DSI Scholars is different from conventional researh experience for students programs by providing enrichment activities that build a community of data science learning and research.
Columbia University Data Science Institute is pleased to announce the launch of the Data Science Institute (DSI) Scholars Program for the Inaugural Class of Summer 2018. The goal of the DSI Scholars Program is to engage Columbia’s undergraduate and master students in data science research with Columbia faculty through a research internship. The program connects students with research projects across Columbia and provides student researchers with an additional learning experience and networking opportunities. Through unique enrichment activities, this program aims to foster a learning and collaborative community in data science at Columbia.
Analysis of the relation between the activity of all the neurons in the cnidarian hydra and its behavioral repertoire.
CTV’s core mission is to facilitate the transfer of inventions from academic labs to the market for the benefit of society. In a typical year, CTV receives ~400 inventions, completes ~100 licenses and options, and helps form ~20 startups. A good video summary of CTV is here: https://vimeo.com/110193999.
We house the world’s largest dataset of once-secret documents on industrial pollution, unleashed from the vaults of corporations like DuPont, Dow, and Monsanto in toxic tort litigation. We are applying data science methods to analyze and render this material useable to a broad audience.
Alzheimer’s disease and related dementia (AD/dementia) represent a looming public health crisis, affecting roughly 5 million people in the U.S. and 11% of older adults. As with other chronic conditions, racial/ethnic and socio-economic disparities exist in the prevalence and burden of illness. However, less is known about how disparities in access to care influence the care trajectories – i.e., the scope, frequency and sequence of services used across healthcare settings – of those with AD/dementia.
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.
