Karl joined Schrödinger in 2013. He is responsible for machine learning research and development across the company. In this role he is one of the Schrödinger members of the Columbia Center for Computational Electrochemistry, a group dedicated to computational research to discover next generation electrochemical technologies.
In 2017 Karl was a visiting researcher at the Pande Lab working on using deep learning techniques for drug discovery. During that time MoleculeNet was posted, a benchmarking paper analyzing machine learning techniques for chemoinformatics. Recently Karl has been concentrating on molecular generative design, combining computational chemistry and chemoinformatics approaches to molecular property prediction, and general purpose machine learned energy potentials.