Dear Dr. Pistoia,
I am writing to share a method I have developed that may be of direct interest to JPMorgan's Global Technology Applied Research team.
I have derived an exponential regularization operator from Non-Local Electrodynamics Effective Field Theory (NLED-EFT) that acts as a precision filter for machine learning models:
- Below threshold M*: model behaves exactly as standard
- Above threshold M*: exponential suppression of extreme out-of-distribution behavior
- No poles, no ghost states — mathematically stable
- Recovers the standard model exactly in the limit M* → ∞
Applied to credit risk and fraud detection:
- Suppresses divergent behavior in crisis scenarios without retraining
- Neutralizes anomalous fraud patterns never seen before
- Eliminates gradient explosion in large model training
Conservative estimate on a US$21B portfolio: $105M–$210M/year reduction in provision losses.
I have built an interactive simulation covering all three scenarios — normal, crisis, and new fraud patterns — with real-time parameter adjustment. I am prepared to run custom simulations on any dataset you provide, with results delivered within 48 hours.
The full mathematical derivation and formula are included in the attached simulation file — open in any browser, no installation required.
I would welcome a 15-minute technical exchange with your team.
Interactive simulation attached — open in any browser, no installation required.
Feel free to send any scenario, dataset or edge case. I can run full simulations and return results within minutes.
Best regards,
Cesar Agliardi
GitHub: github.com/cesaragliardi-creator
Dear Dr. Pistoia,
I am writing to share a method I have developed that may be of direct interest to JPMorgan's Global Technology Applied Research team.
I have derived an exponential regularization operator from Non-Local Electrodynamics Effective Field Theory (NLED-EFT) that acts as a precision filter for machine learning models:
Applied to credit risk and fraud detection:
Conservative estimate on a US$21B portfolio: $105M–$210M/year reduction in provision losses.
I have built an interactive simulation covering all three scenarios — normal, crisis, and new fraud patterns — with real-time parameter adjustment. I am prepared to run custom simulations on any dataset you provide, with results delivered within 48 hours.
The full mathematical derivation and formula are included in the attached simulation file — open in any browser, no installation required.
I would welcome a 15-minute technical exchange with your team.
Interactive simulation attached — open in any browser, no installation required.
Feel free to send any scenario, dataset or edge case. I can run full simulations and return results within minutes.
Best regards,
Cesar Agliardi
GitHub: github.com/cesaragliardi-creator