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Mentor: Lee Cooper ( lee dot cooper at emory dot edu )
Overview: Enabling users to interact with large datasets via machine learning algorithms requires fast algorithm response times. This project will develop GPU pipelines for an active machine learning setting, where classification algorithms sift through millions of samples to select key examples for labeling by human experts. Increasing the speed of these pipelines will improve the user experience and the development of machine learning classifiers for cancer research. This pipeline will interact with a database containing millions of samples and their feature descriptions, and will feed results into a web framework in order to collect user feedback.
Programming Languages/Frameworks: C/C++, CUDA/OpenCL
Prerequisites: C++ programming skills, experience in CUDA/OpenCL , basic experience with machine learning algorithms
Level of Expertise: Intermediate.
Integrating Deep Convolutional Network Features with an Interactive Image Machine Learning System
Mentor: Lee Cooper ( lee dot cooper at emory dot edu )
Overview: Our team is developing an open-sourced active machine learning system to enable medical professionals and scientists to interactively build image classifiers for medical imaging datasets containing millions of samples. One of the most promising emerging areas of machine learning is deep learning, where neural networks are used to learn features for image classification. Our team has been successful in developing deep learning prototypes for our active learning system using the Python library Theano, and we are ready to take the next steps to fully integrate these capabilities. In this project you will be responsible for integrating our deep learning prototypes with the active learning system to create a framework for feature generation and storage. You will work with a team of machine learning engineers and biomedical researchers to test and validate this framework.
Programming Languages/Frameworks: Python, C/C++
Prerequisites: Python and C++ programming skills, basic experience with machine learning algorithms, basic experience with parallel computing
Level of Expertise: Intermediate.
Spatial Extensions to MongoDB
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