My interests are in the intersection of natural language processing and computer vision as well as fairness in computing. I use the structure of language to help design computer vision systems, for example, Situation Recognition (demo), for modeling how objects are interacting in events using semantic roles. Sometimes, I like to work on pure NLP and I have recently released a new dataset for multi-turn information seeking dialogs from documents, QuAC. With AI systems getting better and being more broadly applied, its important to think about how the models might treat people, in similar circumstances, differently. Unfortunately, I've found some systems I built behave differently for men and women Machines Taught By Photos Learn a Sexist View of Women (and systems many others have built) but I've been actively researching how to do better without sacrificing accuracy.