We are constantly exposed to inputs from the outside world, but we do not perceive everything we are exposed to. Some inputs are rather weak: we might perceive them at one point in time, but not at another. The state of our brains right before we receive such sensory inputs influences whether or not we perceive them. Brain oscillations are proposed to play a key role in setting these brain states; however, how exactly these brain rhythms influence our perception remains a topic of active research.

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Scholars would assist with Aim 1 of a new R01 working with our lab and the Health Evaluation and Analytics Laboratory at NYU Wagner.

Congenital heart defects (CHDs) are the most common and resource intensive birth defects managed in the United States (US), affecting ~40,000 births per year in the US. (1) One-year mortality for these children is >10%. It is >30% for children requiring neonatal surgery. (2) Yet there are currently limited data on long-term outcomes and health expenditures for these children. Due to marked heterogeneity in disease subtypes and treatments among CHD patients, the power of single-center studies is limited. Multi-center data are siloed in diagnostic or procedural registries or in-patient databases, or are the product of individual investigations. Administrative data may lack clinical precision, as ICD codes for this population are not based on physiology. Further, data on costs and value typically rely on cost-to-charge ratio based costs, which are highly influenced by hospital accounting.

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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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The Milky Way swarms with orbiting satellite dwarf galaxies of astounding diversity. Some galaxies continue to form stars while others stop and dim in brightness. In computer simulations, the evolutionary history of each dwarf galaxy that leads to these differences is known. Galaxies can lose gas and stop forming stars due to early exposure to stellar radiation (reionization), interaction with the hot gas of the host (ram-pressure stripping), or gravitational interactions with the host/dwarf galaxies (tidal effects).

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The Urban Lead Atlas is a collaborative community-based research initiative to create the nations’ first crowd-sourced open online map identifying toxic lead hazards located within the homes, schools, landscape, and lead service lines for water in American cities. The project will begin by integrating data from a small set of cities – New York, Philadelphia, Washington D.C. and Newark - including housing enforcement and lead service line datasets, data on lead dust in schools, and the results of soil lead tests in parks and backyards, on the websites of Columbia University’s Center for Sustainable Urban Development. Ultimately, the goal of the Urban Lead Atlas is to create and populate a fully open online platform that is capable of integrating data from “citizen scientists” and residents regarding the sites of lead hazards in their city’s environment and buildings. This research is important, as experts estimate that over nine million US children have lead blood levels which may cause sub-clinical effects and permanent adverse health, cognitive, and behavior outcomes. The Lead Atlas is intended as the first model for a national effort, the American Lead Atlas project, which seeks to create a national online collaborative map of lead hazards within American cities.

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In 2013, the Chinese government launched its grand initiative to eradicate rural poverty by 2020. The initiative has made great progress since then, yet little rigorous empirical evidence is available due to data limitations. This project aims to use big data through both official and social media to analyze the trends, achievements, and challenges of this initiative and offer implications for the future and from a comparative perspective.

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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.

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