Orienting to a novel event is a rapid shift in attention to a change in one’s surroundings that appears to be a fundamental biological mechanism for survival and essentially functions as a “what is it” detector. Orienting appears to play a central role in human learning and development, as it facilitates adaptation to an ever-changing environment. Thus, orienting can be viewed as an allocational mechanism in which attention sifts through the complex multi-sensory world and selects relevant stimuli for further processing. The selection of stimuli for further processing has implications for what will be encoded into memories and how strong those memory traces will be. The ability to differentiate between relevant and irrelevant input, to inhibit the processing of irrelevant stimuli, and to sustain attention requires control, and inhibitory processes that improve with age.

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The ocean significantly mitigates climate change by absorbing fossil fuel carbon from the atmosphere. Cumulatively since the preindustrial times, the ocean has absorbed 40% of emissions. To understand past changes, diagnose ongoing changes, and to predict the future behavior of the ocean carbon sink, we must understand its spatial and temporal variability. However, the ocean is poorly sampled and so we cannot do this from direct measurements.

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The introduction of a new technology provides individuals and organizations with a large, unowned, and limitless space for communication and organization. How do individuals use or misuse this space in their decision making? Using online discussion platforms, we will analyze what types of discussions thrive - those with depth of discussion or topical complexity or those with cohesive contours? We’ll ask, are there high status actors who are particularly good at recognizing topic gaps which need new conversations? Using social psychological theories with a large-scale archival dataset, we’ll learn more about the impact of new technologies on group decision-making processes.

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The Quadracci Sustainable Engineering Lab (qSEL) has several research efforts related to the low-carbon energy transition, including pathways to decarbonize building space heating. Recent work has produced large model data sets that have supported recent journal articles. Several maps have been produced using QGIS and data has been made public, but user functionality is limited. While we continue to build on these efforts, we also want to make our results and data available more widely for other researchers and policymakers. The large data sets (10 years of hourly data for more than 72,000 census tracts and six scenarios) and different spatial aggregations (e.g. states and electricity planning/operating regions) present challenges. In this project, the DSI Scholar would first work with qSEL researchers to develop an interactive web interface to display maps of relevant analyses and allow users to produce time series data from the underlying models. Additional research would include further analysis at a regional level – likely New York State – to refine the current model based on additional intraregional and energy source data. The project has the possibility of extending through Summer 2020, subject to fundraising efforts and the success of the Spring 2020 project.

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A major challenge to implementing precision medicine arises from patients who share a clinical diagnosis but have different biological causes of disease. Disease subtypes that arise from obscure etiological heterogeneity create inefficiencies in healthcare and attenuate power in clinical trials and research studies. The ability to stratify patients into biologically homogenous subgroups improves the potential for translational research by allowing us to design more powerful studies.

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Columbia Data Science Institute (DSI) Scholars Program

The DSI Scholars Program is to engage and support undergraduate and master students in participating data science related research with Columbia faculty. The program’s unique enrichment activities will foster a learning and collaborative community in data science at Columbia.

Columbia University DSI

New York, NY