Upcoming Webinars

Changing Climate, Changing Data: A journey of statisticians and climate scientists

Date: Thursday, March 20, 2025 at 1-2pm ET

Speakers: Claudie Beaulieu, Assistant Professor of Ocean Sciences, University of California, Santa Cruz and Rebecca Killick, Professor of Statistics, School of Mathematical Sciences, Lancaster University; Moderator: Emily Casleton, Statistical Sciences Group, Los Alamos National Laboratory (LANL)

Astronomy & Cosmic Emulation

Date: Thursday, April 10, 2025 at 1-2pm ET

 

Speakers: Kelly Renee Moran, Applied Statistician at Los Alamos National Laboratory (LANL) and Katrin Heitmann, Argonne National Laboaratory (ANL); Moderator: Emily Casleton, Statistical Sciences Group, Los Alamos National Laboaratory (LANL)

 

 

 

AI for Health Data

Date: Thursday, May 8, 2025 at 1-2pm ET

 

Speakers: An-Chao Tsai, Department of Computer Science and Artificial Intelligence, National Pingtung University and Anand Paul, LSU Health-New Orleans; Moderator: Qingzhao Yu, Associate Dean for Research at the School of Public Health, Louisiana State University Health, New Orleans

 

 

 

Data Science Techniques for Control of Assistive Devices After Neurological Injury

About the NISS-CANSSI Collaborative Data Science Web Series:

The NISS-CANSSI Collaborative Data Science initiative that the National Institute of Statistical Sciences (NISS) in collaboration with the Canadian Statistical Sciences Institute (CANSSI) brings together experts from various fields to tackle complex data challenges through interdisciplinary teamwork and innovative methodologies.

Goals of the Initiative

The goal is to foster progress in:

• 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;
• Quantifying and summarizing uncertainty in data-driven theories, as well as complex Data Science models, algorithms, and workflows; and
• Establishing new practices for scientific reproducibility and replicability through Data Science.

Promoting Science-Led Advancements in 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 deep domain, broader inter-domain and 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.

The NISS-CANSSI Web Series on Collaborative Data Science

The NISS-CANSSI web series on Collaborative Data Science is dedicated to showcasing data scientists and domain scientists from diverse scientific fields who collaborate to advance science. This initiative celebrates the power of collaboration, demonstrating how the fusion of data science with various disciplines can drive innovation, solve complex problems, and push the frontiers of knowledge beyond the realm of statistics.

Engaging Virtual Seminars Featuring Experts Across Disciplines

Each virtual session will feature two speakers: a data scientist and a subject matter expert from another domain who have successfully partnered to achieve impactful results. Through their shared experiences and insights, attendees will gain a deeper understanding of the collaborative processes that bridge gaps between different scientific landscapes. These seminars will not only highlight successful partnerships but also provide a platform for exchanging ideas, methodologies, and best practices that inspire new collaborations.

Building a Community of Data-Driven Research Excellence

Our mission is to create a vibrant community where data-driven approaches enhance research across fields such as biology, environmental science, engineering, social sciences, and more. By showcasing real-world examples of interdisciplinary teamwork, we aim to empower scientists and data professionals alike to embark on joint ventures that harness the full potential of their combined expertise.

Publishing Opportunities in Data Science in Science

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-Chief. An additional opportunity of becoming part of a NISS collaboration will be to publish a paper on the research done through this initiative in this journal.

Join Us in Driving Scientific Innovation Through Collaboration

Join us in celebrating the transformative impact of collaboration. Whether you are a seasoned data scientist, a researcher from another discipline, or simply passionate about the possibilities that emerge when diverse minds converge, this seminar series offers invaluable opportunities to learn, connect, and innovate together!

About the NISS CoLab