Unsupervised Methods for Statistical Machine Learning - Summer Institutes Online Short Course
In this module, we will present a number of unsupervised learning techniques for finding patterns and associations in Biomedical Big Data. These include dimension reduction techniques such as principal components analysis and non-negative matrix factorization, clustering analysis, and network analysis with graphical models.
We will also discuss large-scale inference issues, such as multiple testing, that arise when mining for associations in Biomedical Big Data. As in Module 4 on supervised learning, the main emphasis will be on the analysis of real high-dimensional data sets from various scientific fields, including genomics and biomedical imaging. The techniques discussed will be demonstrated in R.
This course assumes some previous exposure to linear regression and statistical hypothesis testing, as well as some familiarity with R or another programming language.
Event interval: Single day event. Campus room: Online. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops. Special Events.
Monday, July 27, 2026, 8:00 AM – Wednesday, July 29, 2026, 2:30 PM.
For more info visit si.biostat.washington.edu. Final Exam - Albert Osom
Committee: Ali Shojaie (chair), Aaron Hudson, Ying-Qi Zhao, Ken Rice, Noemi Kreif (GSR)
Presentation: Statistical Methods for Biomarker Discovery and Inference on Causal Functionals
Abstract: Precision medicine seeks to improve patient outcomes by tailoring disease prevention, diagnosis, and treatment to the characteristics of individual patients rather than relying on population-wide recommendations. The increasing availability of large observational studies has created new opportunities to develop individualized clinical decision strategies and to understand how treatment responses vary across patients. This dissertation develops statistical methodology for individualized decision-making under clinically motivated utility constraints and for hypothesis testing of function-valued parameters, with applications to causal inference.
The first project introduces a clinically motivated objective for cancer screening and develops an optimal biomarker-based decision rule that balances statistical optimality…
Event interval: Single day event. Campus location: Hans Rosling Center for Population Health (HRC). Online Meeting Link: https://washington.zoom.us/j/93058175151?pwd=TWELLT1nlFBAPHfs6zdYx6onapx52e.1. Campus room: HRC 370. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Academics.
Monday, July 27, 2026, 11:30 AM – 1:30 PM.
Improving Precision and Power in Randomized Trials by Leveraging Baseline Variables - Summer Institutes Online Short Course
In randomized clinical trials with baseline variables that are correlated with the outcome, there is potential to improve precision and reduce the required sample size by appropriately adjusting for these variables in the statistical analysis (called covariate adjustment). The resulting sample size reductions can lead to substantial cost savings, and also can lead to more ethical trials since they avoid exposing more participants than necessary to experimental treatments. Despite regulators such as the U.S. Food and Drug Administration and the European Medicines Agency recommending covariate adjustment, it remains underutilized leading to inefficient trials in many disease areas. This is especially true for trials with binary, ordinal, and time-to-event outcomes, which are quite common.
This module provides a comprehensive overview of covariate adjustment—explaining what it is, how it works, when it is beneficial, and how to implement it in a preplanned, model-robust manner across various scenarios. Using re…
Event interval: Single day event. Campus room: Online. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Special Events. Workshops.
Wednesday, July 29, 2026, 8:30 AM – Thursday, July 30, 2026, 12:00 PM.
For more info visit si.biostat.washington.edu. Deep Learning and Artificial Intelligence - Summer Institutes Online Short Course
This short course will provide an overview of the statistical underpinnings of Deep Learning (DL) and Artificial Intelligence (AI). The course will trace the evolution of AI models, beginning with Dense Neural Networks before progressing through Convolutional (CNN) and Recurrent (RNN) frameworks to modern Transformers, Diffusion models, and AI agents. Beyond model architecture, we will also explore the relationship between AI and statistics: how AI can advance statistical analyses and research, and conversely how statistics can advance AI.
Event interval: Single day event. Campus room: Online. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops. Special Events.
Wednesday, July 29, 2026, 11:30 AM – Friday, July 31, 2026, 2:30 PM.
For more info visit si.biostat.washington.edu. Final Exam - Ellen Graham
Committee: Andrea Rotnitzky (Chair), Marco Carone, Alex Luedtke, Yanqin Fan (GSR)
Presentation: Semiparametric Inference with Incomplete Data: Data Fusion, Instrumental Variables, and Proximal Causal Models
Abstract: We consider the possibility of debiased inference for parameters broadly linked by their reliance on incomplete data. Our first project focuses on fused data, the integration of data from many different sources, each of which may provide incomplete information about the target distribution of interest. We address the goal of conducting inference about a smooth finite-dimensional parameter by utilizing individual-level data from various independent sources. Recent advancements have led to the development of a comprehensive theory capable of handling scenarios where different data sources align with, possibly distinct subsets of, conditional distributions of a single factorization of the joint target distribution. While this theory proves effective in many significant contexts, it falls short in…
Event interval: Single day event. Campus location: Hans Rosling Center for Population Health (HRC). Online Meeting Link: https://washington.zoom.us/j/98810874266?pwd=ePvHFgXdbGAy5r4CMVjfBuoSdPQ8Da.1. Campus room: HRC 370. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Academics.
Friday, July 31, 2026, 12:00 PM – 2:00 PM.
Joint UW Biostatistics/Statistics JSM Reception
Event: Reception for UW Biostatistics and Statistics department alumni, students, and faculty held in conjunction with 2026 Joint Statistical Meetings
Location: Mahoney Exchange Room, Aloft Boston Seaport District, Boston, MA
Time: 5-7 p.m. EDT (2-4 p.m. PDT)
RSVP requested: https://bit.ly/2026-UW-STAT-BIOST-JSM, Questions: Please reach out to STAT organizer, Kristine Chan, at kyunchan@uw.edu.
Event interval: Single day event. Accessibility Contact: Kristine Chan, kyunchan@uw.edu. Event Types: Special Events. Target Audience: UW Biostatistics and Statistics department alumni, students, and faculty.
Monday, August 3, 2026, 5:00 PM – 7:00 PM.