Statistics and Data Science Seminar - Spring 2019

Usually meets Wednesday at 4:00 pm in SEO 636.
DateSpeakerTitle
January 16
January 23
January 30
February 6
February 13
February 20
(4:00 pm; 636 SEO)
Subhashis Ghoshal
North Carolina State University
Posterior Contraction and Credible Sets for Filaments of Regression Functions
February 27
(4:00 pm; 636 SEO)
William Li
Shanghai Advanced Institute of Finance
Using Prior Information for Intelligent Factor Allocation and Design Selection
March 6
(4:00 pm; 636 SEO)
Yue Niu
University of Arizona
A Super Scalable Algorithm for Short Segment Detection
March 13
(4:00 pm; 636 SEO)
Hani Aldirawi
University of Illinois at Chicago
Identifying Appropriate Probabilistic Models for Sparse Discrete Data
March 20
(4:00 pm; 636 SEO)
Mengjia Yu
UIUC
Finite sample change point inference and identification for high-dimensional location-shift
March 27
No seminar
Spring Break
April 3
(4:00 pm; 636 SEO)
Marco Ferreira
Virginia Tech
Dynamic Multiscale Spatiotemporal Models for Multivariate Gaussian Data
April 10
(4:00 pm; 636 SEO)
Jane Qian
Abbvie
Overview of ICH E9(R1): Estimands and Sensitivity Analysis in Clinical Trials
April 17
(3:00 pm; 636 SEO)
Junhui Wang
City University of Hong Kong
A smooth collaborative recommender system
April 17
(4:00 pm; 636 SEO)
Yehua Li
University of California at Riverside
Spatially Dependent Functional Data: Covariance Estimation, Principal Component Analysis, and Kriging
April 24
(4:00 pm; 636 SEO)
Wenhui Sheng
Marquette University
Sufficient dimension folding via distance covariance
May 1
(4:00 pm; 636 SEO)
Solomon Harrar
University of Kentucky
Weak Dependence Conditions for High-Dimensional Inference: Applications to Group Comparisons
May 13
(3:00 pm; 636 SEO)
Abhyuday Mandal
University of Georgia
EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments with Both Quantitative and Qualitative Factors
May 13
(4:00 pm; 636 SEO)
William Li
Shanghai Advanced Institute of Finance
A Class of New Particle Swarm Optimization Algorithms and Applications to Model Discriminating Designs
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