Amelie Marian is part of a team funded by Google to develop new data search methods that function like human memory
Computer scientists Amelie Marian and Thu D. Nguyen received a $62,500 grant from Google to develop a personal data search system that draws from social media pages, email, Skype conversations and work documents, among other things.
The new method of data retrieval mimics the way human memory helps us search for lost items—by making associations and retracing our steps in time and space.
"It's like when you lose your keys. You're not going to search for them by thinking 'red keys' and 'apartment' the way you would use keywords in a Google search. You'd think, 'where was I last? what was I doing the last time I remember having them?'"' explains Marian, an associate professor in the Department of Computer Science.
"As technology evolves at a rapid pace, many evolutional leaps occur in academia,'' says Michael Rennaker, Google's university relations-fellowships manager and research awards specialist. "Professors Marian and Nguyen's research is unique and forward looking."
The duo's system would consolidate many different sources and use them as an online memory pool, which explains the Proustian title of Marian and Nguyen's project: ''Remembrance of Data Past: Using Context in Personal Information Search."
The technology, which would be available as an app, would search mostly personal account information rather than accessing public data and anticipating queries the way Google does.
The duo's system would consolidate many different sources and use them as an online memory pool, which explains the Proustian title of Marian and Nguyen's project: ''Remembrance of Data Past: Using Context in Personal Information Search."
"We have all this digital memory to hold everything we do online, but it's all fragmented. None of its in the same place,'' she says.
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