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Seminar Series

Highly Scalable Gaussian Processes for Spatial Data Analysis

October 29, 2020

Speakers:

Sudipto Banerjee, UCLA

The BIG DATA problem in multivariate geostatistics has fomented a rich literature on scalable methodologies for analysing massive multivariate spat

Fairness, Accountability, and Transparency: (Counter)-Examples from Predictive Models in Criminal Justice

October 26, 2020

Speakers:

Kristian Lum, University of Pennsylvania

The need for fairness, accountability, and transparency in computer models that make or inform decisions about people has become increasingly clear

Geometry of Optimal Mass Transport, where Probability, Control, and Physics Meet

October 22, 2020

Speakers:

Tryphon Georgiou, University of California, Irvine

Optimal Mass Transport (OMT) was conceived more than two centuries ago by Gaspar Monge (1781) as the mathematical problem to transfer mass between

Fr´echet Change Point Detection

October 1, 2020

Speakers:

Paromita Dubey, USC Marshall Business School

Change point detection is a popular tool for identifying locations in a data sequence where an abrupt change occurs in the data distribution and ha

Challenges in Analyzing Two-sided Markets and Its Application on Ridesourcing Platforms

December 6, 2019

Speakers:

Hongtu Zhu, University of North Carolina

In this talk, we will introduce a general analytical framework for large scale data obtained from two-sided markets, especially ride-sourcing platf

Likelihood-Based Inference for Stochastic Epidemic Models with Application to High-Resolution Contact Tracking Data

November 14, 2019

Speakers:

Jason Xu, Duke University

Stochastic epidemic models such as the Susceptible-Infectious-Removed (SIR) model are widely used to model the spread of disease at the population

Robust Risk Aggregation & Merging P- and E-values

December 5, 2019

Speakers:

Ruodu Wang, University of Waterloo

Modeling inter-dependence among multiple risks often faces statistical as well as modeling challenges, with considerable uncertainty arising natura

Monte Carlo Methods for some not-quite-but-almost Bayesian Problems

November 21, 2019

Speakers:

Pierre Jacob, Harvard University

This talk will describe computational problems motivated by some approaches to statistical inference where the statistician tries to represent unce

Integrated Copula Spectral Densities and their Applications

November 7, 2019

Speakers:

Tobias Kley, Bristol University

Copula spectral densities are defined in terms of the copulas associated with the pairs $(X_{t+k}, X_t)$ of a process $(X_t)_{t \in \mathbb{Z}}$.

Private Data + Approximate Computation = Exact Inference

October 31, 2019

Speakers:

Ruobin Gong, Rutgers University

Abstract: From data collection to model building and to computation, statistical inference at every stage must reconcile with imperfections.

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