This project works with a novel corpus of text-based school data to develop a multi-dimensional measure of the degree to which American colleges and universities offer a liberal arts education. We seek a data scientist for various tasks on a project that uses analysis of multiple text corpora to better understand the liberal arts. This is an ongoing three-year project with opportunities for future collaborations, academic publications, and developing and improving existing data science and machine learning skills. Tasks likely include: (1) Using Amazon Web Services to create and maintain cloud-based storage (SQL, S3 buckets) of the project’s expanding library of data. (2) Extracting information (named entities, times, places, books, and so on) from millions of plain-text syllabus records. (3) Merging multiple forms of data into a single dataset. (4) Scraping websites for relevant information (e.g., college course offerings, school rankings). Some pages may include dynamically created content that requires the use of a program such as Selenium.

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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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Atherosclerosis—a chronic inflammatory disease of the artery wall—is the underlying cause of human coronary heart diseases. Cells within atherosclerotic lesions are heterogeneous and dynamic. Their pathological features have been characterized by histology and flow cytometry and more recently, by bulk-tissue omics profiling. Despite this progress, our knowledge of cell types and their roles in atherogenesis remains incomplete because of masking of differences across cells when using genomic measurement at bulk level. Single-cell RNA sequencing (scRNA-seq) has catalyzed a revolution in understanding of cellular heterogeneity in organ systems and diseases. This project applies scRNA-seq to define the genetic influences on cell subpopulations and functions in atherosclerotic lesion of transgenic mice for candidate risk genes of human coronary heart diseases as inspired by human genomic discoveries. The students involved in this project are expected to work on: (1) analysis of scRNA-seq data using R/Bioconductor packages; (2) Interpretation of 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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A major obstacle to the decarbonization of the electricity production systems is the multi scale (space and time) variability of wind, solar and hydro energy sources. Much work is being done to understand the high frequency variations in these sources from the perspective of grid integration. However, as with rainfall and other natural systems, these variables can exhibit log-log fractal scaling in space and time, such that the variance of the process increases with temporal duration and with spatial scale. Focusing on high frequency variations thus grossly understates the systemic risk that is associated with these sources. Appropriate national grid design including electricity storage allocation, needs to consider both the periodic annual cycle variations and quasi-periodic inter-annual variability which have larger variance, and the phase lags in these variations across space. The proposed project would explore the development of a multi-level, hierarchical spatio-temporal model for wind or solar using data from the continental USA and its subregions to explore stochastic simulations and multi-scale predictions of the associated risk to inform system design and financial instruments development.

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Autonomous driving is developing rapidly. A lot of breakthroughs of autonomous driving have emerged in both academy and industry. However, many traffic accidents related to autonomous driving also occur and cause people’s concern on the safety issue of AV. To ensure safety and reliability, rigorous test and simulation is required before AV can really drive on road. For AV test and simulation, realistic data is an essential component. Comprehensive, multi-regime and sufficient self-driving data would definitely help the AV development.

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Injury, such as falls, motor vehicle crashes, and drug overdose, is a major source of morbidity and mortality. The interaction between injury and disease is complex and mutually causative. For instance, patients with Alzheimer’s Disease or Parkinson’s Disease are known to be at heightened risk of hip fracture from falls and in turn injurious falls among these patients can drastically alter the trajectory of the disease. So far, research on injury-disease interaction has been scant and fragmented. The proposed project is aimed at uncovering the gestalt of the relations between different injuries and different diseases through a data science approach.

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