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X-WR-CALNAME;VALUE=TEXT:Colloquium Series: Anru Zhang
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SUMMARY:Colloquium Series: Anru Zhang
DESCRIPTION:<p>	Our upcoming event for the Statistics Department Colloquium Series is scheduled for Monday, April 7 from 12:00 – 1:00pm (ET) and will be an in-person presentation Science Center Rm. 316. Lunch will be provided to guests following the talk. This week's speaker will be Anru Zhang of the Biostatistics and Bioinformatics department at Duke University.</p><p>	<strong>Tensor Learning in 2020s: Methodology, Theory, and Applications</strong></p><p>	 </p><p>	The analysis of tensor data, i.e., arrays with multiple directions, has become an active research topic in the era of big data. Datasets in the form of tensors arise from a range of scientific applications. Tensor methods also provide unique perspectives to many high-dimensional problems, where the observations are not necessarily tensors. Problems in high-dimensional tensors generally possess distinct characteristics that pose challenges to the data science community.</p><p>	In this talk, we discuss some recent advances in tensor learning and their applications in genomics and computational imaging. We also illustrate how we develop statistically optimal methods and computationally efficient algorithms that interact with the modern theories of computation, high-dimensional statistics, and non-convex optimization.</p><p>	Anru Zhang is the Eugene Anson Stead, Jr. M.D. Associate Professor and primary faculty member jointly appointed by the Department of Biostatistics &amp; Bioinformatics and the Departments of Computer Science at Duke University. He obtained his bachelor's degree from Peking University in 2010 and his Ph.D. from the University of Pennsylvania in 2015. His work focuses on high-dimensional statistical inference, tensor learning, generative models, and applications in electronic health records and microbiome data analysis. He won the IMS Tweedie Award, the COPSS Emerging Leader Award, and the ASA Gottfried E. Noether Junior Award. His research is currently supported by two NIH R01 Grants (as PI and MPI) and an NSF CAREER Award.</p><p>	 </p>
LOCATION:Science Center 316
STATUS:CONFIRMED
DTSTART:20250407T160000Z
DTEND:20250407T170000Z
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