Most JSM technical sessions are organized six to 12 months before the meeting. This smooths the organizing process but does not allow coverage of breaking developments of keen public interest in which statistical issues are highly relevant. To fill this gap, space is reserved for late-breaking sessions at JSM each year.
This year, members of the Committee on Meetings chose a panel session reporting on the NSF workshop, “Statistics at a Crossroads: Challenges and Opportunities in the Data Science Era.”
Late Breaking Session #1
Monday, July 29, 2:00 p.m. – 3:50 p.m.
Statistics at a Crossroads: Who Is for the Challenge?
Organized by Xuming He, University of Michigan
Sponsored by the National Science Foundation, about 50 members of the statistics and data science community participated in the workshop “Statistics at a Crossroads: Challenges and Opportunities in the Data Science Era” in October 2018. The workshop brought together leading researchers and educators to develop a 10–20-year vision for the statistics field, taking advantage of the unprecedented opportunities and challenges in the era of data science. Two pre-workshop webinars included additional participation by hundreds of online participants. The primary goal of the webinars and workshop was to seek broad community input in the following:
- Identifying emerging research topics that will require new statistical foundations, methodology, and computational thinking
- Engaging with important data-driven challenges in different application domains and fostering interdisciplinary collaborations to address important scientific challenges
- Creating a vibrant research community and maintaining an appropriate balance between different sub-fields of statistics, including investments in the foundations
This exercise is important for the statistics community, because we all understand the field of statistics is at a crossroads. We either flourish by embracing and leading data science or we decline and become irrelevant. In the long run, we must redefine, broaden, and transform statistics to thrive. We must evolve and grow to be the transdisciplinary science that collects and extracts useful information from data.
With the fast establishment of various data science entities across campuses, industry, and government, there is a limited window of opportunity for a successful transformation. We must not miss this window. We must effect this change now by reimagining our educational programs, rethinking faculty hiring and promotion, and accelerating the required cultural change. We must identify and embrace big research challenges so our work will have greater impact on science and society in the coming years.
Join the discussion during this panel session, which will include steering committee members who will present and discuss the major findings and recommendations from the workshop and seek further community input.
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