Tags: Colloquium Series

About the Speaker: Dr. Daniela Witten is a professor of Statistics and Biostatistics at University of Washington, and the Dorothy Gilford Endowed Chair in Mathematical Statistics. She develops statistical machine learning methods for high-dimensional data, with a focus on unsupervised learning. She has received a number of awards for her research in statistical machine learning: most notably the Spiegelman Award from the American Public Health…

Firm-Sponsored Online Communities: Building Alignment Capabilities for Participatory Governance  Abstract: Firm-sponsored online communities face governance challenges due to differences in organizing logics, goals, and values between the community and the sponsor. This study focuses on better understanding the successful governance of firm-sponsored online communities. Through an inductive case study of Mayo Clinic Connect, an online…

Provable Algorithms for Machine Learning in the Wild: Mobilizing, Hierarchizing, and Adaptive Morphing Abstract: Amidst increasing data volumes, addressing large-scale machine learning challenges in environments characterized by inherent variability is crucial. Such variability impacts data collection, format, quality, computational capacity, and connectivity within cyber-physical systems, thereby shaping the development of resilient machine…

A general framework for brain network extraction from fMRI data with repeated measurements Abstract: We introduce a general framework for decomposing brain function into functional brain networks for multi-subject data with repeated measurements and covariate effects. This general method provides a much-needed tool for investigating brain networks and their differences in imaging studies with complex study designs including longitudinal and/or…

How can emerging statistical methodologies improve the global response to human trafficking? Abstract: Having accurate prevalence data is critical for enabling informed decisions about how to allocate scarce resources to strengthen human trafficking (HT) response. Traditional prevalence estimation strategies, namely those utilizing probability sampling designs that are based on combinations of stratified and multistage sampling, have long been…

Evaluating biomarkers for treatment selection from reproducibility studies Abstract We consider evaluating new or more accurately measured predictive biomarkers for treatment selection based on a previous clinical trial involving standard biomarkers. Instead of rerunning the clinical trial with the new biomarkers, we propose a more efficient approach which requires only either conducting a reproducibility study in which the new biomarkers and…

On estimation and order selection for multivariate extremes via clustering Abstract: We investigate the estimation of multivariate extreme models with a discrete spectral measure using clustering techniques. The primary innovation involves devising a method for selecting the appropriate order that not only consistently identifies the true order in theory but also has a straightforward and easy implementation in practice. Specifically, we…

Confidence ellipsoids of a multivariate normal mean vector based on noise perturbed and synthetic data with applications Abstract: We discuss at length the problem of constructing a confidence ellipsoid of a multivariate normal mean vector based on a random sample from it. The central issue at hand is the sensitivity of the original micro data and hence the data cannot be directly used/analyzed. We consider a few perturbations of the original…

The Impact Of Treatment Discontinuation Due Adverse Events On Efficacy In An Oncology Clinical Trial Abstract Randomized clinical trials persist as the gold standard for evaluating the efficacy and safety of new treatments. However, in clinical trials of reasonable size and duration, it is common for some patients to deviate from their assigned study treatment due to various reasons. One such deviation is treatment discontinuation resulting from…

Fully Functional Neural Networks for Functional Regression Abstract We consider evaluating new or more accurately measured predictive biomarkers for treatment selection based on a previous clinical trial involving standard biomarkers. Instead of rerunning the clinical trial with the new biomarkers, we propose a more efficient approach which requires only either conducting a reproducibility study in which the new biomarkers and standard…