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Schedule Interview NowMy name is Rachel D. and I have over 4 years of experience in the tech industry. I specialize in the following technologies: Natural Language Processing, PyTorch, Computer Vision, TensorFlow, Python, etc.. I hold a degree in Doctor of Philosophy (PhD), Doctor of Medicine (MD), Bachelor of Arts (BA). Some of the notable projects I've worked on include: Playing word games with artificial intelligence, Explainable prediction of abnormalities in chest CT scans, Predicting which mutations will cause disease, New method for explaining neural network image classification, Predicting abnormalities in chest CT volumes, etc.. I am based in Durham, United States. I've successfully completed 6 projects while developing at Softaims.
I employ a methodical and structured approach to solution development, prioritizing deep domain understanding before execution. I excel at systems analysis, creating precise technical specifications, and ensuring that the final solution perfectly maps to the complex business logic it is meant to serve.
My tenure at Softaims has reinforced the importance of careful planning and risk mitigation. I am skilled at breaking down massive, ambiguous problems into manageable, iterative development tasks, ensuring consistent progress and predictable delivery schedules.
I strive for clarity and simplicity in both my technical outputs and my communication. I believe that the most powerful solutions are often the simplest ones, and I am committed to finding those elegant answers for our clients.
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4 years
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Duke University
My collaborators and I developed artificial intelligence models to play the word game Codenames. We proposed an algorithm to generate Codenames clues from the language graph BabelNet or from various w
I developed the first explainable model for prediction of multiple abnormalities in chest CT scans. This model, AxialNet, is a convolutional neural network that uses multiple instance learning to tell
I developed an open-source Python framework, GENESIS, that identifies high-performing gene-specific models to predict which mutations will cause disease. This work is detailed in our paper, "GENESIS:
I developed a novel method for neural network explainability called HiResCAM, which highlights the regions in an image that a convolutional neural network used to make a prediction. HiResCAM is a uniq
The goal of this project was progress towards fully automated interpretation of medical images. I developed the largest volumetric medical imaging dataset in the world, RAD-ChestCT, with 36,316 chest
Doctor of Philosophy (PhD) in Computer science
2014-01-01-2021-01-01
Doctor of Medicine (MD) in
2014-01-01-2022-01-01
Bachelor of Arts (BA) in Biological Sciences
2010-01-01-2013-01-01