jsm2025 is an R package with all the JSM talks. It makes personalized recommendations for talks (based on citation network data) and exports your schedule as an ical file that can be loaded into Google Calendar and similar.
Getting started
You can watch a video demo of the shiny app and this R package or read more below.
To install the package:
remotes::install_github("jacobbien/jsm2025-project", subdir = "jsm2025")An example of the sort of thing you can do with the package:
# which of my co-authors are at JSM?
my_coauthors <- jsm2025::get_coauthors("Jacob Bien")
# which of the people I cite the most are at JSM?
people_cited_by_me <- jsm2025::get_out_citations("Jacob Bien")
# which of the people who cite me the most are at JSM?
people_citing_me <- jsm2025::get_in_citations("Jacob Bien")
# when are their talks?
schedule <- jsm2025::get_talks(
speakers = c(my_coauthors, people_cited_by_me[1:12], people_citing_me[1:5]),
session_types = "Paper",
days = c("2025-08-03", "2025-08-04", "2025-08-05", "2025-08-06")
)This produces a data frame of talks:
dplyr::select(schedule, speaker, title)
#> # A tibble: 12 × 2
#> speaker title
#> <chr> <glue>
#> 1 Aaditya Ramdas [Discussion] 'Innovations in False Discovery Rate Control…
#> 2 Shuangge Ma Cancer Human Disease Networks (cHDNs) via Deep Learning S…
#> 3 Keshav Motwani Disentangling Genetic Contributions to Human Brain Connec…
#> 4 Aaditya Ramdas Conformal changepoint localization
#> 5 Florentina Bunea Softmax mixture ensembles for interpretable latent discov…
#> 6 Ryan Tibshirani Gradient Equilibrium in Online Learning
#> 7 Andee Kaplan An improved sampler for recursive Bayesian inference
#> 8 David Matteson Locally Adaptive Random Walk Stochastic Volatility
#> 9 Arkajyoti Saha Random forests for binary geospatial data
#> 10 Sumanta Basu Random Forests for Time Series Data
#> 11 Robert Tibshirani PresentationFF
#> 12 George Michailidis A VAE-based Framework for Learning Multi-Level Neural Gra…See ?get_talks for the other ways you can filter talks. Finally, you can export this in the ical format, which can be imported into Google Calendar and other standard calendars:
jsm2025::export_calendar_to_ics(schedule, file = "jsm-talks.ics")Once imported into Google Calendar, we get the following:
About
We wrangled decades of citation and coauthorship data from Semantic Scholar and arxiv to form these recommendations. Special thanks to Donna LaLonde and Ronald Wasserstein this year for providing JSM program information. We have also included functionality for exporting your schedule as an ical that can be loaded into Google Calendar or similar. The result is a package that streamlines the process of finding talks that you may want to attend. The first version of this was done as a web app for JSM 2019 with Ronak Upadhyaya and the second version, for JSM 2022, was done with Yibin Xiong. jsm2025 was written using literate programming. In particular, the entire package was generated by “litr-knitting” a single Rmd file. To learn more about the litr package and the literate programming approach to writing R packages, see here.
Citation and coauthor data acknowledgments
The citation data came from Semantic Scholar’s API and is associated with the following paper:
Waleed Ammar, Dirk Groeneveld, Chandra Bhagavatula, Iz Beltagy, Miles Crawford, Doug Downey, Jason Dunkelberger, Ahmed Elgohary, Sergey Feldman, Vu A. Ha, Rodney Michael Kinney, Sebastian Kohlmeier, Kyle Lo, Tyler C. Murray, Hsu-Han Ooi, Matthew E. Peters, Joanna L. Power, Sam Skjonsberg, Lucy Lu Wang, Christopher Wilhelm, Zheng Yuan, Madeleine van Zuylen, Oren Etzioni, Construction of the Literature Graph in Semantic Scholar. NAACL 2018.
Coauthorship data came also from arxiv metadata.