Chris joined the Document Intelligence Lab at Adobe Research in 2018 as a Research Scientist after performing a Summer research internship. He graduated with a PhD in computer science from Brigham Young University and developed new deep learning models to solve document analysis problems as part of his dissertation. His research interests span a variety of document intelligence topics, including layout analysis and generation, text recognition, chart data extraction, and semantic understanding of documents. The big project occupying most of his time deals with table understanding. Deep learning models are his favorite hammer when confronted with a new nail (problem), but if the nail turns out to be, e.g., a screw, he has other tools in his toolbox too.