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

By registering for this conference you (1) consent to the use of your personal information for the purpose of processing this registration, (2) agree that the conference may include your name, affiliation, and country of residence on the list of attendees, and (3) agree that the organizers may use that information to contact you with updates about this conference and future events.

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

Description:

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.

Time	Description	Speaker
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
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
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

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

Description:

Speakers

Speaker Titles/Abstracts

Posters

Conference Program

Scientific Program Committee

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