Previous Conferences & Workshops

Abstract: We have seen the rise of high-dimensional omics data, e.g., genome, transcriptome, microbiome, and proteome in recent decades. The different types of missingness in modern omics data bring up significant biological and statistical...

Abstract: Cells are the basic biological units of multicellular organisms. The development of single-cell technologies such as single cell RNA sequencing (scRNA-seq) have enabled us to study the diversity of cell types in tissue and to elucidate the...

Abstract: I will give a brief introduction to the technology followed by some exploratory data analysis demonstrating the statistical challenges and how some of these can be considered missing data problems.

This talk will form part of a series of three talks focusing on Broué’s Abelian Defect Group Conjecture, which concerns the modular representation theory of finite groups. We will pay particular attention here to the ‘geometric’ form of the...

Abstract: Recent advances in biotechnology and genomics have generated dizzying amounts of large, noisy, and sparse datasets that require concomitant development of machine learning methods. The analyses of single-cell RNA-seq data have driven the...

Abstract: Representation learning is typically applied to only one mode of a data matrix, either its rows or columns. Yet in many applications, there is an underlying geometry to both the rows and the columns. We propose utilizing this coupled...

Abstract: Causal inference has been widely conducted in various fields and many methods have been proposed for different settings. However, for noisy data with both mismeasurements and missing observations, those methods often break down. In this...

Abstract: In many applications, it is of interest to cluster subjects based on very high-dimensional data, often in the presence of missing data. Although discrete mixture models are routinely used, we demonstrate pitfalls in high-dimensional...

Abstract: In today’s data-driven society, human beings still make most critical decisions and yet they are increasingly utilizing recommendations produced by statistical and machine learning methods. Given the prevalence of this approach in many...