HCI & Design at the University of Washington
More than 10% of the population has dyslexia, and most are diagnosed only after they fail in school. My work is changing this through scalable early detection and tools that help people with dyslexia read and write better. To detect dyslexia, I am developing machine learning models that predict reading and writing difficulties by watching how people interact with my web-based game Dytective. My experiments have revealed differences in how people with dyslexia read and write, and I have developed a series of tools that integrate these results to help people with dyslexia read and write better. These tools are used by tens of thousands of people, which apart from supporting users, also serve as living laboratories in which to develop and prove techniques for detection and intervention. Moving forward, we are working with schools to put our approach into practice at scale to finally eliminate school failure as a primary way dyslexia is diagnosed.
Luz Rello is a Post Doctoral Fellow at Carnegie Mellon University in the Human-Computer Interaction Institute. She is also an Ashoka Fellow, invited expert to W3C-WAI and co-founder of the Cookie Cloud team that creates applications from research results. She holds a degree in Linguistics (Complutense University of Madrid), a MSc in Natural Language Processing (University of Wolverhampton) and a Ph.D. in Computer Science (Pompeu Fabra University). She has received a number of awards, including the MIT Technology Review ‘Innovators under 35 Award Spain’ (2014) and the European Young Researchers’ Award (2013) for her work on applying technology for dyslexia using Linguistics, HCI and NLP. Her IDEAL eBook reader and Dyseggxia game (Vodafone Foundation Mobile for Good Europe Awards 2013) have received nearly one hundred thousand downloads in more than 70 countries. Currently, she is working to detect dyslexia at large scale and on bringing this work to society via her non-profit organization Change Dyslexia.