Peter Washington

Early childhood is the most potent opportunity to impact long-term health and learning. However, there are major bottlenecks to care, with a massive shortage of clinicians for diagnosis and treatment, disproportionately affecting underserved populations. My thesis centers around developing a streamlined system for continuously phenotyping children with potential developmental delays by leveraging distributed non-expert crowdworkers in conjunction with machine learning algorithms. This work involves collecting diagnostically rich information from children and their parents in a secure and trustworthy manner, curating a reliable and capable crowd workforce for labeling behavioral features, and training improved behavioral classifiers for detection of neurodevelopmental concerns.