Department of Statistics Weekly Seminar Series
Hosted by Dixon Vimalajeewa
Seminars will take place every Wednesday from 3pm-4pm in Hardin Hall 49 unless otherwise specified. You will find HARH 49 located in the basement of the north tower of Hardin Hall. The seminars are open to all UNL Students, Staff, and Faculty. If you would like to attend, but are not a part of our Statistics department, please fill out the information on the Webform below and you will receive a Zoom link via email.
Date: Wednesday, September 13.
Speaker: Dr. Tianjing Zhao, Department of Animal Science, University of Nebraska Lincoln.
Title: Solving emerging challenges in statistical genetics and genomics: new data, large data, and sharing data.
With the development of high-throughput sequencing, whole-genome analysis, such as genomic prediction and genome-wide association studies (GWAS), plays an important role in animal, plant, and human studies. As the amount and diversity of omics data continue to grow, several challenges arise for the linear mixed model. First, there is a need to extend mixed models to incorporate multiple sequential layers of data as one connected network (e.g., the regulatory cascades). Second, due to increasing concerns about data privacy, there is a need to adopt mixed models for encrypted data, enabling the sharing of confidential data in genome-to-phenome analyses. Also, there is a need to address computational costs for large data analysis. We proposed new methods to solve these challenges.
Date: Wednesday, August 30.
Speaker: Dr. Pavel N. Krivitsky, senior lecturer in Mathematics and Statistics, University of New South Wales.
Title: A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks.
Two large, heterogeneous samples of small networks of within-household contacts in Belgium were collected using two different but complementary sampling designs: one smaller but with all contacts in each household observed, the other larger and more representative but recording contacts of only one person per household. We wish to combine their strengths to learn the social forces that shape household contact formation and facilitate simulation for prediction of disease spread, while generalising to the population of households in the region.