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Room: Conference Room, No. 306, Bldg: Eagan Research Center

April 2017

Small Data: A Big Challenge for Classical Machine Learning

We live in a world inundated with data: websites, mobile devices, security systems, and even small wireless sensor systems constantly collect data. In fact, such systems often collect so much data that traditional data processing techniques are insufficient. This is the big data problem. However, sometimes things go the other way: there is a critical constriction in the size of the data at some point in the processing pipeline that prevents traditional machine learning techniques from working. We call this…

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Augmented Cognition or how to enhance human information-processing capabilities

Creating augmented cognition systems for healthcare providers and patients with chronic conditions, she is working on systems that do a better job of presenting and organizing information for doctors and healthcare professionals to enhance human cognition rather than replacing it.  These systems have three primary components: the sensing element, the analytic element, and the feedback element. Robust analytics is the cornerstone of the system. She uses both machine learning models and biomechanically-inspired structural models to develop this systems Speaker(s): Prof. Sara Ostadabbas,…

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