Luke will present the thesis entitled “Identifying Mixtures of PFAS Exposure Profiles in a cohort of pregnant women using a Bayesian nonparametric Gaussian Mixture Model". The thesis committee is chaired by Dr. Briana Stephenson, and includes Dr. Tamarra James-Todd and Dr. Jeffrey Miller.
Asja will present the thesis entitled “Penalization of the Log-Binomial Model". The thesis committee is chaired by Dr. Michelle Hacker, and includes Dr. Paul Catalano and Dr. Erin Lake.
Sara will present the thesis entitled “Evaluating Genetic Heritability of Obstructive Sleep Apnea and Cardiometabolic Comorbidities: A Comparative Analysis of post-GWAS Local Genetic Correlation Approaches". The thesis committee is chaired by Dr. Brian Cade, and includes Dr. Sharon Lutz and Dr. Erin Lake.
Stephanie will present the dissertation entitled “Bayesian Weighted Clustering Methods for Dietary Survey Data". The dissertation committee is co-chaired by Dr. Briana Stephenson and Dr. Michael Hughes, and includes Dr. Brent Coull and Dr. Sebastien Haneuse.
Isatou will present the thesis entitled “Evaluating Disparities in End-Stage Renal Disease Risk Prediction Using the All of Us Cohort". The thesis committee is chaired by Dr. Rui Duan, and includes Dr. Sebastien Haneuse and Dr. Erin Lake.
Elizabeth Graff, PhD Student, Department of Biostatistics, Harvard T.H. Chan School of Public Health Discussion of "Contrastive Learning Inverts the Data Generating Process" by Zimmerman et. al (2021) Abstract: Contrastive learning has recently seen tremendous success in self-supervised learning. So far, however, it is largely unclear why the learned representations generalize so effectively to a...
Riddhiman Saha, PhD Student, Department of Biostatistics, Harvard T.H. Chan School of Public Health Estimating Treatment Effects using Aggregate-level Data from External Controls Abstract: Randomized controlled trials (RCTs) are the gold standard for assessing new treatments, but they are often infeasible in certain contexts, such as life-threatening or rare diseases, due to ethical or practical...
Kimberly Greco, PhD Student, Department of Biostatistics, Harvard T.H. Chan School of Public Health Building Graph Neural Networks from the Ground Up: Overcoming Challenges in Disease Prediction from EHR Abstract: Graph Neural Networks (GNNs) have emerged as powerful tools for disease prediction using Electronic Health Records (EHRs), enabling breakthroughs in identifying latent health conditions and...
Christian Covington, PhD Student, Department of Biostatistics, Harvard T.H. Chan School of Public Health Multiverse Analysis for Causal Inference (and vice versa) Abstract: Multiverse analysis is a framework developed in the quantitative social and behavioral sciences, designed to represent “non-statistical” uncertainty that arises in data analysis when making choices about conceptual operationalization, data preparation, etc....
Omar Melikechi, Postdoctoral Research Fellow, Department of Biostatistics, Harvard University Integrated path stability selection Abstract: Feature selection aims to identify important features in a data set, which can lead to more accurate and interpretable results. For example, it has been used to identify genes that are associated with certain diseases. Stability selection is a popular...
Phillip Nicol, PhD Candidate, Department of Biostatistics, Harvard University Identifying spatially variable genes by projecting to morphologically relevant directions Abstract: Spatial transcriptomics allows for high-resolution sequencing while retaining two-dimensional sample coordinates. A common goal is to identify spatially variable genes within a predefined cell type or domain. However, in many cases this region is implicitly...
HIV Training Grant Lightning Talks Abstract: Join us in learning about the important work being conducted by the PhD and Postdoctoral researchers on the HIV Training Grant! Over two sessions, all 10 trainees will present 5-minute lightning talks about their research projects shaping the future of infectious disease and adjacent areas.
