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Machine Learning for Health Seminar Series Spring 2023

Join us for the Spring 2023 Machine Learning for Health Seminar Series. These talks will explore machine learning, its methodology, and application in biomedicine and health. The purpose of this series is to serve as an introduction to machine learning for researchers, clinician scientists, and others who may be interested in using these methods in their research.

February 24, 2023 (12 - 1 PM) | Chris Schmid, PhD - Professor of Biostatistics, Brown University; Adam Levine, MD - Professor of Emergency Medicine and Health Services, Policy & Practice, Brown University; Kexin Qu, PhD Candidate - Department of Biostatistics, Brown University. Presentation Title: "Validating a Predictive Model"

March 3, 2023 (12 - 1 PM) | Katie Brown, MSN, RN - PhD Candidate with Brown Center for Biomedical Informatics, Brown University. Presentation Title: "Unsupervised Machine Learning: An Overview of Methods and Case Study in Using Cluster Analysis for Studying COVID-19 Mental Health Outcomes"

April 14, 2023 (12 - 1 PM) | Sarah Brown, PhD - Assistant Professor of Computer Science, University of Rhode Island. Presentation Title: "Assessing Machine Learning Problems for Fairness Before Fitting"

This series is hosted by Advance-CTR, and co-sponsored with the Data Science Initiative and the Brown Center for Biomedical Informatics.

This seminar will be recorded. By registering for this event, you consent to audio and video recording, without compensation, for use by Advance-CTR and its partners.

Please note the 4/14 seminar will be accessed via a different Zoom link. Subscribe to our e-letter on AdvanceCTR.org for the latest seminar updates and registration links.