@article{bonas_datta_wikle_boone_alamri_hari_kavila_simmons_jarvis_burr_et al._2024, title={Assessing predictability of environmental time series with statistical and machine learning models}, ISSN={["1099-095X"]}, DOI={10.1002/env.2864}, journal={ENVIRONMETRICS}, author={Bonas, Matthew and Datta, Abhirup and Wikle, Christopher K. and Boone, Edward L. and Alamri, Faten S. and Hari, Bhava Vyasa and Kavila, Indulekha and Simmons, Susan J. and Jarvis, Shannon M. and Burr, Wesley S. and et al.}, year={2024}, month={Jul} }
 @article{meis_pirani_euan_castruccio_simmons_stroud_blangiardo_wikle_wheeler_naumova_et al._2024, title={Catalysing virtual collaboration: The experience of the remote TIES working groups}, ISSN={["1099-095X"]}, DOI={10.1002/env.2855}, abstractNote={Abstract During the COVID‐19 pandemic, the idea of collaboration and scientific exchange between members of the scientific community was enhanced by technology. Virtual meetings and work platforms have become common resources to continue generating research, partially replacing instances of joint in‐person work before, during or after a conference. The idea of teleworking played a fundamental role in remote collaboration groups within The International Statistical Society (TIES), a community of interdisciplinary scientists such as statisticians, mathematicians, meteorologists, and biologists, among others working on quantitative methods to enhance solutions to environmental problems. In 2021 the Society launched three working groups with the aim of improving networking across the Society's members and develop creative collaboration, while advancing statistical and computational methods motivated by real‐world driven applications in environmental research. Here, we provide insights from this virtual collaborative initiative.}, journal={ENVIRONMETRICS}, author={Meis, M. and Pirani, M. and Euan, C. and Castruccio, S. and Simmons, S. and Stroud, J. R. and Blangiardo, M. and Wikle, C. K. and Wheeler, M. and Naumova, E. and et al.}, year={2024}, month={May} }
 @article{wikle_datta_hari_boone_sahoo_kavila_castruccio_simmons_burr_chang_2022, title={An illustration of model agnostic explainability methods applied to environmental data}, ISSN={["1099-095X"]}, DOI={10.1002/env.2772}, abstractNote={Abstract}, journal={ENVIRONMETRICS}, author={Wikle, Christopher K. and Datta, Abhirup and Hari, Bhava Vyasa and Boone, Edward L. and Sahoo, Indranil and Kavila, Indulekha and Castruccio, Stefano and Simmons, Susan J. and Burr, Wesley S. and Chang, Won}, year={2022}, month={Oct} }
 @article{healey_simmons_manivannan_ro_2022, title={Visual Analytics for the Coronavirus COVID-19 Pandemic}, ISSN={["2167-647X"]}, DOI={10.1089/big.2021.0023}, abstractNote={The coronavirus disease COVID-19 was first reported in Wuhan, China, on December 31, 2019. The disease has since spread throughout the world, affecting 227.2 million individuals and resulting in 4,672,629 deaths as of September 9, 2021, according to the Johns Hopkins University Center for Systems Science and Engineering. Numerous sources track and report information on the disease, including Johns Hopkins itself, with its well-known Novel Coronavirus Dashboard. We were also interested in providing information on the pandemic. However, rather than duplicating existing resources, we focused on integrating sophisticated data analytics and visualization for region-to-region comparison, trend prediction, and testing and vaccination analysis. Our high-level goal is to provide visualizations of predictive analytics that offer policymakers and the general public insight into the current pandemic state and how it may progress into the future. Data are visualized using a web-based jQuery+Tableau dashboard.† The dashboard allows both novice viewers and domain experts to gain useful insights into COVID-19's current and predicted future state for different countries and regions of interest throughout the world.}, journal={BIG DATA}, author={Healey, Christopher G. and Simmons, Susan J. and Manivannan, Chandra and Ro, Yoonchul}, year={2022}, month={Jan} }
 @article{snider_hill_simmons_herstine_2018, title={A general framework for gathering data to quantify annual visitation}, volume={36}, number={1}, journal={Journal of Park and Recreation Administration}, author={Snider, A. G. and Hill, J. and Simmons, S. and Herstine, J.}, year={2018}, pages={1–21} }