The ocean has absorbed the equivalent of 41% of industrial-age fossil carbon emissions. In the future, this rate of this ocean carbon sink will determine how much of mankind’s emissions remain in the atmosphere and drive climate change. To quantify the ocean carbon sink, surface ocean pCO2 must be known, but cannot be measured from satellite; instead it requires direct sampling across the vast and dangerous oceans. Thus, there will never be enough observations to directly estimate the carbon sink as it evolves. Data science can fill this gap by offering robust approaches to extrapolate from sparse observations to full coverage fields given auxiliary data that can be measured remotely.

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The goal of this project is to develop and validate a deep neural network that predicts a child’s emotion and cognition. DSI scholars will implement 3D convolutional neural networks on brain imaging data from thousands of children to predict cognitive, emotional, and socio-developmental variables. Statistical evaluation of the model performance will be conducted. The scalable deep neural network analysis will help find brain underpinnings of cognition and emotion.

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Project components: (i) Monitoring of traffic intersections, using bird’s eye cameras, supported by ultra-low latency computational/communications hubs; (ii) Simultaneous video-based tracking of cars and pedestrians, and prediction of movement based on long-term observations of the intersection; (iii) Real-time computational processing, using deep learning, utilizing GPUs, in support of ii; (iv) Sub-10ms latency communication between all vehicles and the computational/communication hub, to be used in support of autonomous vehicle navigation.

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Using machine learning to conduct brain state classification at real-time on EEG/fNIRS/fMRI data.

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