Anusua Trivedi proposes a method to apply a pretrained deep convolution neural network (DCNN) on different types of images datasets—a fluorescein angiographic eye image to predict diabetic retinopathy and a fashion image to predict the clothing type in that image—to improve prediction accuracy. This approach improves prediction accuracy on domain-specific image datasets compared to state-of-the-art machine-learning approaches.
Anusua Trivedi is a data scientist on Microsoft’s advanced data science and strategic initiatives team, where she works on developing advanced predictive analytics and deep learning models. Previously, Anusua was a data scientist at the Texas Advanced Computing Center (TACC), a supercomputer center, where she developed algorithms and methods for the supercomputer to explore, analyze, and visualize clinical and biological big data. Anusua is a frequent speaker at machine learning and big data conferences across the United States, including Supercomputing 2015 (SC15), PyData Seattle 2015, and MLconf Atlanta 2015. Anusua has also held positions with UT Austin and University of Utah.
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