Immune checkpoint blockade therapy has shown successful clinical outcomes in the treatment of various solid tumors such as head and neck squamous cell carcinoma (HNSCC), melanoma, non-small cell lung cancer (NSCLC) and others. However, immune checkpoint inhibitors work best in patients who exhibit certain tumor biomarkers. In a collaboration with the Department of Hematology Oncology, the Department of Systems Biology, and the Mailman School of Public Health at Columbia University we aim to identify biomarkers which are associated with treatment outcome in patients with solid tumors who underwent immunotherapy. The project includes bioinformatic analysis of sequencing data. Mentoring and training will be provided.

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Freshwater supply is critical for managing and meeting human and ecological demands. However, while stocks of water in both natural and artificial reservoirs are helpful for increasing availability, droughts and floods, as well as whiplash events affect reliability on these systems, posing grave consequences on water users. This risk is particularly salient in the state of California, where many local communities have been plagued by extreme hydrological events. In this current research, we contribute to California’s Water Data Challenge effort where a diverse group of volunteers convened to form a multi-disciplinary team that addresses the crucial issues of extreme events in California using data science approaches. Members include researchers and professionals who come from a range of backgrounds representing academia and private sectors. We combine a range of publicly available datasets with Machine Learning (ML) techniques to explore predictability of extreme events during California’s water years. More specifically, we use a variety of water districts and showcase how ML prediction models are not only able to predict the flow of water at varying time horizons, they capture uncertainties posed by the climate and human influences.

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COVID-19 has changed the way we use the internet, from taking classes to social interactions and entertainment. The FCC publishes a large dataset of network measurements from thousands of homes, with gigabytes of data. The project goal is to analyze the data and answer questions such as: Has the increased usage reduced internet speeds? Can we tell how much people are staying at home from data usage records? Is the increased use of video conferencing reflected in the upload metrics?

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In 2013, the Chinese government launched its grand initiative to eradicate rural poverty by 2020. The initiative has made great progress since then, yet little rigorous empirical evidence is available due to data limitations. This project aims to use big data through both official and social media to analyze the trends, achievements, and challenges of this initiative and offer implications for the future and from a comparative perspective.

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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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