Particle physics aims to answer profound questions about the fundamental building blocks of the Universe through enormous data sets collected at experiments like the Large Hadron Collider at CERN. Inference in this context involves two extremes. On one hand the theories of fundamental particle interactions are described by quantum field theory, which is elegant, highly constrained, and highly predictive. On the other hand, the observations come from interactions with complex sensor arrays with uncertain response, which lead to intractable likelihoods. Machine learning techniques with high-capacity models offer a promising set of tools for coping with the complexity of the data; however, we ultimately want to perform inference in the language of quantum field theory. I will discuss likelihood-free inference, generative models, adversarial training, and other recent progress in machine learning from this point of view.
Kyle Cranmer (New York University)
Kyle Cranmer is an Associate Professor of Physics at New York University and affiliated with NYU's Center for Data Science. He is an experimental particle physicists working, primarily, on the Large Hadron Collider, based in Geneva, Switzerland. He was awarded the Presidential Early Career Award for Science and Engineering in 2007 and the National Science Foundation's Career Award in 2009. Professor Cranmer developed a framework that enables collaborative statistical modeling, which was used extensively for the discovery of the Higgs boson in July, 2012. His current interests are at the intersection of physics and machine learning and include inference in the context of intractable likelihoods, development of machine learning models imbued with physics knowledge, adversarial training for robustness to systematic uncertainty, the use of generative models in the physical sciences, and integration of reproducible workflows in the inference pipeline.
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