Daniel Cherdack

Background

About Daniel Cherdack

Neutrinos are the most abundant particles in the universe, after the photon, but almost never interact. These ghostly particles give us a unique view into the underlying structure of matter and energy in the universe. And by studying their properties we hope fill in the missing link between the theory of the big bang and the universe we observe today. I work on two neutrino experiments, one that is currently operating in Japan called T2K, and a next-generation experiment in the US called DUNE. For T2K I work on understanding how neutrinos interact with normal matter, while for the DUNE, I lead an international team of scientists working to ensure that the proposed design will be able to achieve the primary physics goals of the experiment. In these roles I have developed an advanced suite of analysis, and problem solving skills, along with knack for leadership and mentoring. I am one of the pioneers of using machine learning and multivariate analysis in neutrino physics, and am now interested in convolutional neural networks to help isolate data samples, and adversarial CNNs to generate fast simulations. Other skills include data visualization, parameter estimation in high dimensional spaces, systematic error evaluation, and advanced statistical analysis.