Bio (useful for introductions):
Kyle Cranmer is the David R. Anderson Director of the UW-Madison Data Science Institute and a Professor of Physics with courtesy appointments in Statistics and Computer Science. He is also the Editor in Chief of the journal Machine Learning Science and Technology. Cranmer was a Professor of Physics and Data Science at NYU from 2007 – 2022. He obtained his Ph.D. in Physics from the University of Wisconsin-Madison in 2005. He was awarded the Presidential Early Career Award for Science and Engineering in 2007, the National Science Foundation's Career Award in 2009, and became a Fellow of the American Physical Society in 2021 for his work at the Large Hadron Collider. Professor Cranmer developed a framework that enables collaborative statistical modeling, which was used extensively for the discovery of the Higgs boson in 2012. His current interests are at the intersection of physics, statistics, and machine learning.
I am primarily an experimental particle physicist working on the ATLAS experiment at the LHC, though my interests are quite broad. Early in my education I had a difficult time choosing between theoretical and experimental physics, and I decided to focus on the interface with a heavy emphasis on analysis techniques, statistical methodology, and cyberinfrastructure. I try to keep abreast of current thinking in theoretical physics to ensure that the results from the large experiments are relevant and impactful. At the same time, I like to keep the experimental program cleanly factorized from theoretical bias. Thus a theme of much of my work has been to establish the analysis strategy, statistical techniques, and cyberinfrastrure so that the experiments can provide powerful, interpretable tests for a wide range of theories.
Recently, I've been particularly interested in inference in the context of intractable likelihoods, development of machine learning models imbued with physics knowledge, use of 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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- ORCID : 0000-0002-5769-7094