Craig's background is diverse in that he has significant training in both computational genomics and developmental genetics. His undergraduate education is in computer science, with a focus on machine learning. For his graduate degree, he worked to apply his computational skills to the analysis of some of the first vertebrate genomes. For his postdoctoral studies, he focused on developmental genetics so that he could perform experiments at the bench to test his computational predictions. In his own lab, he mixes computational genomics and experimental genetics to establish a virtuous cycle. The computational predictions define a set of promising candidates for wet-lab experiments. The wet-lab experiments then either support or disprove the computational predictions, which allows his group to then improve the computational model. This diverse background has allowed him to create an interdisciplinary environment where his lab has had students with backgrounds in mathematics, neuroscience, developmental biology, and genetics.