When: Thursday, August 22, 2024—2:50 p.m. to 3:50 p.m.
Where: LeConte 224

Speakers: Dr. Melissa Smith, Department of Biostatistics, University of Alabama at Birmingham

Abstract: A causal decomposition analysis allows researchers to determine whether the difference in a health outcome between two groups can be attributed to a difference in each group's distribution of one or more modifiable mediator variables. With this knowledge, researchers and policymakers can focus on designing interventions that target these mediator variables. In this talk, I will discuss the similarities and differences between a causal mediation analysis and a causal decomposition analysis. I will then present our recent work on a method for performing causal decomposition analyses with multiple correlated mediator variables. Existing methods for causal decomposition analysis either focus on one mediator variable or assume that each mediator variable is conditionally independent given the group label and the mediator-outcome confounders. Our Monte Carlo-based causal decomposition analysis method is designed to accommodate multiple correlated and interacting mediator variables, while identifying path-specific effects through individual mediators. I will illustrate an evaluation of our method through a simulation study and an application to examine potential reasons for Black-White differences in incident diabetes using data from a national cohort study.

When: Thursday, September 12, 2024—2:50 p.m. to 3:50 p.m.
Where: LeConte 224

Speakers: Dr. Will Cipolli, Department of Mathematics, Colgate University

Abstract: Much work has been done in "robustifying" standard statistical approaches with mixtures of multivariate Polya trees (MMPTs). In this talk, I will present a FAST Markov chain Monte Carlo (MCMC) sampling technique for MMPTs that overcomes difficulties in traditional sampling procedures and is completed in a fraction of the time. This new technique permits time-feasible Bayesian nonparametric solutions to contexts requiring many or repeated density estimates. The efficacy of this approach will be demonstrated via simulation and biomedical applications.

When: Thursday, September 19, 2024—2:50 p.m. to 3:50 p.m.
Where: LeConte 224

Speakers: Dr. Ian Dryden, Department of Statistics, University of South Carolina

Abstract: Complex object data such as networks and shapes are becoming increasingly available, and so there is a need to develop suitable methodology for statistical analysis. Networks can be represented as graph Laplacian matrices, which are a type of manifold-valued data. Shapes of 3D objects are also a type of manifold-valued data, invariant to translation, rotation and scale. Our main objective is to estimate a regression curve from a sample of graph Laplacian matrices or 3D shapes conditional on a set of Euclidean covariates, for example in dynamic objects where the covariate is time. We develop an adapted Nadaraya-Watson estimator which has uniform weak consistency for estimation using Euclidean and power Euclidean metrics, and we also explore splines on shape spaces.

When: Thursday, September 26, 2024—2:50 p.m. to 3:50 p.m.
Where: LeConte 224

Speakers: Dr. Kimberly Sellers, Department of Statistics, North Carolina State University

Abstract: While the Poisson distribution is a classical statistical model for count data, it hinges on the constraining equi-dispersion property (i.e. that the mean and variance equal). This assumption, however, does not usually hold for real count data; over-dispersion (i.e. when the variance is greater than the mean) is a more common phenomenon for count data, however data under-dispersion has also been prevalent in various settings. It would be more convenient to work with a distribution that can effectively model data (over- or under-) dispersion because it can offer more flexibility (and, thus, more appropriate inference) in the statistical methodology. This talk introduces the Conway-Maxwell-Poisson distribution along with several associated statistical methods motivated by this model to better analyze count data under various scenarios (e.g. distributional theory, generalized linear modeling, control chart theory, and count processes). As time permits, this talk will likewise acquaint the audience with available associated tools for statistical computing.

When: Thursday, October 03, 2024—2:50 p.m. to 3:50 p.m.
Where: LeConte 224

Speakers: Dr. Weijie Su, Department of Statistics and Data Science, University of Pennsylvania

Abstract: Large language models (LLMs) have rapidly emerged as a transformative innovation in machine learning. However, their increasing influence on human decision-making processes raises critical societal questions. In this talk, we will demonstrate how statistics can help address two key challenges: ensuring fairness for minority groups through alignment and combating misinformation through watermarking. First, we tackle the challenge of creating fair LLMs that equitably represent and serve diverse populations. We derive a regularization term that is both necessary and sufficient for aligning LLMs with human preferences, ensuring equitable outcomes across different demographics. Second, we introduce a general statistical framework to analyze the efficiency of watermarking schemes for LLMs. We develop optimal detection rules for an important watermarking scheme recently developed at OpenAI and empirically demonstrate its superiority over the existing detection method. Throughout the talk, we will showcase how statistical insights can not only address pressing challenges posed by LLMs but also unlock substantial opportunities for the field of statistics to drive responsible generative AI development. This talk is based on arXiv:2405.16455 and arXiv:2404.01245.

When: Thursday, October 10, 2024—2:50 p.m. to 3:50 p.m.
Where: LeConte 224

Speakers: Dr. Hongtu Zhu, Department of Biostatistics, University of North Carolina at Chapel Hill

Abstract: This talk provides an insightful overview of integrating artificial intelligence (AI) and statistical methods in medical data analysis. It is structured into three key sections: Introduction to Medical Image Data Analysis: This section sets the stage by outlining the fundamentals and significance of medical image analysis in healthcare, charting its evolution and current applications. State-of-the-Art AI Applications and Statistical Challenges: Here, we explore the impact of AI, particularly deep learning, on medical imaging, and address the accompanying statistical challenges, such as data quality and model interpretability. Opportunities for Statisticians: The final section highlights the critical role of statisticians in refining AI applications in medical imaging, focusing on opportunities for advancing algorithmic accuracy and integrating statistical rigor. The talk aims to demonstrate the crucial synergy between AI and statistics in enhancing medical data analysis, emphasizing the evolving challenges and the vital contributions of statisticians in this domain.