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recordings, behavioral training, and visual experimentation, while also developing and testing deep neural network models of visual representation. In short: experiments first, models second. Current and
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models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees. Research areas include Representation Learning, Machine learning and Optimization
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between historical methods underpinning modern data science, which were developed for significantly different contexts and applications than current AI-driven business use cases. Postdoctoral Fellows at D^3
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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at the intersection of academia and practice. For more information on D^3, please visit https://d3.harvard.edu/labs . D^3 is looking for candidates with diverse backgrounds and/or new perspectives. There are no
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees