Electronic Health Records (EHR) provide a rich integrated source of phenotypic information that allow for automated extraction and recognition of phenotypes from EHR narratives and provide an efficient framework for conducting epidemiological and clinical studies. In addition, when EHR are linked to genetic data in electronic biorepositories such as eMERGE and All of US, phenotype information embedded in EHR can be used to efficiently construct cohorts powered for genetic discoveries. However, limitations arise from repurposing data generated from healthcare processes for research, which can include data sparseness, low quality data and diagnostic errors. Phenotyping algorithms are developed to overcome these limitations providing a robust means to assess case status.

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This project is the first comprehensive examination of African North Americans who crossed one of the U.S.-Canada borders, going either direction, after the Underground Railroad, in the generation alive roughly 1865-1930. It analyzes census and other records to match individuals and families across the decades, despite changes or ambiguities in their names, ages, “color,” birthplace, or other details.

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NYC DDC has initiated a machine learning project to develop predictive model for estimating cost of project and work items. Using the latest technique in Machine Learning and Advanced Statistics, NYC DDC to develop a model that predicts the cost of future and active projects and construction work items in different phases of the lifecycle of the project based on historical data. DDC has partnered with Microsoft who is providing the proof of concept guidance and making tools available for the proof of concept development. DDC is seeking assistance of a data scientist from the Town and Gown program to develop the model.

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New York Presbyterian/Columbia University Irving Medical Center (NYP/CUIMC) serves a high number of racial/ethnic minority and low-income patients. In this project, we will create a data repository of all patients who have completed a universal screen in a clinical encounter for social determinants of health, including food insecurity. The scholar will handle large datasets extracted from the medical record for database creation and data visualizations. The dataset will include patient demographics, food security, and clinical outcomes. This data resource will allow the scholar to partner with researchers to examine predictors of food insecurity, clinical courses, and health outcomes among a large population of patients, including a time period prior to the COVID-19 surge in New York City. The project will be co-mentored through the members of the University-wide Food Systems Network, a novel collaboration of researchers at the Medical Center, Earth Institute, SIPA, and Teacher’s College.

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Getting a better approximation of the age of a NYC’s building can improve assigning the building to a structural type that includes type of construction and relevant building code in effect. Mapping the age and type of building would help NYC DOB and the City on a number of fronts, which include enabling NYC DOB to be more effective in enforcing building and construction safety and evaluating risk when adjacent or nearby subsurface construction is proposed. Furthermore, the more precise characterization of NYC buildings will improve efforts by the City to craft policies aimed at energy efficiency (TWG) as it drives to 80% GHG reductions by 2050 (80X50) and determining natural disaster vulnerability of its building stock (HAZUS).

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The State regulates Construction and Demolition Waste (CDW) — its generation, recycling and reuse — and collects all data on CDW. There is no city source of data for CDW. For the city to innovate policy with respect to CDW by leveraging its capital program as one way to close material loops, which would generate environmental sustainability and financial sustainability benefits, understanding where CDW goes from the demolition process through the recycling process is the most important single step.

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In collaboration with DDC, Microsoft AI team has developed a predictive machine learning model that forecasts monthly distribution of cash flow for DDC’s active projects. DDC intends to operationalize this model and possibly integrate into our dashboards. Assistance is needed of a data scientist to collaborate with DDC in operationalizing the model whereby DDC can prepare the visuals and data scientist can assist with operationalizing the machine learning components.

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