All complex behaviors require animals to coordinate their perception and actions. To successfully achieve a goal, a decision maker (DM; be it a human, animal, or artificial agent) must determine which action to take and, faced with much more information than she can fully process, must decide which source of information to consult to best guide that action. But in contrast with natural tasks, traditional research has focused primarily on action selection but eschewed the process of information demand. We aim to fill this gap by investigating the factors that motivate people to become curious and seek information. We are collecting behavioral data from a large sample of participants on a battery of online tasks testing various aspects of curiosity, and seek a DSI scholar who can quantitatively analyze the data. The scholar will be supervised by two co-PIs: Jacqueline Gottlieb, in Columbia’s Neuroscience Department and Zuckerman Institute, and Vince Dorie, in the DSI. The scholar will obtain training with advanced data analytic methods and the opportunity to co-author what is expected to be a high impact paper with interdisciplinary appeal in economics, neuroscience, and psychology.

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We have been studying bladder cancer in a mouse model of the disease and we are seeking to understand the molecular features of the mouse models as they relate to human bladder cancer.

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Big data with temporal dependence brings unique challenges in effective prediction and data analysis. The complex high-dimensional interactions between observations in such data brings unique challenges which standard off-the-shelf machine learning algorithms cannot handle. Even basic tasks of clustering, visualization and identification of recurring patterns are difficult.

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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 funded study examines policies that regulate the PO supply, including those related to prescription drug monitoring programs, pain management clinic laws, and prescribing limits. In this supplement, we add a set of complementary opioid policies that affect access to treatment for opioid use disorder (e.g., Medicaid coverage of medication for opioid use disorder).

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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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