Special year - math workshop

Sep 11 2020

Low Algebraic Dimension Matrix Completion

Speaker: Laura Balzano
2:00pm | Virtual
Low rank matrix completion (LRMC) has received tremendous attention in recent years.  The low rank assumption means that the columns (or rows) of the matrix to be completed are points on a low-dimensional linear variety.  This work extends this thinking to cases where the...
Sep 11 2020

Low-rank matrix recovery from quantized or count observations

Speaker: Mark Davenport
1:30pm | Virtual
Abstract: Low-rank matrices play a fundamental role in modeling and computational methods for signal processing and machine learning. In many applications where low-rank matrices arise, these matrices cannot be fully sampled or directly observed, and one encounters...
Sep 11 2020

Metric and manifold repair for missing data

Speaker: Anna Gilbert
12:00pm | Virtual
Abstract: For many machine learning tasks, the input data lie on a low-dimensional manifold embedded in a high-dimensional space and, because of this high-dimensional structure, most algorithms inefficient. The typical solution is to reduce the dimension of the...
Sep 10 2020

High-dimensional omics data analysis with missing values

Speaker: Anru Zhang
2:45pm | Virtual
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 challenges. In this talk, we focus on two...
Sep 10 2020

Gene expression recovery in single cell transcriptomic data

Speaker: Nancy Zhang
1:30pm | Virtual
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 roles of individual...
Sep 10 2020

Statistical challenges with single cell RNA-Seq technologies

Speaker: Rafael Irizarry
12:00pm | Virtual
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.
Sep 09 2020

Regularization and spurious correlations in sparse single-cell transcriptomes

Speaker: Mickey Atwal
2:45pm | Virtual
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 development of...
Sep 09 2020

Co-manifold learning with missing data

Speaker: Eric Chi
1:55pm | Virtual
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 structure to...
Sep 09 2020

Causal inference with binary outcomes subject to both missingness and misclassification

Speaker: Grace Yi
1:30pm | Virtual
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 talk, I will discuss a...

Pages