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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Project: analyze behavior of Siamese fighting fish (Betta splendens) as part of a collaboration between the Bendesky and Cunningham labs of the Zuckerman Institute (NeuroTheory Center)

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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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Many of the cryptocurrency transactions have involved fraudulent activities including ponzi schemes, ransomware as well money-laundering. The objective is to use Graph Machine Learning methods to identify the miscreants on Bitcoin and Etherium Networks. There are many challenges including the amount of data in 100s of Gigabytes, creation and scalability of algorithms.

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The function for much of the 3 billion letters in the human genome remain to be understood. Advances in DNA sequencing technology have generated enormous amount of data, yet we don’t have the tool to extract rules of how the genome works. Deep learning holds great potential in decoding the genome, in particular due to the digital nature of DNA sequences and the ability to handle large data sets. However, like many other applications, the interpretability of deep learning models hampers its ability to help understand the genome. We are developing deep learning architectures embedded with the principles of gene regulation and we will be leveraging millions of existing whole genome measurements of gene activity to learn a mechanistic model of gene regulation in human cells.

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The microbiome comprises a heterogeneous mix of bacterial strains, many with strong association to human diseases. Recent work has shown that even the same bacteria could have differences in their genomes across multiple individuals. Such differences, termed structural variations, are strongly associated with host disease risk factors [1]. However, methods for their systematic extraction and profiling are currently lacking. This project aims to make cross-sample analysis of structural variants from hundreds of individual microbiomes feasible by efficient representation of metagenomic data. The colored De-Bruijn graph (cDBG) data structure is a natural choice for this representation [2]. However, current cDBG implementations are either fast at the cost of a large space, or highly space efficient but either slow or lacking valuable practical features.

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