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.

Dissertation Defense

Zidi Xiu, PhD Candidate
Friday, July 16, 2021 - 09:00 at Zoom Conference

Hybrid Dissertation Presentation: "Improving Clinical Prediction Models with Statistical Representation Learning" by Zidi Xiu, PhD Candidate This dissertation defense will be held in person and on Zoom Friday, July 16 2021, 9:00am. In Person - **Advance Permission Required. Please contact Ladonna Huseman at if you wish to attend in...
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Examining the Spatial and Spatio-temporal Heterogeneity in the COVID-19 Pandemic: Who you are, and where you live matters?

Loni Philip Tabb, PhD
Friday, April 30, 2021 - 12:00 at Zoom Conference

Abstract: The COVID-19 pandemic has impacted millions of lives both here in the United States and as well as globally. As of March 17, 2021, there were a total of 29,319,457 related cases across the US, with 533,057 associated deaths. With the first vaccine being distributed in the US in...
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PDA: privacy-preserving distributed algorithms and statistical inference

Yong Chen, PhD
Monday, April 26, 2021 - 12:15 at Zoom Conference

Abstract: With the increasing availability of electronic health records (EHR) data, it is important to effectively integrate evidence from multiple data sources to enable reproducible scientific discovery. However, we are still facing practical challenges in data integration, such as protection of data privacy, the high dimensionality of features, and heterogeneity...
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Integrative Analysis of Omics Data to Construct Whole-Genome Gene Regulatory Networks

Min Zhang, PhD
Friday, April 2, 2021 - 12:00 at Zoom Conference

Abstract: Constructing gene regulatory networks is crucial to understanding the molecular interactions underlying complex diseases. By integrating transcriptomic and genomic data, we propose a parallel algorithm, i.e., the two-stage penalized least squares method (2SPLS), to infer the causal relationships between all genes in an organism, via a model-based framework. With...
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Functional Data Analysis: Novel Statistical Methods and Applications in Alzheimer’s Disease Research

Sheng Luo, PhD
Thursday, April 1, 2021 - 12:00 at Zoom Conference

Abstract: Alzheimer's disease (AD) is a progressive neurodegenerative disease that causes impairment in multiple domains (e.g., cognition and behavior) and progresses heterogeneously in time and across domains and individuals. AD studies collect data from multiple sources: longitudinal clinical data (e.g., neuropsychological, functional, and behavioral assessments), neuroimaging (e.g., MRI, and PET),...
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