Bio (useful for introductions):

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.

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.


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