We are interested in investigating how deaths and hospitalizations resulting from opioid overdoses cluster across space and time in the US. This analysis will be conducted with the aid of two comprehensive databases: 1) detailed mortality data across the US; and 2) a stratified sample of all hospitalizations in the US, which can be subset to select for opioid overdoses. Analyses will be extended to drug type (prescription drugs, fentanyl etc.) and subject demographics (age, race, etc.). We have previously conducted similar cluster analysis for other health phenomena.
Defective efferocytosis, the phagocytic clearance of apoptotic cells, by macrophages is the cause of many human diseases including tumor, autoimmune diseases and atherosclerosis. Enhancing efferocytosis has potential therapeutic benefits. Many key regulators of efferocytosis have been identified, but a systematic approach to map regulators of efferocytosis in an unbiased manner on a genome-wide scale is missing. This project applies innovative genome-wide CRISPR screen to discover novel regulators of macrophage efferocytosis.
We have been studying bladder cancer in a mouse model of the disease and we are seeking to understand the molecular features of the mouse models as they relate to human bladder cancer.
The Federal Communications Commission (FCC) and the Census regularly publish data on U.S. Internet availability, performance and use, at granularities from census block to county and state. The project goal is to answer questions based on the available data, such as “How reliable is Internet access?”, “Who is deploying fiber where?”, “Can we predict reliability of different technologies?”, “Can we predict the deployment of fiber?”
Our goal is use a large pool of homecare data (including structured data, free text clinical notes, and recorded patient-provider phone conversations) to build predictive models that help identify patients at risk for poor outcomes (like hospital admission or falls).
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.
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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