We explore how affective computing and AI can help us study people and their experiences.
We explore how recent deep learning methods can help us find patterns in human signals and relate them to emotion and behavior. We are interested in what these models can reveal, as well as where their interpretations fall short.
We try to understand how people feel, behave, and express themselves. We are interested in how people make sense of situations and respond to the world around them.
Voice, facial expressions, and language offer different perspectives on human experience. We take a multimodal approach, exploring what these signals can tell us when we consider them together and in context.