Inference for Model-Agnostic Longitudinal Variable Importance
Talk, IMS International Conference on Statistics and Data Science (ICSDS), Lisbon, Portugal
I discuss inference for summaries of longitudinal model-agnostic variable importance.
Talks
All talks licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Inference for Model-Agnostic Longitudinal Variable Importance
Talk, Mental Health Research Network Statistical Methods Scientific Interest Group, Virtual
I discuss inference for summaries of longitudinal model-agnostic variable importance.
Statistics in Infectious Disease Research
Talk, University of Washington Biostatistics 111, Seattle, WA
Lecture on some of the ways that statistics is used in infectious disease research to students in UW Biostatistics 111.
Inference for Model-Agnostic Longitudinal Variable Importance
Talk, 15th International Conference of the European Consortium for Informatics and Mathematics Working Group on Computational and Methodological Statistics (CMStatistics), London, UK
I discuss a framework for inference on general model-agnostic variable importance measures and possible summary measures for longitudinal variable importance.
Scalable Phenotyping for Safety Outcomes Using Electronic Health Record Data
Talk, Forum on the Integration of Observational and Randomized Data, Washington, DC
I discuss a framework for predicting safety outcomes of interest within the FDA’s Sentinel system using data from electronic health records.
Inference for Model-Agnostic Variable Importance
Talk, International Chinese Statistical Association Applied Statistics Symposium, Gainesville, FL
I discuss a framework for inference on general model-agnostic variable importance measures and possible summary measures for longitudinal variable importance.
Flexible Variable Selection with Missing Data
Talk, WNAR 2022, Virtual
I discuss a method for model-agnostic variable selection that is robust to model misspecification and valid in settings with missing data.
Model-Agnostic Variable Importance and Selection
Talk, University of Washington Department of Biostatistics Seminar Series, Virtual
I discuss a framework for inference on general model-agnostic variable importance measures, and how this framework can be used to perform variable selection. I also briefly discuss several directions of current work, including longitudinal variable importance; a measure of how important variables are for tailoring treatment; and fairness-aware variable importance.
Inference for Model-Agnostic Variable Importance
Talk, American Statistical Association Statistical Learning and Data Science Webinar Series, Virtual
I discuss a framework for inference on general model-agnostic variable importance measures.
Flexible Variable Selection in the Presence of Missing Data
Talk, Joint Statistical Meetings, Virtual
I gave a talk on work in progress, developing methods for variable selection in settings with missing data that do not rely on (generalized) linear models.
Inference for Model-Agnostic Variable Importance
Talk, 3rd Annual Hutch United Symposium, Virtual
Keynote talk at the 3rd annual Hutch United Symposium.
Inference for Model-Agnostic Variable Importance
Talk, Kaiser Permanente Washington Health Research Institute Seminar Series, Virtual
I discuss a framework for inference on general model-agnostic variable importance measures.
Statistical Inference and Containerization in Computational Pipelines
Talk, Fred Hutchinson Cancer Research Center Biostatistics Program Seminar Series, Virtual
I discuss three approaches towards a more principled use of machine learning: inference on the goodness of fit, inference on variable importance, and containerization.
Statistics in Infectious Disease Research
Talk, Roanoke Valley Governor's School Computational Biology Course, Virtual
Guest lecture on some of the ways that statistics is used in infectious disease research in the Computational Biology course at Roanoke Valley Governor’s School.
A Unified Approach for Inference for Model-Agnostic Variable Importance
Talk, Vanderbilt University Department of Biostatistics Seminar, Virtual
I discuss a framework for inference on general model-agnostic variable importance measures.
Efficient Nonparametric Statistical Inference on Population Feature Importance using Shapley Values
Talk, 37th International Conference on Machine Learning, Virtual
We discuss our paper to be published in the Proceedings of the Thirty-seventh International Conference on Machine Learning.
Guiding HIV-1 Antibody Regimen Down-selection and Prevention Efficacy Trial Design using Machine Learning
Talk, 27th International Dynamics and Evolution of HIV and Other Human Viruses Meeting, Virtual
Contributed talk at the 27th International Dynamics and Evolution of HIV and Other Human Viruses Meeting on SLAPNAP (see publications).
A Unified Approach to Nonparametric Variable Importance Assessment
Talk, Joint Statistical Meetings, Denver, CO
This talk (on a preliminary version of my general variable importance paper published in Journal of the American Statistical Association) was selected for an ASA Nonparametrics Section Travel Award.
Assessing Variable Importance Nonparametrically using Machine Learning Techniques
Talk, UW Biostatistics Colloquium, Seattle, WA
Invited talk on my dissertation research given at the UW Biostatistics Colloquium.
Assessing Variable Importance Nonparametrically using Machine Learning Techniques
Talk, Joint Statistical Meetings, Baltimore, MD
This talk (on a preliminary version of my R-squared variable importance paper published in Biometrics) was selected for an ASA Biometrics Section Travel Award.
Nonparametric Variable Importance using an Augmented Neural Network with Multi-Task Learning
Talk, Thirty-fifth International Conference on Machine Learning, Virtual
Contributed talk at the Thirty-fifth International Conference on Machine Learning.
Assessing Variable Importance Nonparametrically using Machine Learning Techniques
Talk, WNAR of the International Biometric Society, Santa Fe, NM
This talk (on a preliminary version of my R-squared variable importance paper published in Biometrics) was selected as the Most Outstanding Oral Paper.