Видео

Juan Pablo Lewinger: Targeted learning overview: a path toward greater data analysis rigor

Joycelynne Palmer: “The Changing Landscape of Early-Phase Cell and Immunotherapy Clinical Trials in Oncology” The early-phase trial design landscape in oncology continues to evolve in direct response to advances in our understanding of cancer biology and the development of cell and immunotherapy. Given the underlying complexity and cost associated with conducting such studies, in recent years, ...

Chanita Hughes-Halbert: “Integrating Social Determinants of Health into Biostatistics Research and Practice” The goal of Dr. Hughes-Halbert’s research program is to improve the precision of multilevel strategies for achieving health equity by identifying diverse determinants of minority health and cancer health disparities and by translating this information into sustainable interventions in cl...

The role of gene-environment (GxE) interaction in disease and complex trait architectures is widely hypothesized, but currently unknown. In the first part of the talk, I will discuss new approaches to measuring the contribution of polygenic GxE to complex traits using three complimentary statistical approaches based on genetic correlation, SNP-heritability differences, and polygenic risk score ...

"Constrained multivariate functional principal components analysis for novel outcomes in eye-tracking experiments" Individuals with autism spectrum disorder (ASD) tend to experience greater difficulties with social communication and processing sensory information. Of particular interest in ASD, biomarker research is the study of visual attention, effectively quantified in eye tracking (ET) expe...

Chun Li: Random Thoughts from a Biostatistician Trying to Catch up with Al

This presentation introduces PyTorch and machine learning, illuminating the theoretical foundations and practical applications of these transformative technologies. It will provide an overview of deep learning's core concepts, then progress to an in-depth exploration of PyTorch, a leading machine learning library renowned for its flexibility, ease of use, and dynamic computation capabilities. A...

Much of modern computational statistics relies upon high dimensional likelihoods whose gradients can be tedious to derive and correctly implement. Recent advances in automatic differentiation (i.e. autodiff) have enabled ultra-high dimensional objectives to be optimized (e.g., deep learning), yet their use for statistical settings has received less attention. Here I'll showcase the utility of a...

Talk given at NCI Division of Cancer Epidemiology and Genetics Seminar on November 30, 2023.

Twenty-five years ago, baseball statistics were primarily limited to what could be found in the newspaper. Today, all 30 teams employ teams of statistical analysts to inform player acquisition, coaching, and on-field strategy. Two members of the Dodgers Quantitative Analysis group will discuss the history of statistics in baseball, recent developments in data and methods of analysis, and how th...

Rank-based analyses are robust because they are insensitive to extreme values and skewed distributions, and they do not depend on the scale of the outcome. However, rank regression for continuous outcomes is virtually nonexistent in practice. We have developed cumulative probability models (CPMs), in which continuous outcomes are analyzed as if they are ordinal. CPMs are semiparametric models, ...

Shanpeng Li (City of Hope): “Novel Approaches to Joint Modeling of Longitudinal and Time-to-Event Data: With Applications to Dynamic Predictions of Health Outcome Using Massive Electronic Health Records” It is often of interest to study the temporal patterns of longitudinal data and associate them with time to event data in biomedical studies. Joint models of longitudinal and time-to-event data...

This Happy Scientist session will explore the concept of “Copilot for R”, an emerging AI technology that acts as a collaborative partner for data analysts and scientists. By seamlessly integrating AI into the R environment, Copilot for R assists in various stages of the data analysis pipeline, from data preprocessing and feature engineering to model selection and deployment.

2023 R Bootcamp day 3: Automatic reports with Rmarkdown

2023 R Bootcamp day 2: Data wrangling fundamentals

2023 R Bootcamp Day 1: What is R? A motivating example of data viz

Michael Pazzani: “Explanation in Artificial Intelligence and Medicine”

Patrick Turley: “A General Approach to Adjusting Genetic Studies for Assortative Mating”

Serghei Mangul: “Systematic Benchmarking of Omics Computational Tools and Databases”

Ver Steeg and Marmarelis: “Latent Factor Discovery with Transcriptomics Data”

Natalie DelRocco: “Two Contributions to Statistical Improvement of Clinical Research”

Huaiyu Mi: Annotation Query (AnnoQ): An easy-to-use tool for large-scale genetic variant annotation

Fernandez: Study Design and Statistical Modeling to Enable Data-Driven Policy in the Opioid Epidemic

Millstein: “FDR Confidence Interval Selection and Adjustment for Large-Scale Hypothesis Testing”

Lu Zhang, Getting started with Stan: A hands-on introduction to the "8 schools" problem

Masoud Fallah-Shorshani: “Estimating Traffic Noise and Developing an Air Quality Noise Based Model”

Kelly Street: “Analysis of Single Cell T-Cell Receptor Sequencing”

Doc Edge: “Using Gene Genealogies to Understand Complex Traits”

Jason Moore: “Accessible artificial intelligence for data science”