Title: Robust trial augmentation using external data

Abstract: Randomized trials often have sample sizes that are too small to produce precise estimates of treatment effects. One approach for improving trial efficiency is to incorporate external data from previously completed trials or observational studies into the estimation process. When the external data are aligned with the trial data and statistical models for nuisance functions are correctly specified, using the external data can yield consistent estimates and enhance efficiency. 

Some degree of misalignment or misspecification, however, is usually expected and can threaten trial validity. We develop a class of estimators that exploit randomization to ensure consistency and asymptotic normality, even when the external data are misaligned with the trial. We also propose a procedure that uses members of this class to construct a combined estimator that is consistent and asymptotically normal and can leverage external data even when that data are misaligned with the trial, or when models for nuisance functions are mis-specified or have slow convergence rates. 

We show that the efficiency of the combined estimator is no lower than that of each of its component estimators (including the efficient trial-only estimator, if it is used as a component for the combined estimator). Our methods allow investigators to use external data to improve the trial's efficiency without concern for misalignment between the external data and the trial. We examine the finite-sample behavior of the proposed methods in simulation studies and apply them to analyze data from a trial comparing coronary artery bypass grafting surgery plus medical therapy versus medical therapy alone for patients with chronic coronary artery disease.

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Title: Designing Intelligent AI: Insights from Human Cognition

Abstract: Modern AI systems are evolving from passive tools toward agent-based systems that can reason, learn and interact with their environments. This talk discusses emerging research directions in generative AI, drawing inspiration from principles of human cognition including continuous learning and adaptation, effective knowledge transfer and multi-objective decision making. The goal is to stimulate new perspectives on building domain-aware and trustworthy AI systems that can operate reliably in complex, real-world settings.

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Title: Doubly robust estimation of causal effects for random object outcomes with continuous treatments 

Authors: Satarupa Bhattacharjee, Bing Li, Xiao Wu, Lingzhou Xue

Abstract: Causal inference is central to statistics and scientific discovery, enabling researchers to identify cause-and-effect relationships beyond associations. Although traditionally studied within Euclidean spaces, contemporary applications increasingly involve complex, non-Euclidean data structures that reside in abstract metric spaces known as random objects, such as images, shapes, networks, and distributions. 

This paper introduces a novel framework for causal inference with continuous treatments applied to non-Euclidean data. To address the challenges posed by the lack of linear structures, we leverage Hilbert space embeddings of the metric spaces to facilitate Frechet mean estimation and causal effect mapping. Motivated by a study on the impact of exposure to fine particulate matter on age-at-death distributions across U.S. counties, we propose a nonparametric, doubly-debiased causal inference approach for outcomes as random objects with continuous treatments. 

Our framework accommodates moderately high-dimensional vector-valued confounders and derives efficient influence functions for estimation, ensuring both robustness and interpretability. We establish asymptotic properties of the cross-fitted estimators and employ conformal inference techniques for counterfactual outcome prediction. Validated in both simulation and real-world environmental application, our framework extends causal inference methodologies to complex data structures, broadening its applicability across scientific disciplines.

Kickoff Event for the Department of History's CELEBRATING 1776 Series

This event will now be virtual. Click here to join

Join us for a lecture by Pulitzer Prize winner Joseph Ellis, moderated by University of Kentucky professor Amy Murrell Taylor.

About Joseph Ellis

The author of 12 books, Ellis was awarded the Pulitzer Prize for "Founding Brothers: The Revolutionary Generation" and won the National Book Award for "American Sphinx," a biography of Thomas Jefferson. He has taught at Mount Holyoke College, the University of Massachusetts and the U.S. Military Academy at West Point. His commentaries have been featured on CSPAN, CNN, and PBS’s News Hour, and he appears in the major new PBS documentary "The American Revolution."

Ellis’ latest work, "The Great Contradiction," examines how a government that had been justified and founded on the principles articulated in the Declaration of Independence institutionalized slavery and created a tidal wave of western migration by settlers who understood the phrase “pursuit of happiness” to mean the pursuit of Indian lands.

Click here to join virtually

Dr. Sang Mo Yang, Sogang University, South Korea

Title: Ferroelectricity at the Nanoscale: Emerging Materials and Local Probes

Abstract: Ferroelectricity on the nanoscale has been the subject of considerable interest in condensed matter physics for over half a century. Beyond its fundamental importance, ferroelectricity provides essential functionality for advanced electronic devices, including nonvolatile memories, field-effect transistors and tunnel junctions. 

However, conventional perovskite-based ferroelectric oxides (e.g., Pb(Zr,Ti)O3) face significant challenges in achieving device performance that can compete with current dynamic random-access memories and flash memories. Over the past decade, novel ferroelectricity has been discovered in new material systems, including fluorite-structured HfO2-based thin films, two-dimensional (2D) van der Waals (vdW) materials and 2D perovskite halides. These discoveries have brought about a renaissance in the ferroelectric research community. 

In this colloquium, I will present our group’s recent efforts to investigate and understand ferroelectricity across these emerging material platforms [1] using various scanning probe microscopy techniques.
[1] T. H. Jung et al., “Spatially Resolved Observation of Ferroelectric-to-Paraelectric Phase Transition in a Two-Dimensional Halide Perovskite,” Advanced Materials 37, 2506270 (2025)

Dr. Joel Leja, Penn State University

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