2013 Poster Sessions : Quantified Mind: Efficient Scalable Assessment of Within-person Variation in Cognitive Abilities

Student Name : Yoni Donner
Advisor : Daphne Koller
Research Areas: Artificial Intelligence
Abstract:
Cognitive tests are commonly used to compare individuals to a reference population, for selective admission or diagnosis of pathologies. Within-person assessment, where an individual is tested multiple times and their performance is compare to their past performance, provides more accurate estimates of environmental and intervention-based effects on performance, and the ability to track processes such as development, aging and disease progression. I present Quantified Mind, a novel scalable online platform designed specifically for the efficient assessment of within-person variation in cognitive abilities, describe some challenges in building such a system and their solutions, and show some preliminary results.

Bio:
Yoni Donner is a 3rd-year PhD student in Computer Science working with Prof. Daphne Koller. His research applies probabilistic graphical models and Bayesian machine learning in the fields of computational biology and cognitive psychology, specifically to study aging and interventions against age-related diseases and cognitive decline.