The project is analyzing how flood risk affects the composition of coastal neighborhoods. Understanding the effects of flooding on residential mobility and livelihoods is important for designing policies that support equitable adaptation to coastal flooding, addressing inequities in the impacts of flooding. Increasing flood risk may lead to gentrification as wealthier homeowners who have the resources to defend their homes against rising waters displace lower income households in waterfront locations, or coastal neighborhoods may deteriorate if flood-prone real estate loses value and/or economic activity and incomes decline. The study focuses on the effect of flooding associated with Hurricane Sandy on the composition of coastal neighborhoods in New York and New Jersey. The study will use panel data from the American Community Survey from 2010 to 2020 and data on flooding due to Sandy to compare the change in neighborhood characteristics such as income distribution, education, race, gender, and age over time in locations that were affected by Sandy and similar communities that were not. Dr. Malgosia Madajewicz will mentor the scholar, meeting at least once a week in person or virtually, and more often if needed. She will communicate with the scholar by email and phone as often as necessary.

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Understanding the effects of environmental exposures on child health and development is crucial to promote positive health outcomes in adulthood. To advance knowledge in this area, our lab is part of the NIH-funded Environmental influences on Child Health Outcomes (ECHO) Program. As of April 2021, the cohorts have collected data from over 90,000 participants which includes over 57,000 children. The selected student(s) will be involved in projects investigating the relationship between a variety of prenatal and postnatal exposures and physiological variables. Specifically, we aim to build multidimensional associative and predictive models to investigate the effects of prenatal maternal drinking and smoking on development of cardiac and neural systems non-invasively assessed at 4-, 5-, 7-, 9- and 11-years of age. We will advance prior work on the relationship of maternal depression and child development by including depression measures at 1 and 4 years post-delivery and prospective assessment of multiple domains of child development and applying machine learning methods for trajectory identification.

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Traffic safety has become an emerging issue in the city of New York since the pandemic. This project aims to use causal inference methods to investigate how traffic safety countermeasures (including adding more bike lanes) that have been deployed by the city affect traffic fatalities.

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Business email compromise (BEC) is a prevalent cyber attack, where the attacker impersonates a figure of authority or legitimacy (e.g., the CEO, a business associate), and asks the target to wire money to a bank account by the attacker. Based on FBI estimates, in the past several years, attackers have been able to steal over $22B in fraudulent wire transfers. Such attacks have affected a very wide range of individuals and institutions, from the world’s largest and most sophisticated companies (e.g., Google, Facebook), to government and public entities, and even individuals whose house down payment was stolen by an attacker pretending to be their mortgage broker.

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This project is the next phase in ongoing research to document how the MTA and NYPD use public resources to criminalize poverty at the subway turnstile, especially in Black and Brown communities.

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Atherosclerosis, a chronic inflammatory disease of the artery wall, is the underlying cause of human coronary heart diseases. Single-cell genomics have catalyzed the revolution in understanding of cellular heterogeneity and dynamics in atherosclerotic vasculature. The goal of the project is to leverage published and our own single-cell genomic data and perform a meta-analysis. Meta-analysis allows integrated analysis of much larger cell numbers and helps resolve the full spectrum of cellular heterogeneity and dynamics in atherosclerotic vessels and facilitate therapeutic translation. The DSI scholar will: (1) use the latest bioinformatic pipeline to integrate the existing scRNA-seq, CITE-seq, and scATAC-seq datasets; (2) analyze the integrated datasets using R/Bioconductor packages (e.g. Seurat); (3) interpret the data using pathway and network analysis. Some relevant workflows are available through the “Resources” page of our lab website at https://hanruizhang.github.io/zhanglab/.

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Understanding the structure and function of the human gut microbiome is expected to revolutionize healthcare due to its many associations with human disease. A critical step in microbiome analysis involves a clustering stage, where genomic sequences of unknown origin are assigned to latent genomes present in the sample. Current clustering methods rely on mixture-models, yet these fail to correctly model the features of genomic sequences shared across multiple genomes. These sequences are of great importance, often encoding antibiotic resistance genes that drive resistant outbreaks. This project’s goal is to develop a clustering algorithm that will effectively cluster both shared and unique genomic sequences. We have developed two probabilistic models, both based around hierarchical Poisson factorization, that have already produced promising results. The project’s goal will be to refine these models: This will involve robustly evaluating the current models, determining their limitations, and designing new models that improve upon the current. A successful project will enable for the first time, scalable, and comprehensive reconstruction of bacterial genomes. In turn, this will enable a large-scale analysis of antimicrobial resistance in the context of the human gut microbiome. We anticipate a successful project to result in an exciting publication.

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