Anja Butter
Introduction
My research:
Improvement of event generation and inference with machine learning. Development of ML based methods for unfolding.
My expertise is:
Generative networks, Bayesian networks, Unfolding, Monte Carlo simulation
A problem I’m grappling with:
Proper uncertainty estimation for ML problems
I’ve got my eyes on:
Anything that helps improve precision and efficiency
I want to know more about:
How to include analytics in ML
Projects
Transfer learning for jet energy scales
Include (in-situ) data information into the calibration of jet energy scales (JES) at an early ML-based stage, and improve the jet energy resolution due to considering the correlations among the variables relevant for the JES calibration and to integrating the in-situ constraints at the step of the training of the ML algorithm.
Denoising diffusion probabilistic models
Develop novel methods of inverse problem solutions based on denoising diffusion probabilistic models; investigate new approaches to uncertainty estimation based on stochasticity of denoising diffusion models; introduce new techniques of conditional generation based on concatenation, cross-attention and bias.
Physics Model-Based AI for Rare Events
Develop a novel strategy to perform reliable extrapolation with machine learning (ML) by space-unwrapping; efficiently handle extreme value modelling via machine learning using model-based strategies like exponentially tiled estimators; solve stochastic partial differential equations via physics informed neural networks for transport equations.
