Optimizing Memory using Neural Information (OMNI)

The goal of the OMNI project is to develop a computational framework for the prediction and optimization of human memory which is informed by indirect measurements of brain activity. Simply put, we aim to record people's brains while they learn things, predict which things they learned successfully and which they didn't, and then devise better ways to teach them. The project applies state of the art in machine learning and cognitive modeling methods to the design of next-generation intelligent tutoring systems.

There are seven categories of project releases: data, publications, posters, abstracts, pre-prints, modeling code, and blog posts/news. The following list shows all current and planned data releases (white buttons are planned and upcoming releases):

• Shannon Tubridy (Post-doc)
• Todd Gureckis (PI)
• Lila Davachi (Co-PI)
• David Halpern (PhD Student)
• Pam Osborn Popp (PhD Student)
• Camille Gasser (Research Assistant)
• Mike Mozer (Advisor/Consultant)

The project is currently supported by National Science Foundation grant DRL-1631436 and by a startup grant from the NYU College of Arts and Science.