Kimberly Greco, PhD Student, Department of Biostatistics, Harvard T.H. Chan School of Public Health Graph Attention Framework to Enhance Rare Disease Sub-Phenotyping from EHR Abstract: Accurately sub-phenotyping patients according to their risk for an adverse clinical outcome can significantly enhance clinical decision-making. Recent advances in patient representation learning have enabled the development of sophisticated clustering...

Elizabeth Graff, PhD Student, Department of Biostatistics, Harvard T.H. Chan School of Public Health Applications of Deep Learning for Graph-Structured Data: From Disease Spread to Social Networks Abstract: How can we apply deep learning to solve problems in modeling the spread of disease? In this talk, we will explore the components and applications of Graph...

Christian Covington, PhD Student, Department of Biostatistics, Harvard T.H. Chan School of Public Health Statistical theory and the practice of data analysis: A brief and biased history Abstract: This talk gives an account of the replication crisis and how different disciplines– namely applied sciences, statistics, and theoretical computer science (TCS), have developed their own research...

Mónica Robles Fontán, PhD Student, Department of Biostatistics, Harvard T.H. Chan School of Public Health Leveraging Record Linkage To Enhance Public Health Research Abstract: Record linkage is the task of combining records from different populations that belong to a single entity to create a new single population. This task allows researchers to take advantage of...

Tanayott (Tony) Thaweethai Associate Director for Biostatistics Research and Engagement, Massachusetts General Hospital Biostatistics Development of a symptom-based definition of long COVID using negative-unlabeled data Abstract: While most people infected with COVID-19 recover after the acute phase of infection, some people continue to experience persistent symptoms months and even years after infection. These symptoms, also...

Phillip Nicol, PhD Student, Department of Biostatistics, Harvard T.H. Chan School of Public Health Estimation in Poisson log-bilinear models Abstract: The Poisson log-bilinear model, also known as GLM-PCA, is a commonly used approach for dimension reduction in single-cell RNA-seq data. Model parameters are usually estimated via maximum likelihood. However, we show that the MLE can...