretation system that compares uncertain birth-hour candidates, fixes a working selection, and introduces personal context through a separate reporting layer. |
| Info Collector | A reading application that turns saved webpages into translations, summaries, and a searchable local library, with a weekly review of what was read. |
| 130U-FinLLM | A training system for a compact financial language model, built around a 24.26M-parameter architecture. Its objective is stance toward a specific asset, with training, recovery, and calibration integrated into the project. |
I develop a mathematical framework for certifying discretization error in arithmetic Asian valuation. Its core is a weighted payoff-remainder bound derived from a Gaussian factor shared across observation dates. This connects the payoff’s nonsmooth structure to computable moment bounds, which I combine with validated transforms for the original projected Euler scheme under Heston stochastic volatility.
In the paper’s one-year Heston Asian call-spread example with twelve monthly fixings, the framework establishes a complete discretization-error bound below 0.011025 price units. The contribution connects payoff analysis, stochastic dynamics and validated computation to give a rigorous monetary guarantee for the discrete valuation.
Paper · Research repository · Results
I establish a deterministic framework for joint pricing-error certification in rough Heston. A finite-history theorem carries continuous fractional Riccati residuals into the pricing exponent, while a joint error set preserves the Fourier perturbations shared across strikes. Together, these results provide rigorous bounds for spreads and portfolio valuations sharing the same model and maturity.
For the paper’s quarter-year 4400–4500 call spread, the framework certifies the stored numerical output with a complete error upper bound of 0.233318843 index points, meeting a 0.25-point accuracy budget. The certificate includes quadrature, omitted frequencies, the infinite tail and arithmetic, connecting fractional dynamics to a checkable guarantee for the valuation.
Paper · Research repository · Results
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