Collaborative Data Science

The Collaborative Data Science initiative at NISS brings together experts from various fields to tackle complex data challenges through interdisciplinary teamwork and innovative methodologies.

The goal is to foster progress in:

  1. Developing new ideas for experimental and observational data-driven learning and discovery that address key questions at the cutting edge of science and scientific deduction;
  2. Quantifying and summarizing uncertainty in data-driven theories, as well as complex Data Science models, algorithms, and workflows; and
  3. Establishing new practices for scientific reproducibility and replicability through Data Science.

This initiative promotes the inherently interdisciplinary nature of Data Science, seeking science-led advancements in Data Science and their innovative, significant, or transformative applications in science. It encourages robust collaboration and integration across the broadly defined realms of Science and Data Science via: (i) deep domain, (ii) broader inter-domain, and (iii) cross-domain collaborative research. It advocates for collective scientific advancement through novel collaborative and scientific methods and theories that can enrich the knowledge and strengthen the data practices among domain and data scientists.

Data Science in Science | Taylor & Francis Online is an open access, international journal publishing original research and reviews at the intersection of Science and Data Science. NISS Director David S. Matteson is one of the Editors-in-Cheif. An additional opportunity of becoming part of a NISS collaboration will be to publish a paper on the reasearch done through this initiative in this journal.

 

Collaborative Data Science Leadership Committee Members

Emily Casleton
Statistical Sciences Group
Los Alamos National Laboratory

Qingzhao Yu
Association Dean for Research at the School of Public Health, Louisiana State University Health, New Orleans

 

Don Estep
Director, Canadian Statistical Sciences Institute (CANSSI)
Canada Research Chair (Tier 1), Department of Statistics and Actuarial Science
Simon Fraser University

Xiao-Li Meng
Whipple V. N. Jones Professor of Statistics
Harvard University

Saman Muthukumarana, Ph.D.
Director, Data Science Nexus
Professor & Head of Department of Statistics at
University of Manitoba

Sahar Zengeneh
RTI International & University of Washington School of Public Health

 

Jiguo Cao
Canada Research Chair in Data Science, Professor
Simon Fraser University

Jane Pinelis
Chief AI Engineer, AI Assurance expert
Johns Hopkins University Applied Physics Laboratory

Jiguo Cao, PhD
Canada Research Chair in Data Science
Professor, Department of Statistics and Actuarial Science
Simon Fraser University

 

 

 

 

David S. Matteson
Director, NISS
Professor, Department of Statistics
Cornell University

Nancy McMillan
 Data Science Research Leader
Health Research & Analytics Business Line
Battelle

Snigdhansu (Ansu) Chatterjee
School of Statistics,
University of Minnesota 

 

Joel Dubin
Professor, Statistics and Actuarial Science
​Health Data Science Lab (HDSL) Lead
University of Waterloo

 

 

 

 

About the NISS CoLab

The National Institute of Statistical Sciences (NISS), an independent non-profit research organization founded in 1990 by the ASA, IBS, IMS, the triangle universities, and others, is excited to announce the launch of the NISS Collaboratory (CoLab). This new initiative will host collaborative events and activities, bringing together NISS Affiliates and partner institutions to work on high-impact cross-disciplinary and cross-sector research. NISS identifies, seeds, catalyzes, and fosters such research in the statistical and data sciences, serving as a neutral, objective expert in delivering critical scientific and public policy research to academia, industry, and government.

CoLab aims to strengthen these efforts by enhancing collaboration and innovation across diverse fields:

Full details are available on the NISS CoLab page: https://www.niss.org/CoLab