As a research scientist at Qualcomm AI Research Amsterdam, I am interested in causality, representation learning, and interactive learning. Before that, I was a postdoc in Kyle Cranmer's group at NYU, working on the intersection of machine learning and physics. My background is in particle physics, I got my PhD in Tilman Plehn's group at Heidelberg University.
He / him
I find it interesting to combine machine learning models with structure that describes our human knowledge of the underlying laws of nature or the symmetries of a system.
One flavour of this is simulation-based inference. Here we combine simulators written by domain experts, neural surrogate models, and a dash of statistics to get the most precise measurements of parameters out of high-dimensional data. This approach has the potential to speed up scientific progress in areas from particle physics to cosmology, from chemistry to robotics, from neuroscience to sociology.
Another direction I am interested in are latent variable models with structured latent spaces. Lately I have been interested in causality, in particular causal representation learning, and how that can help us solve practical problems in interactive learning.
You can find a more or less complete list of my papers on Google Scholar. Here I want to highlight a few favorites:
Weakly supervised causal representation learning.
Johann Brehmer, Pim de Haan, Phillip Lippe, and Taco Cohen.
NeurIPS 2022.
[arXiv]
[bibtex]
Deconfounded imitation learning.
Risto Vuorio, Johann Brehmer, Hanno Ackermann, Daniel Dijkman, Taco Cohen, and Pim de Haan.
NeurIPS workshop on deep reinforcement learning (2022).
[arXiv]
Flows for simultaneous manifold learning and density estimation.
Johann Brehmer and Kyle Cranmer.
NeurIPS 2020.
[arXiv]
[conference]
[bibtex]
[code]
Instance-adaptive video compression: Improving neural codecs by training on the test set.
Ties van Rozendaal, Johann Brehmer, Yunfan Zhang, Reza Pourreza, and Taco Cohen.
Under review (2022).
[arXiv]
[bibtex]
The frontier of simulation-based inference.
Kyle Cranmer, Johann Brehmer, and Gilles Louppe.
Proceedings of the National Academy of Science 117 (2020).
[arXiv]
[journal]
[bibtex]
Simulation-based inference methods for particle physics.
Johann Brehmer and Kyle Cranmer.
Book chapter in Artificial Intelligence for High-Energy Physics (World Scientific, 2022).
[arXiv]
[book]
[bibtex]
Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning.
Johann Brehmer, Siddharth Mishra-Sharma, Joeri Hermans, Gilles Louppe, and Kyle Cranmer.
The Astrophysical Journal 886 (2019).
[arXiv]
[journal]
[bibtex]
[code]
MadMiner: Machine learning-based inference for particle physics.
Johann Brehmer, Felix Kling, Irina Espejo, and Kyle Cranmer.
Computing and Software for Big Science 4 (2020).
[arXiv]
[journal]
[bibtex]
[code]
Likelihood-free inference with an improved cross-entropy estimator.
Markus Stoye, Johann Brehmer, Gilles Louppe, Juan Pavez, and Kyle Cranmer.
NeurIPS workshop on Machine Learning and the Physical Sciences (2019).
[arXiv]
[workshop]
[bibtex]
Mining gold from implicit models to improve likelihood-free inference.
Johann Brehmer, Gilles Louppe, Juan Pavez, and Kyle Cranmer.
Proceedings of the National Academy of Science 117 (2020).
[arXiv]
[journal]
[bibtex]
Constraining effective field theories with machine learning.
Johann Brehmer, Kyle Cranmer, Gilles Louppe, and Juan Pavez.
Physical Review Letters 121 (2018).
[arXiv]
[journal]
[bibtex]
[code]
A guide to constraining effective field theories with machine learning.
Johann Brehmer, Kyle Cranmer, Gilles Louppe, and Juan Pavez.
Physical Review D 98 (2018).
[arXiv]
[journal]
[bibtex]
[code]
Hierarchical clustering in particle physics through reinforcement learning.
Johann Brehmer, Sebastian Macaluso, Duccio Pappadopulo, and Kyle Cranmer.
NeurIPS workshop on Machine Learning and the Physical Sciences (2020).
[arXiv]
[workshop]
[bibtex]
[code]
Better Higgs Measurements Through Information Geometry.
Johann Brehmer, Kyle Cranmer, Felix Kling, Tilman Plehn.
Physical Review D 95 (2017).
[arXiv]
[journal]
[bibtex]
Together with Felix Kling, Irina Espejo, Sinclert Perez, and Kyle Cranmer, I have developed the MadMiner library, which automates machine learning–powered simulation-based inference techniques for particle physics experiments.
My GitHub page contains the code used for most of my pre-Qualcomm papers.
Over the years, I had a lot of fun presenting our research in different places. See this repository for a list and PDF versions of my slides. I'm always happy to talk about our recent papers.
Write me at jbrehmer@qti.qualcomm.com (work) mail@johannbrehmer.de (personal), connect on Twitter, or come visit me in Amsterdam!
