Computational approaches to understanding early typical and atypical experience
- Daniel Messinger, University of Miami (USA)
Scientific Coordinator: Gianluca Esposito
Much development occurs during unstructured social interaction, but expert coding of interaction is resource-intensive. Guided by a dynamic systems perspective, Dr. Messinger uses machine learning to understand social behavior from audio, video, and ultrawideband sensors. Additional computational tools are used to model early face-to-face interaction and predict attachment and autism spectrum disorder. Most recently, Dr. Messinger is working to understand the classroom social networks of children with ASD and hearing loss, and to predict their language development.
Dr. Messinger is a professor in the departments of Psychology, Pediatrics, and Electrical and Computer Engineering, as well the Linda Ray Intervention Center Research Director. He is an interdisciplinary developmental psychologist, and the author of over 120 scientific publications appearing in journals such as Science Reports, Developmental Science, and Molecular Autism. Dr. Messinger uses big behavioral data to understand social, language and emotional development. His research has been continuously funded by the US federal government for 20 years.