Tags: Colloquium Series

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


Weijie Su

When Will You Become the Best Reviewer of Your Own Papers? A Truthful Owner-Assisted Scoring Mechanism Zoom Info Link: https://zoom.us/j/93936847707?pwd=ZWFVRWlqL0x1bWxNM2xMQTczb3lPQT09 Meeting ID: 939 3684 7707 Passcode: 821272 Abstract In 2014, NeurIPS received 1,678 paper submissions, while this number increased to 10,411 in 2022, putting a tremendous strain on the peer review process. In this talk, we attempt to address this challenge…


Nilanjan Chatterjee

Biography Dr. Nilanjan Chatterjee is a Bloomberg Distinguished Professor of Biostatistics and Genetic Epidemiology at Johns Hopkins University, with appointments in the Department of Biostatistics in the Bloomberg School of Public Health and in the Department of Oncology in the Sidney Kimmel Comprehensive Cancer Center in the Johns Hopkins School of Medicine. He was formerly…


Claire Donnat

Leveraging Horizontal and Vertical Network Structure Abstract The recent years have witnessed a surge in the amount of available structured data, typically modelled as a network capturing the relationships between different entities. This structure can either hold “horizontally” across features, or “vertically” across observations, and can be leveraged to considerably improve estimation - two aspects that we propose exploring throughout this…


Robert Mee

A Synthesis for Order-of-Addition Models Abstract: A wide variety of models have been proposed recently for experiments that vary the order in which components are added, or steps are performed. There are models with a linear effect of each component's position in the sequence, and models with added quadratic effects and product terms. Kriging models based on a component's position have also been proposed. Before models based on each component's…


Rebecca Steorts

Almost All of Entity Resolution Abstract Whether the goal is to estimate the number of people that live in a congressional district, to estimate the number of individuals that have died in an armed conflict, or to disambiguate individual authors using bibliographic data, all these applications have a common theme—integrating information from multiple sources. Before such questions can be answered, databases must be cleaned and integrated in a…


Georgios Fellouris

Sequential multiple testing  Abstract In this talk we will consider the multiple testing problem when theobservationsare collected in real time over multiple data streams and the goal isto decide as quickly as possible. In this context, a multiple testingprocedure consists of not only a decision rule but also a stopping rule.We will present such pairs that minimize asymptoticallythe expected time for decision, uniformly with respect to the…


Jyotishka Datta

Recent Developments in Bayesian Shrinkage for Sparse and Structured Data Abstract Sparse signal recovery remains an important challenge in large scale data analysis and global-local (G-L) shrinkage priors have undergone an explosive development in the last decade in both theory and methodology. These developments have established the G-L priors as the state-of-the-art Bayesian tool for sparse signal recovery. In the first part of my talk, I will…


Simon Mak

Graphical multi-fidelity Gaussian process modeling, with applications to emulation of heavy-ion collisions Abstract: With advances in scientific computing, complex phenomena can now be reliably simulated via computer code. Such simulations can be very time-intensive, requiring millions of CPU hours to perform. One solution is multi-fidelity emulation, which uses data of varying accuracies (or fidelities) to train an efficient predictive model…


Qing Mai

Tensor t-Distribution and Tensor Response Regression Abstract: In recent years, promising statistical modeling approaches to tensor data analysis have been rapidly developed. Traditional multivariate analysis tools, such as multivariate regression and discriminant analysis, are now generalized from modeling random vectors and matrices to higher-order random tensors (a.k.a. array-valued random objects). Equipped with tensor algebra and high-…


Yuanzhe Xi

Accelerating Gaussian process regressions with preconditioning Kernel matrices can help handle nonlinearities in the data in many machine learning applications. The entries of the kernel matrix are defined as the values of a kernel function at all pairs of points from a given dataset. Since the spectrum of the kernel matrix associated with the same dataset can vary dramatically as the parameters of the kernel function change, developing robust…
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