Joint UW Biostatistics/Fred Hutch Seminar: Evaluating Conversational AI for Medical Diagnosis and Management
Speaker: Anil Palepu, PhD, Research Scientist, Google Research
Presentation: Evaluating Conversational AI for Medical Diagnosis and Management
Abstract: The medical interview has been termed “the most powerful, sensitive, and versatile instrument available to the physician.” While Large Language Models (LLMs) have achieved expert-level scores on medical board examinations, these static benchmarks fail to capture the essence of clinical practice: the ability to intelligently and compassionately acquire information under conditions of uncertainty. To bridge this gap, we must evaluate AI through frameworks that mirror the complexity of human practice—most notably the Objective Structured Clinical Examination (OSCE), a validated gold standard for assessing clinical competence in medical trainees.
In this talk, I will discuss AMIE (Articulate Medical Intelligence Explorer), a research program from Google dedicated to developing and robustly evaluating AI capabilities for clinical reasoning and dialogue. Moving…
Event interval: Single day event. Online Meeting Link: https://bit.ly/AnilPalepuGoogle. Campus room: Fred Hutch Arnold Building, Behnke Suite (M1-A307). Accessibility Contact: Kevin Tatunay, kst123@fredhutch.org, 206-667-2844. Event Types: Academics.
Wednesday, May 20, 2026, 12:00 PM – 1:00 PM.
Biostatistics Seminar: TBD
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Lectures/Seminars.
Thursday, May 28, 2026, 3:30 PM – 4:30 PM.
Biostatistics Seminar: Parametric and Non-Parametric Representation in Cell Science
Speaker: Matheus Viana, PhD, Associate Director, Image Analysis and Descriptive Modeling, The Cell Science team at Allen Institute
Presentation: Parametric and Non-Parametric Representation in Cell Science
Abstract: Understanding how cells organize themselves and how they transition between states is central to the mission of the Cell Science team at the Allen Institute, and microscopy imaging is our primary window into both questions. How we represent and analyze image data, however, depends strongly on the question being asked. In this seminar I will walk through a set of cell representations our team has developed over the past decade, drawing a deliberate parallel to the parametric / non-parametric distinction familiar to biostatisticians. On the parametric side, I will focus on spherical harmonic expansions of cell and nuclear shape, which yield a compact, ordered, and invertible coordinate system that supports classical downstream inference, hypothesis testing, and structured comparison across…
Event interval: Single day event. Campus location: Hans Rosling Center for Population Health (HRC). Campus room: HRC 135. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Lectures/Seminars.
Thursday, June 4, 2026, 3:30 PM – 4:30 PM.
Causal Inference with Observational Data: Common Designs and Statistical Methods - Summer Institutes Online Short Course
Observational studies are non-interventional empirical investigations of causal effects and are playing an increasingly vital role in healthcare decision making in the era of data science. The study design is particularly important in planning observational studies due to the lack of randomization. Aspects of design include defining the objectives and context under investigation, collecting the right data, and choosing suitable strategies to remove bias from measured and unmeasured confounders. Statistical analysis should also align with the design.
This module covers key concepts and useful methods for designing and analyzing observational studies. The first part of the module will focus on matching and weighting methods for cohort and case-control studies for causal inference. Specific topics include basic tools of matching and weighting, randomization inference, and sensitivity analysis. The second part of the module will focus on methods to address unmeasured confounding via causal exclusion. Specific to…
Event interval: Single day event. Campus room: Online. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops. Special Events. Target Audience: Target audiences for this module are: 1. clinical researchers who need to use observational data to.
Tuesday, July 7, 2026, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu. Design and Analysis of Clinical Trials - Summer Institutes Online Short Course
The design and analysis of a randomized clinical trial involves a series of decisions, including the choice of the primary outcome, sample size, randomization algorithm, interim monitoring plan, and the choice of the primary analysis and estimand of interest. This course will focus on the statistical considerations that inform each of these decisions. Additional topics include addressing multiple comparisons, handling missing data, and whether to consider an adaptive design. We will present a set of simple tools and principles that go a long way towards defining a robust clinical trial design. We will also shed light on some common pitfalls to avoid. Discussions will be driven by examples of trials from a variety of domains including cardiovascular disease, infectious disease (HIV, Ebola, COVID-19), as well as other settings.
We assume enrollees will be familiar with topics taught in introductory statistics (t-tests, regression, confidence intervals, p-values, and a basic understanding of the central limit…
Event interval: Single day event. Campus room: Online. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops. Special Events.
Tuesday, July 7, 2026, 8:30 AM – Thursday, July 9, 2026, 12:30 PM.
For more info visit si.biostat.washington.edu. Introduction to Survival Analysis - Summer Institutes Online Short Course
Time-to-event data are common in biomedical research and present unique challenges for analysis, given many subjects under study will not experience the event of interest. After outlining the basic structure of survival data, we will the cover the key methods for survival analysis including Kaplan Meier survival curves; the log-rank test and alternative testing procedures for group comparisons; and the Cox proportional hazards model. Analytical approaches for survival data with competing risks will also be introduced. Emphasis in this module will be on the practical application of these methods, with illustrative examples from medical and public health research being used throughout. Examples will feature best practices for reporting of results and analysis pitfalls to avoid. As time allows, we will consider concepts and controversies for survival analysis estimands and fundamental issues for study design and power. All examples will be conducted using R.
Event interval: Single day event. Campus room: Online. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Special Events. Workshops.
Thursday, July 9, 2026, 1:00 PM – Friday, July 10, 2026, 4:30 PM.
For more info visit si.biostat.washington.edu. Topics in Clinical Trials: Issues in Non-Inferiority Trials and Addressing Missing Data - Online Short Course
Over the past 7 decades, the randomized clinical trial (RCT) has become the gold standard for evaluation of new drugs, biologics, devices, procedures, and behavioral interventions. In a half-day short course, two critically important topics will be discussed that have broad implications in the design and conduct of clinical trials: the Design of Non-Inferiority Trials and the Prevention of Missing Data.
Event interval: Single day event. Campus room: Online. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops. Special Events.
Friday, July 10, 2026, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu. Data Wrangling with R - Summer Institutes Online Short Course
Participants will learn how to prepare and process data, a key step prior to visualization and statistical analysis. Our approach focuses on the concept of creating “tidy data” e.g. data that is organized into readable and distributable files. In this module, we will: Use hands-on examples covering concepts on data retrieval, cleaning, manipulation, and formatting. , Touch on reproducible research using R Markdown and collaborative code sharing using GitHub.
Some familiarity with R is needed for this module.
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 13, 2026, 8:00 AM – Wednesday, July 15, 2026, 2:30 PM.
For more info visit si.biostat.washington.edu. Generalized Estimating Equations and Mixed-Effects Models for Longitudinal Data Analysis - Summer Institutes Online Short Course
Longitudinal studies follow individuals over time and repeatedly measure health status, which facilitates prospective ascertainment of exposures and incident outcomes, and identification of changes over time within individuals. Analyses of longitudinal data must account for the correlation that arises from collecting repeated measures on the same individuals over time.
This module will introduce statistical methods for the analysis of longitudinal data, with a focus on marginal (or, population-averaged) models fit via generalized estimating equations and conditional (or, subject-specific) models fit via generalized linear mixed-effects models. Relevant theoretical background will be provided. Illustrative examples and interactive activities (conducted in R) will be used to practice analysis approaches, modeling strategies, and interpretation of results.
This course is targeted toward individuals with little or no prior experience with statistical methods for longitudinal data analysis. Experience with using…
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 13, 2026, 8:30 AM – Wednesday, July 15, 2026, 12:00 PM.
For more info visit si.biostat.washington.edu. Multi-state Models for Time-to-Event Data, with Applications to Clinical Research - Summer Online Short Course
Course description coming soon.
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 13, 2026, 8:30 AM – Wednesday, July 15, 2026, 12:00 PM.
For more info visit si.biostat.washington.edu. Design and Analysis of Two-Phase Studies - Summer Institutes Online Short Course
Researchers often need to measure new variables, or validate existing measurements. Doing this for a whole cohort or database can be prohibitively expensive, so techniques are needed for choosing good subsamples and analyzing them efficiently. This module will give both a conceptual and practical introduction to planning and analyzing modern two-phase study designs. We will cover efficient weighted estimation using the whole cohort, and the optimal design and allocation of subsamples. Exercises will focus on practical aspects of implementing methods using the R survey package.
Open configuration options.
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 15, 2026, 1:00 PM – Thursday, July 16, 2026, 4:30 PM.
For more info visit si.biostat.washington.edu. Joint Modeling of Longitudinal and Survival Data - Summer Institutes Online Short Course
Longitudinal studies follow individuals over time and repeatedly measure health status. Analyses of longitudinal data are often complicated by several factors that can threaten the validity of standard analysis methods. First, missing data in longitudinal outcomes can arise when individuals are lost to follow-up, either due to drop-out (e.g. in randomized trails) or death (e.g. in long-term observational studies). Second, when modeling intermittently measured time-dependent covariates in a survival analysis, biological variation can lead to measurement error. Joint modeling of longitudinal and survival outcomes has emerged as a novel approach to handle these issues.
We will detail the use of mixed-effects models for the analysis of repeated longitudinal measures, Cox regression models for the analysis of event-time outcomes with longitudinal measures as time-dependent covariates, and their combination in a joint modeling framework. An in-depth data analysis (conducted in R) will be used to discuss analysis…
Event interval: Single day event. Campus room: Online. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops. Special Events.
Thursday, July 16, 2026, 8:30 AM – Friday, July 17, 2026, 12:00 PM.
For more info visit si.biostat.washington.edu. Data Visualization - Summer Institutes Online Short Course
We will present general-purpose techniques for visualizing a variety of data, as well as specific techniques for visualizing common types of biological data sets. Som strategies for working with large data will be provided. Understanding data involves an iterative cycle of visualization and modeling. We will illustrate this with several examples during the workshop.
The first segment of this module will focus on structured development of graphics using static graphics. This will use the ggplot2 package in R. It enables building plots using grammatically defined elements, and producing templates for use with multiple data sets. We will include some these principles for working with biological and genomic data.
The second segment will focus on interactive graphics for rapid exploration. We will also demonstrate interactive techniques for high-performance local display, and for easily creating interactive web graphics. In addition, we will explain how to create simple web GUIs for managing interactive analysis…
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 20, 2026, 8:00 AM – Wednesday, July 22, 2026, 2:30 PM.
For more info visit si.biostat.washington.edu. Missing Data Methods - Summer Institutes Online Short Course
Although missing data are pervasive in studies across disciplines, the impact of missing data on estimation and inference and the strengths and weaknesses of modern approaches to handling missing data are not widely understood.
This module will review common missing data mechanisms, then introduce a variety of methods for estimation and inference in the presence of missing data, including conventional methods, the EM algorithm, multiple imputation, and semi-parametric methods. Approaches to sensitivity analyses will also be discussed. All methods will be illustrated in R using data from observational studies.
This course is targeted towards individuals with little or no prior experience with modern missing data methods. Experience using regression methods to analyze data (e.g. linear regression, logistic regression) is important background for this module.
Open configuration options.
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 20, 2026, 8:30 AM – Wednesday, July 22, 2026, 12:00 PM.
For more info visit si.biostat.washington.edu. Using Causal Graphs in Epidemiological Research - Summer Institutes Online Short Course
This course will introduce the basic concepts of graphical models focusing on their use in causal inference as applied in epidemiological and biostatistical research. Causal graphs aim to encapsulate the key dependencies believed to be present between variables observed in a multivariable study setting. Such graphs assist the researcher in formulating appropriate statistical methods for estimating key causal quantities of interest, and guide them into the appropriate form of statistical adjustment for confounding. This module will demonstrate the use of causal graphs and the accompanying analyses in different settings. Participants will learn how to construct analysis based on causal graph structures.
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 20, 2026, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu. Modern Statistical Learning for Observational Data - Summer Institutes Online Short Course
While clinical trials provide the highest level of evidence to compare clinical treatments or public health interventions, they are often not feasible due to ethical, logistic or economic constraints. Observational studies provide an opportunity to learn about the effect of interventions for which little or no trial data are available. These studies constitute a potentially rich and relatively cheap source of information. However, in such studies, treatment or intervention allocation may be strongly confounded by other important patient characteristics and much care is needed to disentangle observed relationships and infer causal effects.
In this course, we will provide an overview of modern statistical techniques for analyzing observational data. We will focus primarily on recent advances in the field of targeted learning, which facilitates the use of state-of-the-art machine learning tools to flexibly adjust for confounding while yielding valid statistical inference. In contrast, conventional techniques fo…
Event interval: Single day event. Campus room: Online. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops. Special Events.
Tuesday, July 21, 2026, 8:30 AM – Friday, July 24, 2026, 12:00 PM.
For more info visit si.biostat.washington.edu. Supervised Methods for Statistical Machine Learning - Summer Institutes Online Short Course
n this module, we will present a number of supervised learning techniques for the analysis of Biomedical Big Data. These techniques include penalized approaches for performing regression, classification, and survival analysis with Big Data. Support vector machines, decision trees, and random forests will also be covered.
The main emphasis will be on the analysis of “high-dimensional” data sets from genomics, transcriptomics, metabolomics, proteomics, and other fields. These data are typically characterized by a huge number of molecular measurements (such as genes) and a relatively small number of samples (such as patients). We will also consider electronic health record data sets, which often contain many missing measurements.
Throughout the course, we will focus on common pitfalls in the supervised analysis of Biomedical Big Data and how to avoid them. The techniques discussed will be demonstrated in R.
This course assumes some previous exposure to linear regression and statistical hypothesis testing, as…
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 22, 2026, 11:30 AM – Friday, July 24, 2026, 2:30 PM.
For more info visit si.biostat.washington.edu. Evaluation of Biomarkers and Risk Models - Summer Institutes Online Short Course
This course discusses methodology for evaluating biomarkers and risk prediction models, covering principles, concepts, metrics, and graphical tools.
We will discuss motivations for risk prediction in clinical medicine and public health, clarify the concept of “personal” risk, and consider concepts of risk model calibration and performance. Metrics and graphical tools will include ROC curves and AUC; calibration plots for risk prediction models; and net benefit and decision curves. The module will also discuss methods for comparing risk prediction models and, in particular, assessing the incremental value of a new biomarker when there are already established predictors. We will consider the utility of a biomarker for prognostic enrichment of a clinical trial. Throughout the module, we will highlight some common myths and mistakes to avoid.
There will be an opportunity for hands-on practice in R using packages such as rms, rmda, and BioPET. The software component of this module is small and knowledge of R is…
Event interval: Single day event. Campus room: Online. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Special Events. Workshops.
Thursday, July 23, 2026, 8:30 AM – Friday, July 24, 2026, 12:00 PM.
For more info visit si.biostat.washington.edu. Propensity Scores - Summer Institutes Online Short Course
The propensity score is a key component of many causal inference procedures. After establishing the basic causal inference framework, we will outline the key methods of construction of propensity score functions, and study their core mathematical properties. We will detail the use of the propensity score in matching, inverse weighting and regression adjustments that allow the unconfounded effect of an exposure or treatment of interest to be estimated consistently.
Using the framework of semiparametric inference, we will contrast the statistical properties of estimators derived using each method. We will investigate issues of model selection for the propensity score, and demonstrate the utility of judicious choice of predictors that enter into the propensity function. This will be illustrated in standard problems and also in the case of high-dimensional predictors. Longitudinal data will also be studied in the causal setting.
Finally, we will develop the Bayesian framework for handling causal inference and i…
Event interval: Single day event. Campus room: Online. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops. Special Events.
Thursday, July 23, 2026, 8:30 AM – Friday, July 24, 2026, 12:00 PM.
For more info visit si.biostat.washington.edu. 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. 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. 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: Aloft Boston Seaport District, 401-403 D Street, Boston, MA 02210
Time: 5-7 p.m. EDT (2-4 p.m. PDT)
RSVP required: Link will be posted by the end of June.
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.