This project works with a novel corpus of text-based school data to develop a multi-dimensional measure of the degree to which American colleges and universities offer a liberal arts education. We seek a data scientist for various tasks on a project that uses analysis of multiple text corpora to better understand the liberal arts. This is an ongoing three-year project with opportunities for future collaborations, academic publications, and developing and improving existing data science and machine learning skills.

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In a globalized world we live in today consequences of catastrophic events easily transgress national borders. Whether it’s a natural disaster, a war or an economic crisis it’s likely to spread out and affect all of us. We propose a framework to model global risks that is not bound to any specific model and is a hybrid of human and machine intelligence. The core of this approach is in using Bayesian Nets of causalities constructed by an analyst equipped with text mining and a map of economic, political and business interconnections.

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The development of computational data science techniques in natural language processing (NLP) and machine learning (ML) algorithms to analyze large and complex textual information opens new avenues to study intricate processes, such as government regulation of financial markets, at a scale unimaginable even a few years ago. This project develops scalable NLP and ML algorithms (classification, clustering and ranking methods) that automatically classify laws into various codes/labels, rank feature sets based on use case, and induce best structured representation of sentences for various types of computational analysis.

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Recently Columbia University, Cornell, and NewYork-Presbyterian have agreed to integrate their clinical (healthcare) and business IT systems onto one shared platform called Epic. The motivating factors to move to Epic are to enhance the patient experience, improve and integrate care, and give our physicians an integrated technology platform that supports the mission of an academic medical center. The intern will assist with developing the “operational” analytics capabilities of Columbia University Medical Center including financial, healthcare operations and healthcare quality analytics.

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The quality of biomedical evidence can affect research sustainability, patient safety, and the public’s trust in biomedical research. However, often the quality of biomedical evidence remains opaque to the public. It is imperative to improve the transparency of evidence quality. This project aims to leverage the public data sources, including but not limited to The ClinicalTrials.gov, The PubMed database for biomedical literature, The National Health and Nutrition Examination Survey (NHANES) database, and so on, to develop and apply novel data mining and visualization methods for appraising the biomedical research evidence, uncovering implicit biases in clinical research designs at different levels, and presenting this information intuitively to the public. Students on this project will acquire or hone their skills in data mining, results presentation, and user interface designs and evaluation.

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