Ugo de Noyers

Authored production-quality, Python-based data pipelines for theoretical model simulation and analysis. Leveraged MCMC and DNN algorithms to perform Bayesian parameter estimation and define exclusion boundaries in 30+ dimensional spaces. Optimized parallel computing resources to boost scanning algorithm throughput by a factor 1000 (ideally), reducing iteration time for model validation.

Preliminary work for PhD thesis, focusing on the study of Dark Matter and lepton flavor physics. Comparison with results obtained from a previous theoretical model.

Developed and implemented a Monte Carlo Markov Chain and a Lanczos algorithm to predict behaviors of classical and quantum physics systems. Analyzed and compared results of the two possible physical regimes.

PhD focusing on phenomenological aspects of scotogenic models.

Graduated with high honors specializing in particle physics and statistical methods.