Discriminatory development policies have systematically relegated certain populations to undesirable locations including low elevation areas at risk of flooding. As the climate changes, many properties will no longer be inhabitable and others, especially houses in floodplains, will suffer damage due to more frequent and significant flooding. Current U.S. federal policy funds flood risk mitigation measures, such as property acquisition, relocation and retrofitting, however depending on various factors at the sub-county level, these actions can have disproportionate benefit to high income areas and not extend to vulnerable populations. We investigate patterns related to potential disproportionate availability and access to government linked programs, exploring different types of climatic factors using flood insurance claims data from NFIP. Work with the intern will build off existing research on programmatic wide and event specific analysis in the Carolinas to explore patterns that may be of interest specifically to state and county level decision makers to evaluate how communities are benefiting from existing programs and to ensure equity. We plan to publish an event specific research article using high resolution data on the distribution of risks and benefits following a major disaster.

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Structural variants (SVs) are large genomic alterations which can be implicated in disease. This project will focus on using novel genomic techniques to identify structural variants in genomic cold cases with neurological disorders. These “cold” cases which have previously remained unsolved with standard genomic approaches. We will use optical genome mapping and long read sequencing, together with novel bioinformatic techniques to detect and analyze structural variants.

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LEAP will offer research experiences for undergraduate students as summer research internships as part of LEAP’s research through synergistic innovations in data science and climate science. This includes, but is not limited to, physics-informed and causally-informed machine learning, novel ML-based subgrid parameterizations for Earth System Models, global parameter inference, and new ML- based diagnostics and metrics for evaluating these models, with a focus on the Community Earth System Model (CESM). The center is committed to building a diverse research community at the intersection of geosciences and data sciences with the objective to build a LEAP community on par with the US population in terms of gender and race diversity.

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Road traffic crashes involving child passengers, child pedestrians, and child bicyclists are the leading cause of death for people aged 5 to 15 years in the USA. A total of 10,344 children died on US roads in the decade from 2010-2019; a further 4.2 million were hospitalized. Urban design—meaning the overall physical form of cities—is a modifiable environmental feature that can be changed to reduce the immense burden due to child road traffic injuries. Altering the overall configuration of a city’s transportation network affects the way children and other road users routinely travel through urban space, thereby altering children’s risks for being injured or killed in a road traffic crash.

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Future wireless networks will use high-frequency millimeter-wave (mmWave) links for transmitting and receiving information with high throughput. A key difference between mmWave links and conventional sub-6GHz links is that mmWave links are severely affected by weather conditions. Students working on this project will use a state-of-the-art mmWave radar to assess the impact of wind speed, temperature, humidity, and other factors on the high-frequency link. The end goal of the project is to develop a classifier that can infer weather conditions based on the signal received from the mmWave radar. In this project, students are expected to learn how the mmWave radar works, design experiments to obtain labeled data, perform measurements, and develop the classifier.

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Call for Faculty Participation- Spring/Summer 2022.

The Data Science Institute is calling for faculty submissions of research projects for the Spring and/or summer 2022 sessions of the Data Science Institute (DSI) Scholars Program. The goal of the DSI Scholars Program is to engage undergraduate and master students to work with Columbia faculty, through the creation of data science research internships. Last year, we worked with over 40 projects and received more than 350 unique applications from Columbia Students. The program’s unique enrichment activities foster a learning and collaborative community in data science at Columbia. Apply here.

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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