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Emergent deep neural network architectures for biomedical and clinical datasets for improving human health (Remote UROP)
MAS: Media Arts and Sciences
Sam Ghosal: email@example.com
Co-authorship on publications and opportunity to apply deep learning to develop deployable solutions . Deidentified numerical measurements, images, and videos from clinical datasets from human subjects are available to develop novel deep learning tools. Your will be trained to create visualizations that allow us to understand the data along its multiple dimensions and to identify areas for deep learning analyses. This includes creating a landing page for these visualizations and this project in general. You will also implement novel deep learning algorithms to classify this and other publicly available datasets for real-world use. Preferred technical skills: Loading and processing large data from files in text format (e.g., csv, json, xml, etc.) using programming tools (e.g., Python Pandas, R, etc.). Computational graph and auto-differentiation tools (e.g., Pytorch, Tensorflow, Theano, etc.) for deep learning models. Visualization tools (e.g. Matplotlib, Tableau, Jupyter Notebook, etc.). Parallel processing tools (e.g. Python Multiprocessing, MPI, etc.). Basic statistical tools (e.g. linear regression, null hypothesis tastings, model comparison, etc.).
Prerequisites: Familiarity and interest in working with data visualization, web programming for clinical deep learning methods. Knowledge/coursework in statistics and EECS is preferred. Include a brief cover letter, resume, a list of related coursework, and other relevant material (projects, portfolio, etc.).