Toyon Statistical Lab

The Toyon Statistical Lab (TSL) is a community of student and faculty researchers in the Statistics and Data Science program within the Department of Mathematics at the University of Arizona under the supervision of Henry Scharf. TSL moved from its previous home at San Diego State University in 2023. TSL researchers develop and evaluate novel statistical methods for applications in a large and growing number of scientific fields.

[10/2024]: Ian Stewart and Josephine Ward join the lab as undergraduate and graduate researchers, respectively, in the Department of Mathematics at the University of Arizona. Welcome!

[09/2024]: Anisha Jean Mathias joins the lab as a postdoctoral scholar in the Department of Mathematics at the University of Arizona. Welcome!

[07/09/2024]: New paper titled Predicting species-level vegetation cover using large satellite imagery data sets co-authored by Jonathan Schierbaum published in the Journal of Agricultural, Biological and Environmental Statistics!

[07/27/2023]: Hugo Rosales successfully defended his Masters Thesis and will graduate with a Masters of Science in Statistics degree from the Department of Mathematics & Statistics at San Diego State University. Congratulations!

[05/09/2023]: Angelica Rivera received the Academic Excellence in Statistics award from the Department of Mathematics & Statistics at San Diego State University. Congratulations!

[01/16/2023]: New version of anipaths (0.10.2) with improved default settings for path interpolation and improved uncertainty visualization, co-authored by Hugo Rosales-Portillo and Angelica Rivera, now available on CRAN.

[10/25/2022]: Elena Dubocanin will present her work on the efficacy of software for the implementation of Bayesian hierarchical models at the Electronic Undergraduate Statistics Research Conference (eUSR) on November 4, 2022.

[06/07/2022]: Navid Nezamabadi successfully defended his Masters Thesis and will soon graduate with a Masters of Science in Statistics degree from the Department of Mathematics & Statistics at San Diego State University. Congratulations!

[05/12/2022]: Jonathan Schierbaum received the Academic Excellence in Statistics award from the Department of Mathematics & Statistics at San Diego State University. Congratulations!

[04/27/2022]: Jonathan Schierbaum successfully defended his Masters Thesis and will soon graduate with a Masters of Science in Statistics degree from the Department of Mathematics & Statistics at San Diego State University. Congratulations!

[09/10/2021]: Zaineb Boulil presented at the BAyesian Young Statisiticans Meeting and won Best Poster in Applications and Computation. Congratulations!

[05/25/2021]: Jennifer Betancourt, Zaineb Boulil, & Kristine Dinh graduate with Masters of Science in Statistics degrees from the Department of Mathematics & Statistics at San Diego State University. Congratulations to the inaugural TSL alumni cohort!

[05/17/2021]: New version of anipaths (0.10.1) with support for visualizing uncertainty in path interpolation, co-authored by Kristine Dinh and Henry Scharf now available on CRAN.

[05/14/2021]: Zaineb Boulil received the Academic Excellence in Biostatistics award from the Department of Mathematics & Statistics at San Diego State University. Congratulations!

Anisha Jean Mathias is a postdoctoral researcher in the Department of Mathematics at the University of Arizona. Her expertise during her Ph.D. was in the field of graph theory, particularly in studying domination in signed graphs. Now, her primary research focuses on applying Bayesian statistics and other modeling techniques to analyze animal movements in interconnected networks and ecological systems.

Ian Stewart is a senior undergraduate researcher majoring in Statistics and Data Science at the University of Arizona (expected graduation May 2025). He is working on predictions and inference related to the behaviors of Moor Macaques (project SEED) using multinomial logistic regression with mixed effects.

Alexander Berliner is a PhD researcher in Statistics and Data Science at the University of Arizona (expected graduation May 2027). His research in the Toyon Statistical Lab focuses on recursive Bayesian inference.

Henry Scharf is an assistant professor in the Department of Mathematics at the University of Arizona. His primary research interests are in theoretical and methodological contributions to the study of stochastic processes and networks, especially for applications in ecology.

Toyon (Heteromeles arbutifolia) is a central member of the Coastal sage scrub habitat, which includes much of San Diego County. It is a robust, wide ranging, drought resistant plant that resprouts after wildfire. It is ubiquitous in coastal sage scrub communities, though not abundant in numbers, and endemic to California and Baja California.

Angelica Rivera is a graduate of the Masters in Statistics program at San Diego State University with a biostatistics concentration (2023). Her work in the Toyon Statistical Lab focused on implementing new features and statistical models for the anipaths R package. She is now a Biostatistician with Emanate Biostats.	Hugo Rosales is a graduate of the Masters in Statistics program at San Diego State University (2023). His work in the Toyon Statistical Lab included creating different visualizations for uncertainty in animal movement utilizing GAM-based interpolation of telemtry data, and developing new methods for the analysis of movement data from multiple interacting individuals.
Elena Dubocanin is a graduate of the BS in Statistics program at SDSU (2022). While in the Toyon Statistics Lab, she worked on implementing Bayesian hierarchical models for vegetation distributions in New Mexico.	Jonathan Schierbaum is a graduate of the Masters in Statistics program at San Diego State University (2022). His work in the Toyon Statistical Lab was focused on understanding and predicting wildfire-driven forest conversion; a topic which also served as the foundation for his thesis. Jonathan's research interests include Bayesian statistics, spatiotemporal analysis, and data visualization. He now works as a Data Scientist for the General Services Administration's Office of Human Resource Management.	Navid Nezamabadi is a graduate of the Masters in Statistics program at San Diego State University (2022). His research in the TSL was centered around Hierarchical Bayesian Models for animal movement. He is now a PhD student in the Department of Statistics at George Washington University.
Kristine Dinh is a graduate of the Masters in Statistics program at San Diego State University (2021). While in the Toyon Statistical Lab, Kristine developed new R functions to extend the functionality of the anipaths package. The new features utilize the the crawl package to depict path uncertainty using multiple realizations for a single individual. Kristine is also working as a statistical modeler at Arrowhead Insurance Agency and an aspiring data scientist.	Zaineb Boulil is a graduate of the Masters in Statistics program at San Diego State University (2021). Her program of study was concentrated in biostatistics, which fostered her interest in spatiotemporal analyses with an emphasis in public health. Her time in the Toyon Statistical Lab consisted of analyzing the effect of sonar on dolphin social structure while co-developing a methodology accommodating unlabeled movement data (Boulil et al. 2022). She currently works in the Department of Pediatric Research at Rady Children’s Hospital, San Diego.	Jennifer Betancourt is a graduate of the Masters in Statistics program at San Diego State University (2021). During her time with the Toyon Statistical Lab she created an R package that simulates animal movement through state space models using NIMBLE. Her research interests include Bayesian statistics and spatiotemporal analysis.

Scharf, H. R., J. Schierbaum^*, H. Matsumoto, T. Assal (2024). Predicting species-level vegetation cover using large satellite imagery data sets. Journal of Agricultural, Biological and Environmental Statistics.

Boulil, Z. L.^*, J. W. Durban, H. Fearnbach, T. W. Joyce, S. G. M. Leander, H. R. Scharf (2022). Detecting changes in dynamic social networks using multiply-labeled movement data. Journal of Agricultural, Biological, and Environmental Statistics, 28: 243–259.

Scharf, H. R. (2021). Statistical analysis of animal movement: Understanding behavior through hierarchical parametric models. Notices of the American Mathematical Society.

Scharf, H. R., F. Buderman (2020). Animal movement models for multiple individuals. Wiley Interdisciplinary Reviews: Computational Statistics, e1506.

Hooten, M. B., H. R. Scharf, T. J. Hefley, A. T. Pearse, M. D. Weegman (2018). Animal movement models for migratory individuals and groups. Methods in Ecology and Evolution, 9, 1692–1705.

Scharf, H. R., M. B. Hooten, D. S. Johnson, J. W. Durban (2018). Process convolution approaches for modeling interacting trajectories. Environmetrics, e2487.

Scharf, H. R., M. B. Hooten, B. K. Fosdick, D. S. Johnson, J. M. London, and J. W. Durban. (2016). Dynamic social networks based on movement. Annals of Applied Statistics, 10(4), 2182–2202.

Scharf, H. R., K. Dinh, H. Rosales-Portillo, A. Rivera (2023). anipaths: Animation of observed trajectories using spline- or state-space model-based interpolation. R package version 0.10.2.

Movement of polar bears in the Chukchi and Beaufort seas.

Scharf, H. R., M. B. Hooten, R. R. Wilson, G. M. Durner, T. C. Atwood (2019). Accounting for phenology in the analysis of animal movement. Biometrics, 75: 810–820.

Movement of killer whales near the Antarctic peninsula.

Scharf, H. R., M. B. Hooten, B. K. Fosdick, D. S. Johnson, J. M. London, and J. W. Durban. (2016). Dynamic social networks based on movement. Annals of Applied Statistics. 10(4), 2182--2202.

Movement of Greenland White Fronted Geese on the east coast of Ireland.

Hooten, M. B., H. R. Scharf, T. J. Hefley, A. T. Pearse, M. D. Weegman (2018). Animal movement models for migratory individuals and groups. Methods in Ecology and Evolution. 9, 1692?1705.

If you would like to work with us, please send Henry an email with a brief description of your research interests. We'll set up a meeting to chat.