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

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

Registration and Fees

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.

Speakers

Tentative Conference Program

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

Time Description Speaker Slides Videos
9:00-9:10 Opening
9:10-10:00 Plenary Talk – TBD Cynthia Rudin, Duke University
10:00-10:10 Break
10:10-11:25 Parallel Sessions
  Statistical Learning Org: Patrick Groenen, Erasmus University
Chun-houh Chen, Academia Sinica
Mikhail Zehlonkin, Erasmus University
  Statistical Learning Org:  Jason Xu, Duke University
Tianxi Li, University of Virginia
Aaron Molstad, University of Florida
  Reproducible Computing and Reporting Org:  Jim Harner, West Virginia University

Dirk Eddelbuettel, U of Illinois at Urbana-Champaign
Containers as a Key Data Science Technology: Examples from the Rocker Project

Brian Rowe, Pez.AI
Works for Me: How Software Process Automation Transforms Repeatability into Reproducibility

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:  Adalbart Wilhelm, Jacobs University
Heike Hofmann, Iowa State University
Susan Vanderplas, University of Nebraska-Lincoln
  Statistical Learning Org.:  Peter Filzmoser, TU Wien
Sugnet Lubbe, University of Stellenbosch
Dorit Hammerling, Colorado School of Mines
Matey Neykov, Carnegie Mellon University
12:50 Adjourn

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

Time Description Speaker Slides Videos
9:00-10:15 Parallel Sessions
  Statistical Learning Org:  Kohei Adachi, Osaka University
Inge Koch, University of Western Australia
Giuseppe Vinci, Rice University
10:15-10:25 Break
10:25-11:15 Plenary Talk – TBD David Dunson, Duke University
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
Yining Chen, London School of Economics
Tengyao Wang, University College London
  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, Atrium Health & Phil Turk, Atrium Health
Using R Shiny for In-Hospital Resource Forecasting During the COVID-19 Outbreak

12:40 Adjourn

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

Time Description Speaker Slides Videos
9:00-9:50 Plenary Talk – TBD Robert Gramacy, Virginia Polytechnic
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
Eun-Kyung Lee, Ewha Womans University
Mu Zhu, University of Waterloo
  TBD Org: _______________________
11:15-11:25 Break
11:25-12:15 Plenary Talk – TBD Ming Yuan, Columbia University
12:15-12:25 Closing