Columbia University Data Science Institute is pleased to announce that the Data Science Institute (DSI) and Data For Good Scholars programs for Spring-Summer 2021 are open for application.

The goal of the DSI Scholars Program is to engage Columbia University’s undergraduate and master’s students in data science research with Columbia faculty through a research internship. The program connects students with research projects across Columbia and provides student researchers with an additional learning experience and networking opportunities. Through unique enrichment activities, this program aims to foster a learning and collaborative community in data science at Columbia.

The Data For Good Scholars program connects student volunteers to organizations and individuals working for the social good whose projects have developed a need for data science expertise. As “real world” problems with real world data, these projects are excellent opportunities for students to learn how data science is practiced outside of the university setting and to learn how to work effectively with people for whom data science sits outside of their subject area.

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Genome wide CRISPR lethality screens show broad variability in cellular fitness phenotypes across cancer. We postulate that genes with overlapping functions should deliver similar responses enabling functional annotation of uncharacterized genes. Here we will build a network connecting genes based on the similarity of their knockout phenotypes, benchmark this network using protein interaction databases and functional transcriptomics, and leverage network analyses to identify mutational and transcriptional modulators of functional complexes.

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A wealth of evidence for the automaticity of perceptual organization processes points toward the existence of a global-to-local processing bias in early perceptual stages. Global features are encoded and spontaneously reported during early conscious vision, resulting in the perception of coherent objects prior to identifying detailed information. Yet, results from experiments that presented illusory figure presentation below the perceptual threshold to study the reliance of perceptual organization on visual awareness have shown conflicting findings, leaving open the question of how global features interact during figure perception. The present study will examine the interaction between symmetry and perceptual completion under conditions of restricted awareness.

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This project has a two-fold aim. First, we seek to determine what makes an idea seem novel versus ordinary and if there is an ideal mix of the two. Second, building on these findings, we build a generative model that suggests tweaks to an idea that enhance its perceived creativity and appeal. We will pursue these two aims using 69K recipes and reviews from allrecipes.com. We will use NLP approach to extract important features from the recipe such as ingredients, preparation instruction and review content.

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The Acute Care and Emergency Referral Systems (ACERS) Project is a three-year, USAID-funded implementation research and capacity building project that aims to contribute to the improvement in maternal and newborn survival rates by increasing care-seeking behavior, strengthening emergency referral and dispatch systems, and providing high quality emergency obstetric and newborn care (EmONC) services in the Northern and Oti Regions of Ghana.

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Using a validated survey called the Digital IT Maturity Survey, the team is conducting a three-wave, longitudinal, repeated measures survey in a national sample of NHs. Currently, in year 2 of recruitment and analyzing year 1 data. Methods include an examination of the relationships between NH IT Maturity and stages of maturity, and nationally-reported, publicly-available NH Quality Measures available through Nursing Home Compare over three consecutive years. Specific aims are: 1) Explore NH IT maturity using the survey and staging model during a 3-year national assessment 2) Examine if NH IT maturity is associated with CMS quality measures in a national sample of NHs over 3 years. This study includes a survey of NH IT Maturity in a nationally representative sample including 10% of NHs recruited from each state in the United States (N=1,570). Statistical analysis will be done using the software SAS v9 (SAS Institute Inc., Cary, NC, USA). Since the sampling method involves stratification by state and since the sampling weights assigned to homes will depend on the number of respondents within each state, the analysis must take the complex sampling design into account. SAS procedures including SURVEYMEANS, SURVEYFREQ, SURVEYLOGISTIC, and SURVEYREG will be used for such analyses.

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Prediction Markets have been used to forecasts outcomes of research interest using market mechanism (See https://www.nature.com/news/the-power-of-prediction-markets-1.20820). A decentralized prediction market, Augur, has been created on blockchain for betting purposes (See, https://www.augur.net/). An alternative approach to prediction market has been proposed in Dalal et al (https://www.sciencedirect.com/science/article/abs/pii/S0040162511000734). This project proposes to develop a new model for decentralized prediction market which can be used to elicit opinions of university researchers on socially important issues. Specifically, the project will use Ethereum based platform to develop a smart contract and an ERC-20 compliant token for researchers to participate in the new market.

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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