Our research covers the development of new algorithms for physics research based on machine learning and artificial intelligence as well as their application to searches for new physical phenomena in experimental particle physics at the LHC.
See below for more details or look at the publications and recent talks. If you are a student at Hamburg University, contact me (gregor.kasieczka"AT"uni-hamburg.de) for possible BSc and MSc projects in these areas. Open PhD/postdoc positions are listed here.
A good overview of our different research efforts is also provided by this poster: Link
The simulation of particle physics data using traditional techniques is computationally very expensive. Generative models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and autoregressive flows offer a potential way to increase the usable statistics and reduce the computing cost. We focus on simulating the interactions of particles with highly granular calorimeter detectors such as the ILD and the CMS HGCal (arxiv:2005.05334). We also study the quality and amplification properties of generative models (arxiv:2008.06545).
Automated machine learning for physics data
Data in fundamental physics often is collected by complex sensors arranged in irregular geometries. This makes standard machine learning architectures impractical and often leads to custom architectures for physics data. To simplify the adaptation of advanced algorithms in physics research, we curate a collection of datasets and algorithms with the goal of providing automated machine learning for physics data (github).
Robustness and uncertainties
The precise quantification of uncertainties is a crucial pillar of scientific data analysis. Similarly, decision algorithms need to be robust against systematic shifts in data or similar effects. We investigate methods to quantify the uncertainty of machine learning algorithms (arxiv:2003.11099) and to decorrelate their output against arbitrary features (arxiv:2001.05310).