Recent Progress in Simulation-Based Inference
Kyle Cranmer
2021 Invited Talk
Abstract
The physical sciences are replete with high-fidelity simulators: computational manifestations of causal, mechanistic models. Ironically, while these simulators provide our highest-fidelity physical models, they are not well suited for inferring properties of the model from data. I will formulate the emerging area of simulation-based inference and describe how machine learning and probabilistic programming techniques are being brought to bear on these challenging problems. Finally, I will provide examples of how these techniques can impact particle physics at the Large Hadron Collider, astrophysics, neuroscience, and public health.
Speaker
Kyle Cranmer
Kyle Cranmer is a Professor of Physics and Data Science at New York University. He is an experimental particle physicists working, primarily, on the Large Hadron Collider, based in Geneva, Switzerland. Professor Cranmer obtained his Ph.D. in Physics from the University of Wisconsin-Madison in 2005 and his B.A. in Mathematics and Physics from Rice University. 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, statistics, and machine learning.
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