Chris' research focuses on problems of fairness, bias and representation. His earliest work applied complexity theory to ranked-choice voting systems and complex systems. He later collaborated with social psychologists on a system for controlling in the topology of a communication network among study participants in randomized control trials. It was used to study the impact of network topology on rumor formation. He later applied network science to study social networks, which led to new methods for recruiting study participants from hidden, underrepresented populations (such as men who have sex with men) that are resistant to traditional survey methods, among other things. His current work, which came from a community-based participatory research group that he belonged to for five years, focuses on machine learning problems with multiple solutions, particularly where different subpopulations have distinct preferences for which solutions are best. Such problems are common in settings such as machine translation, offensive language recognition, language complexity recognition, public health surveillance, and many others.