Tags: Colloquium Series

The Statistics Department hosts weekly colloquia on a variety of statistcal subjects, bringing in speakers from around the world.


TN Sriram

Robust Recovery of the Central Subspace for Regression Using the Influence Function of the Renyi Divergence Abstract: A considerable amount of research in the literature has focused on quantifying the effect of extreme observations on classical methods for estimating the Central Subspace (CS) for regression through the study of influence functions and their sample estimates. Alternatively, a method that is inherently robust to data contamination…


Ching-Yu Huang

Got it?! Using Active Learning Techniques to Assess Student Learning Just in Time  Abstract: Explore how and why active learning techniques can enhance student engagement, collaboration, and reflection. In this workshop, we’ll discuss what an active learning course looks like from the instructor’s perspective across various disciplines. You’ll also experience and discuss a collection of active learning techniques- simple, easy to implement…


Tianming Liu

Unlock Brain Architectures to Harness AI and Model Neurologic Diseases Abstract: The human brain is an intricate generative system that segregates, integrates, and executes diverse functions seamlessly. Unlocking and representing the brain’s structural and functional architectures hold fundamental significance for neuroscience, healthcare of brain diseases, and brain-inspired artificial intelligence (AI), particularly generative AI (GenAI). This…


Daniela M. Witten

Agenda: 3:30 - 4:00pm - Arrival 4:00 - 4:05pm - Opening Remarks, Brooks Hall 145 4:05 - 5:00pm - Lecture, Dr. Daniela Witten, University of Washington, Brooks Hall 145  5:00 - 5:30pm - Break 5:30 - 7:00pm - Dinner, Founders Memorial Garden 7:05 - 7:45pm - After Dinner Talk, Dr. Daniela Witten. Brooks Hall 145 Biography: Dr. Daniela Witten is a professor of Statistics and Biostatistics at University of Washington, and the Dorothy Gilford…


Hani Safadi

Validation Criteria for Computationally Intensive Theory Construction Abstract: Computationally intensive theory construction (CITC) combines computational techniques with traditional quantitative and qualitative techniques to identify patterns in data and generate theoretical insights from those patterns. While guidelines exist for methodological approaches in CITC, the open-ended and exploratory nature of the genre presents challenges in terms…


Jin Lu

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…


Joshua Lukemire

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…


Hui Yi

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…


Xiao Song

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…


He Tang

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…
Subscribe to Colloquium Series