We need someone with strong data wrangling capabilities, to be able to determine quick ways to clean and merge data. The format of the data is spatial (GIS) but it could also be manipulated in tabular format. GRID3 is a program within CIESIN which is a research center located at the Lamont-Doherty Campus (with office space on the morningside campus) and is part of Columbia’s Earth Institute. Candidates can learn more about the program at the GRID3 website.
Our lab is using clinical notes to phenotype COVID patient outcomes. The aim is to better understand the sequela of COVID-19 from clinical notes.
The question we ask is whether online echo-chambers on social media networks enhance the anxiety and depression of individuals during the COVID19 outbreak. More specifically we want to measure the intensity of the communication about COVID-19 within the echo-chamber of individuals on Twitter and investigate the impact on their subsequent tweets in terms of the level of anxiety and signs of depressive language in their Tweets. We measure echo-chambers by the number of users in the social network that tweeted about COVID-19. We build on an extensive dataset of Twitter users for whom we have identified a large number of demographic and geographic variables (such as the gender, age, ethnicity, location by state, political affiliation) as well as their social network.
The objective of this project is to construct linkages across disparate public health data systems using machine learning tools and assess them for bias and equitable representation of subpopulations defined by demographic and socioeconomic factors.
42% of New York City greenhouse gas emissions result from on-site fossil fuel combustion in residential and commercial buildings; space heating is, by far, the majority contributor. Both New York State and NYC have policies to dramatically reduce emissions that will require a transformation in the way buildings are heated, including major efforts in existing buildings. This transition is inextricably linked to existing energy equity issues that we believe significantly overlap across NYC (and elsewhere). These include unreliable heating in the winter, susceptibility to extreme heat (an increasing occurrence with climate change) and struggles to afford energy needs. Various known data sources for NYC are available, though they are disparate and have not been analyzed holistically. Further, we believe there are potential engineering and policy solutions to these challenges. In this project, the DSI scholar will access (and search for where not yet known to qSEL researchers) relevant data sets, analyze those data sets to identify communities exposed to all or a subset of these issues, and assist qSEL researchers in developing models to evaluate possible solutions. The project has the possibility of extending through Summer 2020, subject to fundraising efforts and the success of the Spring 2020 project.
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.
Single cell sequencing has generated unprecedented insight into the cellular complexity of normal and diseased organ. We are interested in using this technique to understand the mechanisms of eye development, disease and regeneration. We also would like to compare the transcriptomic signatures between mouse models and human tissues. This project involves analysis of large amount of data from single cell sequencing. It requires understanding of statistical analysis and proficient programming skills.
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