DEP uses near real-time water quality data to guide its operations (i.e., the selection and routing of water) to achieve optimum quality for consumers. Historical data is used to evaluate the effectiveness of watershed protection programs, and model predictions of future water quality are used to understand potential impacts to the water supply under different infrastructure and climate scenarios.
Memory is a basic function of our brain that enables us to use the past experiences to service the present and future on a daily base, and memory function is often disrupted in neurological and psychiatric diseases, such as Alzheimer’s disease and posttraumatic stress disorder. To understand the molecular mechanism of memory storage, we will focus on DNA methylation, a chemical modification of our genome, that is hypothesized to play a critical role for memory. We have identified thousands of DNA methylation changes at numerous genomic loci occurred during the formation of fear and reward memory in the mouse brain. We will develop new computational tools to analyze these changes of DNA methylation and search for the common sequence features of these genomic loci. The result of this project will lead to a systematic understanding of the principle on the function and regulation of DNA methylation in memory, and will pave the way to develop new therapeutic strategies for diseases involved memory defects.
Complex microbial communities play an important role in numerous fields, from human health to bioremediation. One critical challenge in their data analysis is to separate true biological data from contamination of various sources. While contemporary experimental procedures include various negative controls, a comprehensive statistical approach for their analysis has not been developed. Such a framework would have a far-reaching impact on the field.
Complex microbiomes play an important role in numerous fields. One critical challenge in their data analysis is to separate true biological data from contamination. Contemporary experimental procedures include negative controls from various sources, but their analysis is complicated by “well-to-well” contamination: contamination that associates with the position of samples during experimental procedures. This causes bacteria sampled from a true biological source to appear in nearby control samples, and vice versa. An analytic approach that accounts for this source of contamination would have a far-reaching impact on the field.
Our lab is interested in aneuploidy, or the incorrect number of whole chromosomes and chromosome arms. A challenge in this area of research is that karyotypes require a large number of proliferating cells for analysis. To address this, our lab and collaborators developed new algorithms to identify aneuploidy alterations from DNA sequencing data. Here, the project goal is to implement these algorithms at Columbia, and subsequently to apply these analysis methods to samples generated in the lab and patient samples. Building on this, the DSI student may also develop new algorithms for use with single-cell sequencing data and RNA sequencing data. Experience in one or more of the following is a must: UNIX, R, and python. The DSI student will be mentored by Dr. Alison Taylor, and he/she will also work closely with all lab members.
We are developing machine learning (ML) methods to understand how people influence each others’ behavior in social networks. For example, on Twitter, do users influence the content shared or posted by their followers? Methods that can identify such patterns of influence will play a role in studying, e.g., the spread of misinformation on social media sites.
Understanding the interaction between human-associated microbial communities and human health is expected to revolutionize healthcare. Recent work found that this interaction is, in part, shaped by genetic differences between otherwise identical species in the microbiome. Detecting this variation, however, is a significant challenge. This project aims to profile microbial genetic variation within and across multiple patients' microbiomes. This will allow us to better compare and interpret this variation in the context of human disease, gaining mechanistic insight into complex human-microbiome interactions.
