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Mathematics & Statistics
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  Colloquium Archive

Colloquia — Spring 2021

Friday, January 29, 2021

Title
Speaker

Time
Place
Sponsor

Engrafting Statistical Methodologies on Artificial Intelligence to Solve Image Segmentation Problems
Jiwoong Kim
Michigan State University
3:00pm–4:00pm
MS Teams
K. Ramachandran










Abstract

We are observing keen competition to win in the Fourth Industrial Revolution, which is featured by a relentless advance of technological breakthroughs. Among those technological breakthroughs, artificial intelligence such as machine learning has attracted attention from scientists as a possible alternative to solve image segmentation problems. Especially, medical image segmentation — which segments object of interest such as a tumor in a medical image — has gained enormous attention since it can be applied to diagnose a disease. For example, medical institutions and pharmaceutical companies are rushing to adopt artificial intelligence to diagnose a variety of intractable brain diseases such as dementia and Alzheimer’s disease. In this presentation, we will demonstrate that the performance of artificial intelligence can be enhanced much more when it is integrated with statistical methodologies. Case studies illustrate how artificial intelligence enhanced by those methodologies can be applied to the medical diagnosis of intractable brain diseases.

This is joint work with Hira Koul and Jinhee Jang.

Thursday, January 28, 2021

Title
Speaker

Time
Place
Sponsor

Gaussian Process Modeling with Applications in Remote Sensing and Coastal Flood Hazard Studies
Pulong Ma
Duke University and Statistical and Applied Mathematical Sciences Institute
3:00pm–4:00pm
MS Teams
K. Ramachandran

Abstract

With space-based observations, remote sensing technology provides a wealth of information for understanding geophysical processes with unprecedented spatial and temporal coverage. Quantitative inference for the global carbon cycle has been bolstered by greenhouse gas observing satellites. NASA’s Orbiting Carbon Observatory-2 (OCO-2) collects tens of thousands of observations of reflected sunlight daily. These observed spectra, or radiances, are used to infer the atmospheric carbon dioxide (CO2) at fine spatial and temporal resolution with substantial coverage across the globe. Remote sensing data processing pipeline involves a series of steps that process the raw instrument data into geophysical variables. Uncertainty quantification (UQ) provides a statistical formalism to understand and characterize uncertainty in this pipeline. Statistical analysis of such data at various levels of processing steps needs to deal with a wide range of challenging problems such as high-dimensionality and nonstationarity.

In the first part of my talk, I will give an introduction to the OCO-2 mission and science that motivate this research. In the second part of my talk, I will introduce a new family of covariance functions called the Confluent Hypergeometric (CH) class for kriging or Gaussian process modeling, which has been widely used to understand and predict real-world processes. In the past several decades, the Matérn covariance function has been a popular choice to model dependence structures in spatial statistics. A key benefit of the Matérn class is that it is possible to get precise control over the degree of differentiability of the process realizations. However, the Matérn class possesses exponentially decaying tails, and thus may not be suitable for modeling polynomial-tailed dependence. This problem can be remedied using polynomial covariances; however, one loses control over the degree of differentiability of the process realizations, in that the realizations using polynomial covariances are either infinitely differentiable or not differentiable at all. To overcome this dilemma, a new family of covariance functions is constructed using a scale mixture representation of the Matérn class where one obtains the benefits of both Matérn and polynomial covariances. The resultant covariance contains two parameters: one controls the degree of differentiability near the origin and the other controls the tail heaviness, independently of each other. The CH class also enjoys nice theoretical properties under infill asymptotics including equivalence measures, asymptotic behavior of the maximum likelihood estimators, and asymptotically efficient prediction under misspecified models. The improved theoretical properties in the predictive performance of the CH class are verified via extensive simulations. Application using OCO-2 data confirms the advantage of the CH class over the Matérn class, especially in extrapolative settings. Finally, I will give a brief overview of my research in UQ for remote sensing and coastal flood hazard studies as well as future research directions.

Friday, January 22, 2021

Title
Speaker

Time
Place
Sponsor

Signals Aggregation and Detection Methods for Biomedical Data Analysis
Hong Zhang
Merck Research Laboratories
3:00pm–4:00pm
MS Teams
K. Ramachandran

Abstract

The statistical theory of signals aggregation has played a key role in advancing scientific research in many areas. The combination of \(p\)-values or, equivalently, statistics is one of the most popular and successful approaches for information-aggregated decision making in a lot of applications such as signals detection, data integration and meta-analysis. In this talk, we present recent research progress on both summation-based and maximum-based \(p\)-values combination methods. The distributional properties of these statistics are studied under a global hypothesis testing framework. Efficient \(p\)-value calculation algorithms are proposed to accurately control the type I errors under correlated inputs. Extensive simulation has been conducted to compare the statistical power of these methods. We finally demonstrate the practical utility of these combination methods through various applications in genetic association studies and clinical data analysis.

Thursday, January 21, 2021

Title
Speaker
Time
Place
Sponsor

Bayesian empirical likelihood for ridge and lasso regression
Adel Bedoui
3:00pm–4:00pm
MS Teams
K. Ramachandran

Abstract

Empirical likelihood (EL) is a nonparametric analog of standard likelihood, and inherits many of its properties. Accordingly, Bayesian EL, which replaces the ordinary likelihood function with an empirical likelihood function, has been developed. In this talk, I will briefly introduce empirical likelihood in both its standard and Bayesian versions.

A recent development is Bayesian EL for regularized methods such as ridge and lasso regression. The theoretical background for Bayesian ridge and Bayesian lasso empirical likelihoods will be presented, along with discussion of computational issues.

This is joint work with Nicole Lazar.