A translational medical informatic project is available to identify risk factors associated with head and neck cancer and lung cancer in electronic medical records. Projects include data extraction, data curation, and establishing and maintaining a database of biospecimens and patients' characteristics. Statistical analysis and modeling will be done to identify clinical characteristics and risk factors which are associated with aggressive form of tumors. Training and mentorship will be provided. Prospective candidates should have great communication skills, willingness to work in a highly collaborative environment, and have excellent time management and organizational skills.

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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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We are conducting a large-scale study analyzing brain tissues from mice and humans with different APOE genotypes, using both single-nucleus sequencing and spatial transcriptomics to assess RNA expression differences caused by APOE genotype. We are working with an expert bioinformatics core, but would like a data science student to help perform the analyses and act as an in-lab lead for the bioinformatics analysis. Prior experience analyzing RNA-sequencing data is preferred, but not required.

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The main goal of this work is to assess if storms have increased in frequency over Antarctica. It is theorized that climate change will increase the intensity of the winds and frequency of the storms. With ICESat 2 satellite laser altimetry, we can count the number of storms and blowing snow events. ICESat 2 is a photon counting laser and generates terrabytes of data each day. Innovative data science techniques are needed to handle the data and analyze it. This project is, therefore, a suitable topic for a masters student that combines an important problem in Geophysics and climate science with a great Data Science application.

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Until today there is no comprehensive theory for formation of tropical cyclones (hurricanes, typhoons). Therefore, it is common to use statistical methods to derive empirical indices as proxies for the probability for genesis. There are also different types of genesis pathways that have been explored in ad-hoc manner. I would like to explore the possibility of using machine learning to explore tropical cyclone genesis, in particular the different pathways in a more comprehensive manner.

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The proposed project would focus on analyzing quantitative data from a 4 year NIMH-funded study entitled “Integrating evidence-based depression treatment in primary care: Tuberculosis (TB) in Brazil as a model” (PI: Sweetland, K01MH104514). The aim of the study was to assess whether social network analysis could be used to leverage the receptivity and connectivity of TB providers in a Brazilian public health system in a way that could accelerate the adoption (implementation) and diffusion (dissemination) of an evidence-based treatment for depression treatment in a primary care network. Baseline receptivity was operationalized via six brief quantitative scales to measure mental health literacy, work self-efficacy, organizational climate, attitudes towards evidence-based practices, organizational readiness to change and individual innovation thresholds. Connectivity was assessed by asking TB providers with whom they discuss difficult cases, give advice to, or receive advice regarding difficult TB cases. Baseline receptivity and connectivity data was used to identify 3 pilot sites in which to train primary care providers to deliver evidence-based depression treatment for one year.

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