Data Science, Statistics & Visualization 2020 – July 29-31, 2020

Due to COVID-19 this conference will be presented virtually July 29-31, 2020.  

Registration is now closed

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Participants are expected to adhere to the ISI and Associations Individual Conduct Policy


Data Science, Statistics & Visualisation (2020) is a virtual conference aimed at bringing together researchers and practitioners interested in the interplay of statistics, computer science, and visualization, and to build bridges between these fields.  We shall create a forum to discuss recent progress and emerging ideas in these adjacent disciplines and encourage informal contacts and discussions among all the participants. The conference highlights contributions to practical applications, and in particular those which are linking and integrating these subject areas. Presentations will be oriented towards a very wide scientific audience and will cover topics such as machine learning, the visualization of data, big data infrastructures and analytics, interactive learning, advanced computing, and other important themes.

In order to encourage networking during this virtual conference, it will be possible to set up (virtual) meetings with other participants.


Speaker Titles/Abstracts


(Posters will be presented in 30 minute parallel sessions. Participants can virtually attend the sessions to discuss posters with the presenters.)

Conference Program

Wednesday, July 29, 2020
Virtual – U.S. New York/Eastern Daylight Time

Time Description Speaker Slides Videos
8:00-8:50 Test Audio/Visual Join the “Click Here to Test Audio/Video Connections” session by navigating to the “Agenda” tab in Whova. (Note: we will not be able to assist with audio/visual issues once the meeting has begun)
9:00-9:10 Opening David Banks, Duke University and SAMSI
9:10-10:00 Plenary Talk Chair: David Banks, Duke University and SAMSI

Cynthia Rudin, Duke University
Seeing into Data and Models

10:00-10:10 Break
10:10-11:25 Parallel Sessions
  Statistical Learning Org: Patrick Groenen, Erasmus University
Chun-houh Chen, Academia Sinica
Covariate-adjusted Heatmaps for Visualizing Biological Data via Correlation Decomposition

Patrick Groenen, Erasmus University
Interpretable Kernels for Explainable AI

Mikhail Zehlonkin, Erasmus University
Probabilistic Forecasting of Binary Outcomes in the Presence of Outliers

  Statistical Learning Org:  Jason Xu, Duke University
Jason Xu, Duke University
A Proximal Distance Algorithm for Likelihood-Based Sparse Covariance Estimation

Tianxi Li, University of Virginia
Linear Regression and its Inference on Noisy Network-linked Data

Aaron J. Molstad, University of Florida
Insights and Algorithms for the Multivariate Square-root Lass

  Reproducible Computing and Reporting Org:  Jim Harner, West Virginia University
Dirk Eddelbuettel, U of Illinois at Urbana-Champaign
Reliable Reproducible Research via Containers from the Rocker Project

Brian Lee Yung Rowe, Pez.AI
Achieving Practical Reproducibility with Transparency and Accessibility

Jim Harner, West Virginia University; Chris Grant, Rc2ai; Mark Lilback, Rc2ai
Reproducible Computing and Reporting in a Complex Software Environment

11:25-11:35 Break
11:35-12:50 Parallel Sessions
  Visualisation Org:  Adalbert Wilhelm, Jacobs University
Adalbert Wilhelm, Jacobs University
Visual Story Telling of Covid-19: A Case Study

Xiaoyue “Zoe” Cheng, University of Nebraska
Visually Exploring Age-based Population Data over Time

Heike Hofmann, Iowa State University
Visualizing Elections in the U.S.

Susan Vanderplas, University of Nebraska-Lincoln
Perception and Visual Communication in a Global Pandemic

  Statistical Learning Org.:  Peter Filzmoser, TU Wien
Sugnet Lubbe, University of Stellenbosch
Comparison of Zero Replacement Strategies for Compositional Data with Large Numbers of Zeros

Dorit Hammerling, Colorado School of Mines
Contained Chaos: Ensemble Consistency Testing for the Community Earth System Model

Matey Neykov, Carnegie Mellon University
High-Temperature Structure Detection in Ferromagnets

  Data Science Org.:  Ruda Zhang, SAMSI
Ruda Zhang, SAMSI
Normal-bundle Bootstrap

Deborshee Sen, SAMSI
Bayesian Neural Networks and Dimensionality Reduction

Jason Poulos, SAMSI
Retrospective Causal Prediction via Elapsed-Time and Propensity-Weighted Matrix Completion, with an Evaluation of the Effect of European Integration on Labour Market Outcomes

12:50 Adjourn

Thursday, July 30, 2020
Virtual – U.S. New York/Eastern Daylight Time

Time Description Speaker Slides Videos
8:00-8:50 Test Audio/Visual Join the “Click Here to Test Audio/Video Connections” session by navigating to the “Agenda” tab in Whova. (Note: we will not be able to assist with audio/visual issues once the meeting has begun)
9:00-10:15 Parallel Sessions
  Statistical Learning Org:  Kohei Adachi, Osaka University
Kohei Adachi, Osaka University, Japan
Principal Component versus Factor Analyses with their Intermediate Procedure in Matrix Decomposition Formulation

Inge Koch, University of Western Australia
Principal Components for High-Dimensional and Directional Data

Giuseppe Vinci, Rice University
Graph Quilting: Graphical Model Selection from Partially Observed Covariances

Data Science Org:  John Nardini, SAMSI
John Nardini, SAMSI
Learning Differential Equation Models for Noisy Biological Data

Glen Wright Colopy. Cenduit
Personalized Inference Protects Patients and Science

Xinyi Li, SAMSI
Sparse Learning and Structure Identification for Ultra-High-Dimensional Image-on-Scalar Regression

10:15-10:25 Break
10:25-11:15 Plenary Talk Chair: Patrick Groenen, Erasmus University

David Dunson, Duke University
Generalized Bayes for Probabilistic Uncertainty Quantification in Unsupervised Learning

11:15-11:25 Break
11:25-12:40 Parallel Sessions
  Statistical Computing Org:  Richard Samworth, University of Cambridge
Hao Chen, University of California, Davis
Change-point Analysis for Modern Data

Yining Chen, London School of Economics
Jump or Kink: Super-efficiency in Segmented Linear Regression Break-point Estimation

Tengyao Wang, University College London
High-Dimensional, Multiscale Online Changepoint Detection

  Data Science Technology Org: Jim Harner, West Virginia University
Javier Luraschi, RStudio
Training ImageNet Using TensorFow and R

Soren Harner, LayerJot & Jim Harner, West Virginia University
Harnessing Big Data and Machine Learning with Arrow Data Frames in R and Python

Shih-Hsiung Chou & Phil Turk, Atrium Health
CURVE: a Web Application for In-Hospital Resource Forecasting During the COVID-19 Outbreak

  New Ideas for Old Problems Org: Deborshee Sen, SAMSI
Pulong Ma, SAMSI
Multifidelity Computer Model Emulation with High-Dimensional Output: An Application to Storm Surge

Kate Moore, Wake Forest University
Communities in Data

Wenjia Wang, SAMSI
Uncertainty Quantification for Bayesian Optimization

12:40 Poster Session
1:10 Adjourn

Friday, July 31, 2020
Virtual – U.S. New York/Eastern Daylight Time

Time Description Speaker Slides Videos
8:00-8:50 Test Audio/Visual Join the “Click Here to Test Audio/Video Connections” session by navigating to the “Agenda” tab in Whova. (Note: we will not be able to assist with audio/visual issues once the meeting has begun)
9:00-9:50 Plenary Talk Chair: Peter Filzmoser, TU Wien

Robert Gramacy, Virginia Polytechnic
Replication or Exploration? Sequential Design for Stochastic Simulation Experiments

9:50-10:00 Break
10:00-11:15 Parallel Sessions
  JDSSV Orgs: Patrick Groenen, Erasmus University & Stefan Van Aelst, KU Leuven
Andreas Alfons, Erasmus University
Cellwise and Rowwise Robust Regression with Compositional Covariates

Eun-Kyung Lee, Ewha Woman’s University
Tree-structured Models using Projection Pursuit Method and their Explanation

Mu Zhu, University of Waterloo
Some Statistical Applications of Generative Neural Networks

  SAS Orgs:  Brett Wujek, SAS Institute
Xan Gregg, SAS Institute
Understanding Smoothers through Interactive Examples

Kelci Miclaus, JMP Lifesciences
The Role of Visualization in Translational and Clinical Research

Guohui Wu, SAS Institute
Location matters: Estimating Spatial Regression Models with Large Spatial Weights Matrices using SAS Econometrics

11:15-11:25 Break
11:25-12:15 Plenary Talk Chair: David Banks, Duke University and SAMSI

Ming Yuan, Columbia University
Information Based Complexity of High Dimensional Sparse Functions

12:15-12:25 Closing