The quantitative and predictive description of electrochemical processes ranging from biological catalysis to the operation of batteries is a frontier challenge in chemistry, materials science and engineering. The difficulty of accurately modeling such systems stems from their broad length and time scales, as well as the complex and heterogeneous nature of their constituents. These challenges have necessitated the use of continuum theories or semiempirical microscopic approaches to the description of the electronic structure and nuclear dynamics of electrochemical processes. While these methods have produced useful, intuition-building rationalizations of observed behavior, the successful design of functional electrochemical systems requires an interdisciplinary approach with cutting-edge computational tools. In the CCCE, we tackle this problem by combining advanced electronic structure theory, statistical mechanics, and machine learning approaches. We partner with Schrödinger, Inc. in New York City, and collaborate with scientists at Columbia and elsewhere to push the frontiers of our understanding of electrochemical systems and processes.