Mehdi Zahedi

• Developed a neural surrogate model to accelerate large-scale epidemic simulators, significantly reducing computational costs while maintaining accuracy.

• Designed and implemented a scalable pipeline to calibrate the surrogate model for 51 U.S. states, leveraging cloud infrastructure for large-scale parallel computing.

• Applied Bayesian inference, deep learning, and time-series forecasting to improve epidemic modeling and simulation efficiency.

• Conducted research on generative models and AI applications in epidemiology, focusing on probabilistic forecasting and disease spread prediction.