Tags: Colloquium Series

The Statistics Department hosts weekly colloquia on a variety of statistcal subjects, bringing in speakers from around the world.


Shiyu Wang

Exploratory Cognitive Diagnosis Models: Attribute Hierarchy Estimation and Exploration of Utilizing Eye-tracking Data. Abstract: Attribute hierarchy, the underlying prerequisite relationship among attributes, plays an important role in applying Cognitive Diagnosis Models (CDM) for designing efficient cognitive diagnostic assessments. However, there are limited statistical tools to directly estimate attribute hierarchy from response data. In this…


Andrew Brown

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging Abstract: Alzheimer's disease is a neurodegenerative condition that accelerates cognitive decline relative to normal aging. It is of critical scientific importance to gain a better understanding of early disease mechanisms in the brain to facilitate effective, targeted therapies. The volume of the hippocampus is often used in diagnosis and monitoring of the disease.…


William Brenneman

The Interplay between Statistical Practice and Academic Research Abstract: In the statistics field, there is a strong symbiotic relationship between academics and industry. We will explore this relationship primarily from a practicing statisticians’ perspective and discuss the lessons learned on how to foster, grow and bridge the gap between statistical practice and academic research. Several problems encountered in industry that led to academic…


David Banks

Deming and the Industries of Today Abstract: Dr. Deming was one of the foundational leaders in industrial statistics, with contributions to experimental design, sampling, and process control. More importantly, he changed the culture of business leadership in two nations, and implicitly, around the world. But the industries of his day focused on manufacturing, while today’s industries reflect the knowledge economy. This talk asks the industrial…


Vladimir Dragalin

SIMULATION GUIDED CLINICAL TRIAL DESIGN   Abstract: Simulation guided Clinical Trial Design is a framework for stimulating scientific dialogue between trial designer and clinical team with the final goal of developing the most appropriate design for the study. We create different “what-if” scenarios under different assumptions about the drug effect and simulate data from such scenarios and apply the design. We then see if under a scenario…


Shihao Yang

Inference of dynamic systems from noisy and sparse data via manifold-constrained Gaussian processes Abstract: Parameter estimation for nonlinear dynamic system models, represented by ordinary differential equations (ODEs) or partial differential equations (PDEs), using noisy and sparse experimental data is a vital task in many fields. We propose a fast and accurate method, manifold-constrained Gaussian process Inference, for this task. Our…


Xinyi Li

Functional Individualized Treatment Regimes with Imaging Features Abstract Precision medicine seeks to discover an optimal personalized treatment plan and thereby provide informed and principled decision support, based on the characteristics of individual patients. With recent advancements in medical imaging, it is crucial to incorporate patient-specific imaging features in the study of individualized treatment regimes. We propose a novel, data-…


Agus Sudjianto

Machine Learning for High-Risk Applications in Banking Renowned statistician George Box once famously stated, “All models are wrong, but some are useful.” In a world where machine learning increasingly automates important decisions about our lives, the consequences of model failures can be catastrophic. It’s critical to take deliberate steps to mitigate risk and avoid unintended harm. Evaluating the conceptual soundness of machine learning…


2023 Clemson-UGA Joint Colloquium

Do statisticians have labs? Introduction to the Precision Medicine Artificial Intelligence Lab (PHAIR) Event Slides Abstract: In this talk, I discuss a non-traditional approach to statistics training through the use of a virtual lab structure. For over a decade, the Precision Health Artificial Intelligence lab gradually evolved from an informal meeting for a few students engaged in similar research topics to a large virtual lab with about 30…


Tuo Zhao

Approximation and Statistical Properties of Deep Neural Networks on Structured Data Abstract Deep neural networks have demonstrated remarkable generalization performance in high dimensional problems, e.g., image classification, where each image contains a large number of pixels. Such appealing performance contradicts a fundamental theoretical challenge – curse of data dimensionality. To explain this huge gap, we take the data intrinsic geometric…
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