Seminar Series

The Applied Biostatistics Seminar Series consists of a series of talks with the primary purpose of furthering statistical knowledge on an applied level. Talks will focus on advances in biostatistical methods and statistical programming techniques and their translation into addressing biomedical research questions. The seminars are open to all members of the Duke community, but primarily geared toward applied statistical researchers.

Design and Monitoring of Clinical Trials with Clustered Time-to-Event Endpoint

Wednesday, June 3, 2020 - 02:00 at Zoom Conference

Dissertation Defense Jianghao Li's dissertation defense will be held in Zoom on Wednesday , June 3, 2020, 10:00-12:00pm . Please do join us by Zoom. We appreciate your continued support of our doctoral program. Zoom Link: The link to remotely join the Zoom session of the dissertation defense is: https://zoom.us...
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Multiple Testing Embedded in an Aggregation Tree with Applications to Omics Data

John Pura
Monday, May 25, 2020 - 02:00 at Zoom Conference

Dissertation Defense John Pura's dissertation defense will be held in Zoom on Monday, May 25, 2020, 2:00-4:00pm . Please do join us by Zoom. We appreciate your continued support of our doctoral program. Zoom Link: The link to remotely join the Zoom session of the dissertation defense is: https://zoom.us ...
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Statistical Learning for High-dimensional Tensor Data

Anru Zhang, PhD
Wednesday, April 1, 2020 - 10:00 at On Line

Abstract: The analysis of tensor data has become an active research topic in this area of big data. Datasets in the form of tensors, or high-order matrices, arise from a wide range of applications, such as genomics, material science, and hyperspectral imaging, neuroimaging. In addition, tensor methods provide unique perspectives...
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Imputation and Causal Inference in Genomics

Audrey Qiuyan Fu, PhD
Friday, March 20, 2020 - 10:00 at MSRB III 1125

POSTPONED. To be updated when a new date is set. Abstract: Genomic data can be complex, large, noisy and sparse. Here I will discuss two problems we have worked on. The first problem deals with the highly sparse data from experiments of measuring gene expression in single cells. These data...
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Gromov-Wasserstein Learning: A New Machine Learning Framework for Structured Data Analysis

Monday, March 2, 2020 - 12:00 at LSRC D106

Abstract: Many biomedical data types like protein-protein interaction (PPI) networks and biological molecules are structured data, which are represented as graphs optionally accompanied with node attributes. From the viewpoint of machine learning, tasks focusing on these structured data, such as network alignment and molecule analysis, can often be formulated as...
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