The Zhou Lab has two projects available that will allow the student to either work on interface design with mostly web-technologies, or to design and train “self-interpreting” deep learning networks.
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We invite undergraduate students interested in machine learning to help us design a deep learning-based shape selector that can support our state-of-the-art computational and experimental studies on cell morphology.
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In this project, you will adapt computer graphics algorithms for use on cancer cells. In particular, you will develop a tool to measure the spatial correlations of signaling distributions defined on the irregular manifold that is the cell surface, or, alternatively, choose to work on another mutually agreed upon application of computer graphics to cell morphology.
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Have you been impressed by the license plate recognition in the CSI (Crime Scene Investigation) TV series? Have you ever wondered how they do it? Similar tasks also exist in biomedical imaging, in which scientists try to enhance image quality or/and improve image resolution, to resolve sub-cellular structures. In this project we will use deep learning to perform image deconvolution.
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Spend your gap year developing state-of-the art microscopy tools to study biological processes such as the behavior of cancer cells in circulation. This project will be part of a group effort and involve systematic analysis of acquisition parameters, hyper-parameter optimization, 3D visualization, programming in Python, and advanced reconstruction algorithms including neuronal networks.
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Apply machine learning techniques combined with eye-tracking technology to decipher visual evaluation on craniofacial reconstructive surgery outcomes.
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Help build the essential infrastructure that will support an AI-based system that could provide timely, quantitative, consistent, and scalable surgical skill analyses, greatly enhancing the quality of surgery as well as the effectiveness and efficiency of training.
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Three projects in the Montillo Lab focus on 3D MRI image segmentation in neurodegenerative disorders, deep learning architectures for decoding 4D brain activation in disorders such as schizophrenia, and measuring brain connectivity with tensor decomposition methods to predict depression treatment response.
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Invest your gap year with the Rajaram Lab using deep learning to generate the next generation of histopathology models with clinical grade reliability or developing a framework optimized for generating reproducible deep-learning pipelines in biomedicine.
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Dive into the “black box” of neural network layers using DeepCAT, an interpretative deep learning algorithm developed by the Li Lab, to determine which cancer or non-cancer TCRs share common biochemical signatures.
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This project aims to develop a 3D computer graphics program that can visualize an entire cell.
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