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Quantitative Issues in Cancer Research Working Group Seminar

In Person

Anuraag Gopaluni, PhD Candidate, Department of Biostatistics, Harvard University Methods for accurate real-time estimates of death in the context of reporting delays Abstract: State-level mortality data in the United States is subject to reporting delays of up to 18 weeks, causing gaps between reported and true mortality in the short-term. Existing methods for correcting gaps … Continue reading "Quantitative Issues in Cancer Research Working Group Seminar"

HIV Working Group Seminar

Virtual In Person

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.

Quantitative Issues in Cancer Research Working Group Seminar

In Person

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 … Continue reading "Quantitative Issues in Cancer Research Working Group Seminar"

Quantitative Issues in Cancer Research Working Group Seminar

In Person

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 … Continue reading "Quantitative Issues in Cancer Research Working Group Seminar"

Quantitative Issues in Cancer Research Working Group Seminar

In Person

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. … Continue reading "Quantitative Issues in Cancer Research Working Group Seminar"

Quantitative Issues in Cancer Research Working Group Seminar

In Person

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 … Continue reading "Quantitative Issues in Cancer Research Working Group Seminar"

Quantitative Issues in Cancer Research Working Group Seminar

Virtual

Jodeci Wheaden, PhD Student, Department of Biostatistics, Harvard T.H. Chan School of Public Health A multivariate Poisson-log normal mixture model for clustering transcriptome sequencing data Abstract: This talk will discuss the paper "A multivariate Poisson-log normal mixture model for clustering transcriptome sequencing data" by Silva et al. (2019), available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6636065/, in relation to the … Continue reading "Quantitative Issues in Cancer Research Working Group Seminar"

Quantitative Issues in Cancer Research Working Group Seminar

In Person

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 … Continue reading "Quantitative Issues in Cancer Research Working Group Seminar"

Quantitative Issues in Cancer Research Working Group Seminar

In Person

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 … Continue reading "Quantitative Issues in Cancer Research Working Group Seminar"

Thesis Defense - Ndey Isatou Jobe

Thesis Defense – Ndey Isatou Jobe

Virtual In Person

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.