Auburn University

Statistics and Data Science Seminar


The Statistics & Data Science Seminar is hosted by the Department of Mathematics and Statistics and provides a weekly platform for academics and researchers from different domains to present and discuss problems and solutions regarding data collection, management and analysis.


  

Fall 2021 Seminar

Welcome to the Fall 2021 Seminar series! The seminar will take place every Thursday at 2pm CDT over Zoom (https://auburn.zoom.us/j/82501343299). For any questions or requests, please contact Jingyi (Ginny) Zheng. The list of speakers for this series can be found in the table below which is followed by information of the title and abstract of each seminar talk.

 

Speaker Institution Date
Yao Xie
Georgia Institute of Technology Aug. 19
Chenang Liu
Oklahoma State University Aug. 26
Xiongtao Dai
Iowa State University Sep. 2
Xiaowei Yue   Virginia Tech Sep. 9
Fushing Hsieh UC Davis Sep. 16
Yanzhao Cao Auburn University Sep. 23
Gaetan Bakalli
Auburn University Sep. 30
Stephane Guerrier University of Geneva Oct. 14
Yao Li UNC at Chapel Hill Oct. 21
Da Yan University of Alabama at Birmingham Oct. 28
Paromita Dubey USC Marshall Business School Nov. 4
Xuan Cao University of Cincinnati Nov. 11
Lei Li FDA Nov. 18
Luca Insolia Sant'Anna School of Advanced Studies Dec. 2

 

Speaker: Yao Xie, Georgia Institute of Technology

Title: Statistical Inference for Spatio-Temporal Point Processes

Abstract Discrete events are a sequence of observations consisting of event time, location, and possibly "marks" with additional event information. Such event data is ubiquitous in modern applications, such as social networks, seismic activities, police reports data, neuronal spike trains, and disease spread counts. We are particularly interested in capturing the complex dependence of the discrete events data, particularly estimating how nodes interact with each other, such as the triggering or inhibiting effects of the historical events on future events. This helps us recovery network topology, perform causal inference, understand spatio-temporal dynamics, and make predictions. Motivated by popular Hawkes processes, we introduce a new general modeling approach for capturing spatio-temporal interaction, which enjoys computationally efficient model estimation procedures. We establish statistical guarantees by connecting to a modern convex optimization theory of solving variational inequality. The good performance of the proposed method is illustrated using several real-world data sets.

 

Speaker: Chenang Liu, Oklahoma State University

Title: Data-Driven Process Monitoring and Blockchain-Enabled Security Protection for Smart Manufacturing

Abstract With the wide application of the Internet of things (IoT) and information technologies, the environment of manufacturing become data-rich and cyber-enabled, which enables the rapid development of smart manufacturing. However, how to achieve accurate process monitoring and effective cyber-security protection still remains challenging. To address these two critical issues, this research aims to develop methodologies that can detect the process anomalies accurately and also prevent the potential cyber-physical attacks effectively. Toward this objective, a generative adversarial networks (GAN) based approach is proposed for online process anomaly detection when data are highly imbalanced. Then a blockchain-enabled method is also developed to effectively prevent the common cyber-physical attacks in manufacturing. Furthermore, real-world case studies based on additive manufacturing was also conducted to demonstrate the effective and potential of the proposed methods.

 

Speaker: Xiongtao Dai, Iowa State University

Title: Exploratory Data Analysis for Data Objects on a Metric Space via Tukey's Depth

Abstract Exploratory data analysis involves looking at the data and understanding what can be done with them. Non-standard data objects such as directions, covariance matrices, trees, functions, and images have become increasingly common in modern practice. Such complex data objects are hard to examine due to the lack of a nature ordering and efficient visualization tools. We develop a novel exploratory tool for data objects lying on a metric space based on data depth, extending the celebrated Tukey's depth for Euclidean data. The proposed metric halfspace depth assigns depth values to data points, characterizing the centrality and outlyingness of these points. This also leads to an interpretable center-outward ranking, which can be used to construct rank tests. I will demonstrate two applications, one to reveal differential brain connectivity patterns in an Alzheimer's disease study, and another to infer the phylogenetic history and outlying phylogenies in 7 pathogenic parasites.

 

Speaker: Xiaowei Yue, Virgina Tech

Title: Title goes here

Abstract Abstract goes here.

 

Speaker: Fushing Hsieh, UC Davis

Title: The geometry of colors in van Gogh's Sunflowers

Abstract "Paintings fade like flowers": van Gogh's prediction on the impact of age on paintings came true for most of his paintings. We have studied the consequences of this aging on the Sunflowers in a vase with a yellow background series, namely its original, F454, currently in London, and two replicates, F457, in Tokyo, and F458, in Amsterdam, which van Gogh painted using the original as a model. The background and flower renditions in those paintings have faded and turned brown, making them less vibrant that van Gogh had most likely intended. We have attempted to restore van Gogh's intent using a computational approach based on data science. After identifications of regions of interest (ROI) within the three paintings F454, F457, and F458 that capture the flowers, stems of the flowers, and background, respectively, we studied the geometry of the color space (in RGB representation) occupied by those ROIs. By comparing those color spaces with those occupied by similar ROIs in photographs of real sunflowers, we identified shifts in all three-color coordinates, R, G, and B, with the positive shift in the blue coordinate being the more salient. We have proposed two algorithms, PCR-1 and PCR-2, for correcting that shift in blue and generate representations of the paintings that aim to restore their original conditions. The reduction of the blue component in the yellow hues has led to more vibrant and less brownish digital rendition of the three Sunflowers in a vase with a yellow background.

 

Speaker: Yanzhao Cao, Auburn University

Title: Title goes here

Abstract Abstract goes here.

 

Speaker: Gaetan Bakalli, Auburn University

Title: Title goes here

Abstract Abstract goes here.

 

Speaker: Stephane Guerrier, University of Geneva

Title: Title goes here

Abstract Abstract goes here.

 

Speaker: Yao Li, UNC at Chapel Hill

Title: Title goes here

Abstract Abstract goes here.

 

Speaker: Da Yan, University of Alabama at Birmingham

Title: Title goes here

Abstract Abstract goes here.

 

Speaker: Paromita Dubey, USC Marshall Business School

Title: Title goes here

Abstract Abstract goes here.

 

Speaker: Xuan Cao, University of Cincinnati

Title: Title goes here

Abstract Abstract goes here.

 

Speaker: Lei Li, FDA

Title: Title goes here

Abstract Abstract goes here.

 

Speaker: Luca Insolia, Sant'Anna School of Advanced Studies

Title: Title goes here

Abstract Abstract goes here.