# Tyler Mccormick (he/him)

### Associate Professor, University of Washington

 Email tylermc@uw.edu Phone +1 206 221-6981 UW Box Number 354320 Homepage Personal Home Page ORCID iD 0000-0002-6490-1129

Tyler's work develops statistical models for inference and prediction in scientific settings where data are sparsely observed or measured with error. His recent projects include estimating features of social networks (e.g. the degree of clustering or how central an individual is) using data from standard surveys, inferring a likely cause of death (when deaths happen outside of hospitals) using reports from surviving caretakers, and quantifying & communicating uncertainty in predictive models for global health policymakers. He holds a PhD in Statistics (with distinction) from Columbia University and is the recipient of the NIH Director's New Innovator Award, NIH Career Development (K01) Award, Army Research Office Young Investigator Program Award, and a Google Faculty Research Award. Currently, he is an Associate Professor of Statistics and Sociology at the University of Washington, where he is also a core faculty member in the Center for Statistics and the Social Sciences and a Senior Data Science Fellow in the eScience Institute. During the 2019-2020 academic year Tyler was on leave as a Visiting Faculty Researcher at Google People+AI Research (PAIR).  Tyler currently serves as the Editor for the Journal of Computational and Graphical Statistics (JCGS).

Preprints

Consistently estimating network statistics using Aggregated Relational Data
Emily Breza, Arun G. Chandrasekhar, Shane Lubold, Tyler H. McCormick, Mengjie Pan
Aggregated Relational Data, known as ARD, capture information about a social network by asking a respondent questions of the form "How many people with…

Estimating spillovers using imprecisely measured networks
Morgan Hardy, Rachel M. Heath, Wesley Lee, Tyler H. McCormick
In many experimental contexts, whether and how network interactions impact the outcome of interest for both treated and untreated individuals are key concerns…

Consistently estimating graph statistics using Aggregated Relational Data
Emily Breza, Arun G. Chandrasekhar, Shane Lubold, Tyler H. McCormick, Mengjie Pan
Aggregated Relational Data, known as ARD, capture information about a social network by asking a respondent questions of the form "How many people with…

Identifying the latent space geometry of network models through analysis of curvature
Shane Lubold, Arun G. Chandrasekhar, Tyler H. McCormick
Statistically modeling networks, across numerous disciplines and contexts, is fundamentally challenging because of (often high-order) dependence between…

The "given data" paradigm undermines both cultures
Tyler McCormick
Breiman organizes "Statistical modeling: The two cultures" around a simple visual. Data, to the far right, are compelled into a "black box" with an arrow and…

Inference for Network Regression Models with Community Structure
Mengjie Pan, Tyler H. McCormick, Bailey K. Fosdick
Network regression models, where the outcome comprises the valued edge in a network and the predictors are actor or dyad-level covariates, are used extensively…

Spectral goodness-of-fit tests for complete and partial network data
Shane Lubold, Bolun Liu, Tyler H. McCormick
Networks describe the, often complex, relationships between individual actors. In this work, we address the question of how to determine whether a parametric…

The openVA Toolkit for Verbal Autopsies
Zehang Richard Li, Jason Thomas, Eungang Choi, Tyler H. McCormick, Samuel J. Clark
Verbal autopsy (VA) is a survey-based tool widely used to infer cause of death (COD) in regions without complete-coverage civil registration and vital…

Sequential Estimation of Temporally Evolving Latent Space Network Models
Kathryn Turnbull, Christopher Nemeth, Matthew Nunes, Tyler McCormick
In this article we focus on dynamic network data which describe interactions among a fixed population through time. We model this data using the latent space…

Anomaly Detection in Large Scale Networks with Latent Space Models
Wesley Lee, Tyler H. McCormick, Joshua Neil, Cole Sodja, Yanran Cui
We develop a real-time anomaly detection algorithm for directed activity on large, sparse networks. We model the propensity for future activity using a dynamic…

Identifying the latent space geometry of network models through analysis of curvature
Shane Lubold, Arun G. Chandrasekhar, Tyler H. McCormick
Statistically modeling networks, across numerous disciplines and contexts, is fundamentally challenging because of (often high-order) dependence between…

Consistently estimating graph statistics using Aggregated Relational Data
Emily Breza, Arun G. Chandrasekhar, Tyler H. McCormick, Mengjie Pan
Aggregated Relational Data, known as ARD, capture information about a social network by asking about the number of connections between a person and a group…

Regression of exchangeable relational arrays
Frank W. Marrs, Bailey K. Fosdick, Tyler H. McCormick
Relational arrays represent measures of association between pairs of actors, often in varied contexts or over time. Such data appear as trade flows between…

Modeling the social media relationships of Irish politicians using a generalized latent space stochastic blockmodel
Tin Lok James Ng, Thomas Brendan Murphy, Ted Westling, Tyler H. McCormick, Bailey K. Fosdick
D\'ail \'Eireann is the principal chamber of the Irish parliament. The 31st D\'ail \'Eireann is the principal chamber of the Irish parliament. The 31st D\'ail…

Using Bayesian latent Gaussian graphical models to infer symptom associations in verbal autopsies
Zehang Richard Li, Tyler H. McCormick, Samuel J. Clark
Learning dependence relationships among variables of mixed types provides insights in a variety of scientific settings and is a well-studied problem in…

Bayesian Joint Spike-and-Slab Graphical Lasso
Zehang Richard Li, Tyler H. McCormick, Samuel J. Clark
In this article, we propose a new class of priors for Bayesian inference with multiple Gaussian graphical models. We introduce fully Bayesian treatments of two…

Introducing Bayesian Analysis with $\text{m&m's}^\circledR$: an active-learning exercise for undergraduates
Gwendolyn Eadie, Daniela Huppenkothen, Aaron Springford, Tyler McCormick
We present an active-learning strategy for undergraduates that applies Bayesian analysis to candy-covered chocolate $\text{m&m's}^\circledR$. The exercise…

An Expectation Conditional Maximization approach for Gaussian graphical models
Zehang Richard Li, Tyler H. McCormick
Bayesian graphical models are a useful tool for understanding dependence relationships among many variables, particularly in situations with external prior…

Beyond prediction: A framework for inference with variational approximations in mixture models
Ted Westling, Tyler H. McCormick
Variational inference is a popular method for estimating model parameters and conditional distributions in hierarchical and mixed models, which arise…

Bayesian factor models for probabilistic cause of death assessment with verbal autopsies
Tsuyoshi Kunihama, Zehang Richard Li, Samuel J. Clark, Tyler H. McCormick
The distribution of deaths by cause provides crucial information for public health planning, response, and evaluation. About 60% of deaths globally are not…

Using Aggregated Relational Data to feasibly identify network structure without network data
Emily Breza, Arun G. Chandrasekhar, Tyler H. McCormick, Mengjie Pan
Social network data is often prohibitively expensive to collect, limiting empirical network research. Typical economic network mapping requires (1) enumerating…

Multiresolution network models
Bailey K. Fosdick, Tyler H. McCormick, Thomas Brendan Murphy, Tin Lok James Ng, Ted Westling
Many existing statistical and machine learning tools for social network analysis focus on a single level of analysis. Methods designed for clustering optimize…

Modeling Recovery Curves With Application to Prostatectomy
Fulton Wang, Tyler H. McCormick, Cynthia Rudin, John Gore
We propose a Bayesian model that predicts recovery curves based on information available before the disruptive event. A recovery curve of interest is the…

Inferring social structure from continuous-time interaction data
Wesley Lee, Bailey K. Fosdick, Tyler H. McCormick
Relational event data, which consist of events involving pairs of actors over time, are now commonly available at the finest of temporal resolutions. Existing…

Hyak Mortality Monitoring System: Innovative Sampling and Estimation Methods - Proof of Concept by Simulation
Samuel J. Clark, Jon Wakefield, Tyler McCormick, Michelle Ross
Traditionally health statistics are derived from civil and/or vital registration. Civil registration in low-income countries varies from partial coverage to…

Redrawing the 'Color Line': Examining Racial Segregation in Associative Networks on Twitter
Nina Cesare, Hedwig Lee, Tyler McCormick, Emma S. Spiro
Online social spaces are increasingly salient contexts for associative tie formation. However, the racial composition of associative networks within most of…

Estimating population size using the network scale up method
Rachael Maltiel, Adrian E. Raftery, Tyler H. McCormick, Aaron J. Baraff
We develop methods for estimating the size of hard-to-reach populations from data collected using network-based questions on standard surveys. Such data arise…

Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, David Madigan
We aim to produce predictive models that are not only accurate, but are also interpretable to human experts. Our models are decision lists, which consist of a…

Probabilistic Cause-of-death Assignment using Verbal Autopsies
Tyler H. McCormick, Zehang Li, Clara Calvert, Amelia C. Crampin, Kathleen Kahn, Samuel J. Clark
In regions without complete-coverage civil registration and vital statistics systems there is uncertainty about even the most basic demographic indicators. In…

Reactive point processes: A new approach to predicting power failures in underground electrical systems
Åžeyda Ertekin, Cynthia Rudin, Tyler H. McCormick
Reactive point processes (RPPs) are a new statistical model designed for predicting discrete events in time based on past history. RPPs were developed to…

InSilicoVA: A Method to Automate Cause of Death Assignment for Verbal Autopsy
Samuel J. Clark, Tyler McCormick, Zehang Li, Jon Wakefield
Verbal autopsies (VA) are widely used to provide cause-specific mortality estimates in developing world settings where vital registration does not function…

Clustering South African households based on their asset status using latent variable models
Damien McParland, Isobel Claire Gormley, Tyler H. McCormick, Samuel J. Clark, Chodziwadziwa Whiteson Kabudula, Mark A. Collinson
The Agincourt Health and Demographic Surveillance System has since 2001 conducted a biannual household asset survey in order to quantify household socio…

Latent demographic profile estimation in hard-to-reach groups
Tyler H. McCormick, Tian Zheng
The sampling frame in most social science surveys excludes members of certain groups, known as hard-to-reach groups. These groups, or subpopulations, may be…

Bayesian hierarchical rule modeling for predicting medical conditions
Tyler H. McCormick, Cynthia Rudin, David Madigan
We propose a statistical modeling technique, called the Hierarchical Association Rule Model (HARM), that predicts a patient's possible future medical…