Corey Adams is an Computational Scientist in the Argonne Leadership Computing Facility (ALCF), with a joint appointment in the Argonne Physics Division.  His research interests include fundamental physics, scalable deep learning, and high performance python and datascience applications.  Currently, he works on scaling algorithms for fundamental physics research with machine learning, including segmentation of high resolution particle physics datasets, sparse convolutional networks for 3D datasets, and surrogate models for nuclear theory calculations.

In Physics, Corey leads the Physics Division’s neutrinoless double beta decay research group, operating a prototype optical Time Projection Chamber (TPC) and researching the integration of barium tagging sensors into a full detector.  He is also a member of the NEXT Collaboration, a leading neutrinoless double beta decay program with gaseous xenon time projection chambers.  For his research into Optical TPCs and integration of barium tagging, Corey received a DOE Office of Science Early Career aware in 2021.

Corey received his bachelor’s degrees in physics and mathematics from the University of Rochester (2011) and subsequently his PhD. in Physics at Yale University (2016).  After a postdoc at Harvard, stationed at Fermi National Accelerator Lab, he joined the datascience group in ALCF in 2018.  He has been a joint appointment with the Physics Division since 2019