![]() Probabilistic Modeling of Intercellular Interactions that Drive Ferroptosis Susceptibility of Therapy-resistant Cancer CellsĮlham Azizi (Biomedical Engineering), Jellert Gaublomme (Biological Sciences), Brent Stockwell (Biological Sciences) Using multiple nationally representative large-scale exposure and cancer incidence datasets, this project will build a novel model-inference system to study the dynamics of colorectal cancer, test a range of risk mechanisms over the life course, and identify key risk factors underlying the recent increase in young onset colorectal cancer incidence in the United States to support more effective early prevention. Wan Yang (Epidemiology), Mary Beth Terry (Epidemiology), Jianhua Hu (Biostatistics), Piero Dalerba (Pathology and Cell Biology) Modeling the Dynamics of Young Onset Colorectal Cancer Using Big Population Data The project team will integrate existing coarse resolution data with newly collected deep metagenomics and metabolomics data. This project will develop methods for temporal analysis of gut microbiome compositions to better define the risk of infections in liver transplant recipients. ![]() Itsik Pe’er (Computer Science), Anne-Catrin Uhlemann (Irving Medical Center/Infectious Diseases) Interpretable Microbiome Dynamics in Liver Transplant Recipients The proposed work is a step toward the overarching goal of informing effective clean air solutions and reducing public health burdens associated with exposure to air pollution in India. This project seeks to develop methods to extract patterns from multiple datasets and thereby identify the dominant sources of air pollution across India and how they vary in space and time. The team will chart long-term career trajectories of a large number of high-skilled American workers and examine gender and racial variations and construct measures of company environment, especially that pertains to gender and racial equity, and assess its consequences for the career path of different groups of skilled workers.ĭetecting and Attributing Spatiotemporal Variations in Sources of Ground-level Air Pollution with a Modeling Testbed for Integrating Multiple Noisy Satellite DatasetsĪrlene Fiore (Earth and Environmental Sciences, Lamont-Doherty), Daniel Westervelt (Lamont-Doherty, NASA), Jeff Goldsmith (Public Health/Biostatistics), Marianthi-Anna Kioumourtzoglou (Public Health/Environmental Health Sciences), Ruth DeFries (Ecology, Evolution and Environmental Biology), John Wright (Electrical Engineering) ![]() This research team will combine new sources of labor market data, which include online resumes and employee reviews, with data science methods to identify factors and environments that shape gender and racial inequality in high-skilled labor market. Yao Lu (Sociology), Kriste Krstovski (DSI) Gender and Racial/Ethnic Inequality in High-Skilled Labor Market: Gaining New Insights from Online Resume and Reviews Database This team will develop an innovative artificial intelligence system to detect and assess risk for child abuse and neglect within hospital settings that would prioritize the prevention and reduction of bias against Black and Latinx communities. ![]() The broad adoption of electronic health records in clinical settings offers a new avenue for addressing this epidemic. Max Topaz (Nursing), Aviv Landau (DSI), Desmond Patton (Social Work)Ĭhild abuse and neglect is a social problem that has reached epidemic proportions. The following research teams and projects have received 2020 awards.Īrtificial Intelligence-Assisted Identification of Child Abuse and Neglect in Hospital Settings with Implications for Bias Reduction and Future Interventions Two of this year’s six seed grants are jointly sponsored by Columbia’s Irving Institute for Cancer Dynamics. DSI’s Seed Funds Program supports new collaborations to forge long-term relationships among faculty in different disciplines and use data science to transform all fields across Columbia. The Data Science Institute (DSI) at Columbia University has awarded 2020 seed grants to research teams whose projects merge data science with traditional fields to solve pressing societal problems. Academic Programs, Student Services and Career Management.Data Science Racial Equity Advisory Committee. ![]()
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