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.

Research Interests:

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.

Profiles:

Selected research products based on AltMetrics:

Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
Observing Ultra-High Energy Cosmic Rays with Smartphones
10 Simple Rules for the Care and Feeding of Scientific Data
Measurements of Higgs boson production and couplings in diboson final states with the ATLAS detector at the LHC
Evidence for the spin-0 nature of the Higgs boson using ATLAS data
Machine Learning & Likelihood Free Inference in Particle Physics (NIPS2016 Keynote)
Machine learning and the physical sciences
Constraining Effective Field Theories with Machine Learning
Flows for simultaneous manifold learning and density estimation
Discovering Symbolic Models from Deep Learning with Inductive Biases
Equivariant Flow-Based Sampling for Lattice Gauge Theory
The frontier of simulation-based inference
Hamiltonian Graph Networks with ODE Integrators
Sampling using SU(N) gauge equivariant flows