Jason Lo

What goes on in a child’s brain when they encounter a word? As a doctoral student in Hong Kong, Jason Lo used neural imaging to understand how children learn to read. This work sparked his interest in using computational modeling to capture reading behaviors and provide interventions to improve learning.

Jason specializes in crafting custom deep neural networks that simulate children’s reading acquisition. He has used machine learning tools like TensorFlow to run large-scale simulations, improving understanding of how individual differences affect reading performance.

After completing his PhD, Jason joined a startup focused on personalized reading instruction, deploying a machine learning solution. He hopes this work will lead to affordable, effective solutions for children in under-resourced schools.

This winter, Jason joined the team at the Data Science Institute. In this role, Jason is interested in applying machine learning solutions to a wide range of research or business problems and delivering user-friendly products such as open-source packages, APIs, or web apps. Jason is driven by the opportunity to collaborate with researchers across campus, advancing transdisciplinary understanding and discovery.

“Data science can help you understand something better – the mechanism, how to predict it, how to change it. If you have a good understanding of a phenomenon, you can achieve better results.”