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.

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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Statistical Learning for High-dimensional Tensor Data

Anru Zhang, PhD
Friday, March 13, 2020 - 10:00 at Hock Plaza, 8th Floor #8065

POSTPONED. To be uopdated when a new date is set. 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...
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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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Precision Medicine: Subgroup Identification in Longitudinal Trajectories

Lei Liu, PhD
Friday, February 28, 2020 - 01:30 at 8065 Hock Plaza

Abstract: In clinical studies, the treatment effect may be heterogeneous among patients. It is of interest to identify subpopulations which benefit most from the treatment, regardless of the treatment's overall performance. In this study we are interested in subgroup identification in longitudinal studies when nonlinear trajectory patterns are present. Under...
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Statistical Methods for Decoding Gene Regulation in Single Cells

Wednesday, February 26, 2020 - 02:00 at Hock Plaza, 8th Floor #8065

Abstract: Single-cell sequencing is rapidly transforming biomedical research. With the ability to measure omics information in individual cells, it provides unprecedented resolution to study heterogeneous biological and clinical samples, enabling scientists to discover and characterize previously unknown biological signals and processes carried by novel or rare cell subpopulations. The new...
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