Stat has just published a Special Issue from the 2021 Symposium on Data Science and Statistics (SDSS). The Symposium on Data Science and Statistics is an annual conference sponsored by the American Statistical Association (ASA). The SDSS conference builds on the foundation established by the Symposium on the Interface of Computing Science and Statistics, also known as “The Interface Conference,” which was started in 1967 as a partnership between the Southern California Chapter of the ASA and the Association for Computing Machinery (ACM). Past conferences showcased research in emerging areas, so it seemed a natural progression to include data science as a focus of the new conference. The first SDSS conference took place in 2018 at Reston, Virginia (2018 Symposium on Data Science and Statistics (amstat.org)).
The SDSS 2021 conference was held virtually on 2-4 June 2021. The theme of the 2021 symposium was Beyond Big Data: Shaping the Future. Talks at the SDSS were grouped into six themes: Computational Statistics, Data Visualization, Education, Machine Learning, Practice & Applications, and Software & Data Science Technologies. The conference program included plenary panel sessions on the 2020 Census, Equitable and Inclusive Data and Technology, and the Impacts of COVID-19. Contributed talks were accepted based on refereed abstract submissions with contributed speed e-poster sessions rounding out the schedule.
This SDSS collaboration with Stat began with the publication of a special issue following the 2020 symposium. The editors for the current symposium special issue are Hao Helen Zhang (Stat’s editor-in-chief), Brennan Bean, Donna LaLonde, and Wendy Martinez (SDSS 2021 Program Chair). Authors who presented in a refereed session and all plenary panelists were invited to contribute a paper to this special issue of Stat. Following the Stat submission rules and referee requirements, 10 of the 16 submitted papers were accepted for the issue. The papers (presented below) came from four of the themes (Computational Statistics, Education, Machine Learning, and Practice & Applications) and are summarized in the introduction to the SDSS 2021 special issue.
Computational Statistics
2022). Equity-weighted bootstrapping: Examples and analysis. Stat, 11( 1), e456. https://doi.org/10.1002/sta4.456
, , & (2022). Sparse Bayesian predictive modelling of tumour response using radiomic features. Stat, 11( 1), e450. https://doi.org/10.1002/sta4.450
, , , , , & (2022). Statistical learning for predicting density–matrix-based electron dynamics. Stat, 11( 1), e439. https://doi.org/10.1002/sta4.439
, , , & (2022). K-fold cross-validation for complex sample surveys. Stat, 11( 1), e454. https://doi.org/10.1002/sta4.454 (Open Access article)
, , & (Education
2022). Content and computing outline of two undergraduate Bayesian courses: Tools, examples, and recommendations. Stat, 11( 1), e452. https://doi.org/10.1002/sta4.452 (Open Access article)
, & (2022). Becoming a JEDI statistician. Stat, 11( 1), e448. https://doi.org/10.1002/sta4.448
(Machine Learning
2022). Causal effect random forest of interaction trees for learning individualized treatment regimes with multiple treatments in observational studies. Stat, 11( 1), e457. https://doi.org/10.1002/sta4.457
, , & (2022). Improving image classification robustness using self-supervision. Stat, 11( 1), e455. https://doi.org/10.1002/sta4.455 (Open Access article)
, , & (Practice & Applications
2022). Dissecting the 2015 Chinese stock market crash. Stat, 11( 1), e460. https://doi.org/10.1002/sta4.460
, & (2022). Changing presidential approval: Detecting and understanding change points in interval censored polling data. Stat, 11( 1), e463. https://doi.org/10.1002/sta4.463 (Open Access article)
, & (