Spring 2026 Departmental Seminars & Lectures
During the Fall and Spring semesters, the Department of Biostatistics holds regular seminars on Thursdays, called the Levin Lecture Series, on a wide variety of topics which are of interest to both students and faculty. Over each semester, there are also often guest lectures outside the regular Thursday Levin Lecture Series, to provide a robust schedule the covers the wide range of topics in Biostatistics. The speakers are invited guests who spend the day of their seminar discussing their research with Biostatistics faculty and students. All Levin Lectures will be hosted over zoom, with the following credentials: Meeting ID:913 0905 0869; Passcode: 556019
Spring 2026 Schedule
Thursday, February 12th, Zoom
Levin Lecture
Liangyuan Hu, PhD
Associate Professor of Biostatistics and Epidemiology, Rutgers School of Public Health
Bayesian Machine Learning for Causal Inference and Real-World Evidence
Abstract:
In this talk, I will present a suite of Bayesian machine learning methods that strengthen causal inference with complex real-world data. First, I introduce riAFT-BART, a random-intercept accelerated failure time model that uses Bayesian additive regression trees to estimate causal effects of multiple treatments on clustered time-to-event outcomes. The approach flexibly captures nonlinear covariate effects and heterogeneous treatment responses in hierarchical data. I pair this model with a new Bayesian sensitivity analysis that quantifies how unmeasured confounding could alter posterior causal conclusions. Second, for longitudinal observational studies with time-to-event outcomes, I develop an alternative survival g-formula that embeds BART within the evolving generative components to reduce bias from model misspecification. Focusing on binary time-varying treatments, I propose a class of discrete-time survival g-formulas that incorporate longitudinal balancing scores for both static and dynamic treatment strategies, along with posterior sampling algorithms for inference. I also present a loss-based Bayesian sensitivity analysis that propagates uncertainty while assessing departures from the no unmeasured time-varying confounding assumption. I illustrate these methods in two applications: (i) using the National Cancer Database to compare three treatment strategies for high-risk localized prostate cancer with riAFT-BART and its sensitivity framework, and (ii) applying the new survival g-formula to electronic health record data from the Yale New Haven Health System.
Thursday, February 19th, Hess Commons
Levin Lecture
Xinyi Li, PhD
Assistant Professor, Mathematical and Statistical Sciences, Clemson University
From Functional PCA to Precision Medicine: Regression Inference and Individualized Treatment Rules with Imaging Features
Abstract:
Modern studies increasingly pair clinical features with high-dimensional imaging, where each scan can be viewed as a function living in a Hilbert space. This talk introduces a unified approach that incorporate imaging data as interpretable features via functional principal component analysis (FPCA). First, we discuss a framework for linear regression with Hilbert-space-valued covariates that provides asymptotic normal inference and bootstrap uncertainty quantification, explicitly accounting for the fact that FPCA bases are estimated from data. Second, we use the proposed multi-dimensional FPCA features from imaging to estimate individualized treatment regime under standard causal assumptions, enabling treatment decisions informed by patient-specific imaging patterns along with risk factors. The proposed methods are applied to Alzheimer's Disease Neuroimaging Initiative (ADNI) data, where PET scans and genetic and demographic covariates are used to model cognitive outcomes and guide personalized treatment strategies.
Thursday, February 26th, Zoom
Levin Lecture
Nancy R. Zhang, PhD
Ge Li and Ning Zhao Professor, Professor of Statistics and Data Science, The Wharton School at the University of Pennsylvania
Data Integration in Spatial and Single Cell Omics: What is Erased, and Can you Recover it?
Abstract: In single-cell and spatial biology, data integration refers to the alignment of cells across samples and modalities, and is an ubiquitous challenge affecting all downstream analyses. The goal in cell integration is to find cells across data sets that share the same biological state that may be obscured by technical differences.
In this talk, I will cast the cell integration problem on a continuum of weak to strong linkage, depending on the strength of feature sharing between experiments. First, I will examine integration across data modalities of weak linkage. This arises when there are few shared features between the data being integrated, for example, between single-cell RNA sequencing data and spatial proteomics data. For this, I will present MaxFuse, a method that leverages higher order relationships between all features, including unshared features, to achieve accurate integration. Next, we consider the scenario of data alignment across the same modality in clinical scale studies. For this setting, I will show that existing paradigms are overly aggressive, erasing disease and treatment effects and introducing severe data distortion. I will introduce a "pool-of-controls" experimental design concept to disentangle biological variation from unwanted variation. Based on this, I will describe CellANOVA, a novel statistical model and scalable algorithm that recovers biological signals lost during batch integration and corrects integration related data distortion. Through these two contrasting paradigms, I will share the key lessons learned and the remaining challenges in this field.
Thursday, March 5th, Zoom
Levin Lecture
Ji-Hyun Lee, PhD
Professor, Department of Biostatistics, University of Florida
Conversations with a Collaborative Biostatistician: The Quiet Power of Everyday Statisticians
Abstract:
In a world that prizes headline breakthroughs, the steady, collaborative work of everyday statisticians often flies under the radar. In this interview‑style seminar, I will share a series of candid conversations that illustrate how routine statistical thinking, cross‑disciplinary teamwork, and inclusive leadership drive scientific discovery and improve patient outcomes. I’ll also highlight the initiatives I launched as ASA’s 2025 President to broaden community engagement, advance data‑science practice, and spotlight the tangible impact that ordinary experts have on meaningful research. Join me to uncover the quiet power that underpins modern science
Thursday, March 12th, Zoom
Levin Lecture
Vadim Zipunnikov, PhD
Professor, Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health
Title Forthcoming
Abstract:
Abstract Forthcoming
Thursday, March 26th, Zoom
Levin Lecture
Vanessa Didelez, PhD
Professor of Statistics, Leibniz Institute for Prevention Research and Epidemiology
Title Forthcoming
Abstract:
Abstract Forthcoming