Tensors are rich structures for modeling complex higher order relationships in data-rich domains such as social networks, computer vision, and the Internet of Things. Tensor decomposition methods are embarrassingly parallel and scalable to enormous datasets. They are guaranteed to converge to the global optimum and yield consistent estimates of parameters for many probabilistic models such as topic models, community models, and hidden Markov models.
I will show the results of these methods for learning topics from text data, communities in social networks, disease hierarchies from healthcare records, cell types from mouse brain data, etc. I will also demonstrate how tensor methods can yield rich discriminative features for classification tasks and serve as an alternative method for training neural networks.
Anima Anandkumar is a principal scientist at Amazon Web Services. Anima is currently on leave from UC Irvine, where she is an associate professor. Her research interests are in the areas of large-scale machine learning, nonconvex optimization, and high-dimensional statistics. In particular, she has been spearheading the development and analysis of tensor algorithms. Previously, she was a postdoctoral researcher at MIT and a visiting researcher at Microsoft Research New England. Anima is the recipient of several awards, including the Alfred. P. Sloan fellowship, the Microsoft faculty fellowship, the Google research award, the ARO and AFOSR Young Investigator awards, the NSF CAREER Award, the Early Career Excellence in Research Award at UCI, the Best Thesis Award from the ACM SIGMETRICS society, the IBM Fran Allen PhD fellowship, and several best paper awards. She has been featured in a number of forums, such as the Quora ML session, Huffington Post, Forbes, and O’Reilly Media. Anima holds a BTech in electrical engineering from IIT Madras and a PhD from Cornell University.
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