stanconnect-sessions.qmd

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title: StanConnect Sessions
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StanConnect is a virtual miniseries which consists of 3-hour meetings/mini-symposia
where each meeting is like "session" of an organized conference.
StanConnect sessions provide an alternative format to large, annual or bi-annual in-person meetings.
They are designed to provide opportunities for all members of the community to learn from one another
and gain organizational expertise.

* Anyone can feel free to organize a StanConnect meeting as a “Session Chair”. Simply download the
[StanConnect proposal form](https://docs.google.com/document/d/1Vzeos037akyRwXaEhSw9L6HLxenoxdH57R3kuV7S-IA)
and submit to SGB via email [board@mc-stan.org](board@mc-stan.org)

* The talks must involve Stan and be focused around a subject/topic theme. E.g. “Spatial models in Ecology via Stan”.

* Session Chairs may invite speakers related to their theme and structure the 3-hr meeting as they see fit.
The proposal form provides a few examples of how sessions can be structured.



### Stan Through Space and Time (2022)

A half-day event showcasing the use of Stan in spatial statistics and for modeling time series data.

* Date: October 31, 2022
* GitHub repository of slides and materials: [https://github.com/stan-dev/connect22-space-time](https://github.com/stan-dev/connect22-space-time){target="_blank"}
* Program: 
   + *Time and tide wait for no one: spatio-temporal modelling in river networks* - Edgar Santos Fernandez
   + *Spatio-temporal modelling of mosquitoes vector and its environmental drivers in Hong Kong* - Stan Yip
   + *Robust non-Gaussian models and how to fit them in Stan* - Rafael Cabral
   + *A space-time extension of the Poisson auto-regression to model Covid-19 cases at the England local authorities level* - Pierfrancesco Alaimo Di Loro
   + *Fitting spatio-temporal geostatistical models in Stan using the bmstdr R package* - Sujit Sahu
   + *tipsae: Tools for Handling Indices and Proportions in Small Area Estimation* - Silvia De Nicolo
   + *geostan: An R package for Bayesian spatial analysis* - Connor Donegan
   + *Structure induced by a multiple membership transformation on the conditional autoregressive model* - Marco Gramatica
   + *A Bayesian hierarchical model for disease mapping that accounts for scaling and heavy-tailed latent effects* - Victoire Michal
   + *Bayesian latent spatial autoregressive growth modeling* - Zachary Roman
* [Abstracts](stancon/StanConnect2022_space_time.qmd){target="_blank"}
* Organizer: James Hogg


### Cognitive Science and Neuroscience (2021)

* Date: November 19, 2021
* Recorded Talks: [YouTube](https://youtu.be/rsY3dVAP9gQ?si=jfQKc7sQvfyPCuQB){target="_blank"}
* Program:
    + *Resolving the multiple testing issue in neuroimaging through Bayesian multilevel modeling* - Gang Chen	
    + *Implementation of the Diffusion Decision Model with Across-Trial Variability in the Drift Rate* - Kendal Foster and Henrik Singmann	
    + *It’s complicated: Some observations on the nuanced constraints of the multivariate normal in high dimensions* - Mike Lawrence	
    + *Using computational modeling parameters to measure working memory processes* - Jan Göttmann	
* [Abstracts](https://cognitive-science-stan.github.io/stanconnect2021-cogsci/){target="_blank"}
* Organizer: Bruno Nicenboim


### Biostats (2021)

* Date: October 19, 2021
* Recorded Talks: [YouTube](https://youtu.be/cq5r0sSTPIg?si=t1VBM5zCMRILzbvk){target="_blank"}
* GitHub repository of slides and materials: [https://github.com/maxbiostat/StanConnect2021_Biostatistics](https://github.com/maxbiostat/StanConnect2021_Biostatistics){target="_blank"}
* Program: 
  + [Coding in Stan: the BYM2 model for disconnected graphs](https://github.com/maxbiostat/StanConnect2021_Biostatistics/tree/main/talk_1){target="_blank"}  Mitzi Morris
  + [Normalized power prior models in Stan](https://github.com/maxbiostat/StanConnect2021_Biostatistics/blob/main/talk_2/ethan.md){target="_blank"}  Ethan Alt
  + [Summarising enzyme information from online databases using Stan and Arviz](https://github.com/maxbiostat/StanConnect2021_Biostatistics/blob/main/talk_3/slides.pdf),   Teddy Groves
  + [Automated kinetic modelling in Stan and its application to the methionine cycle](https://github.com/maxbiostat/StanConnect2021_Biostatistics/tree/main/talk_4){target="_blank"}  Nicholas Cowie
  + [Using Hidden Markov Models as a complement/alternative to survival models](https://github.com/maxbiostat/StanConnect2021_Biostatistics/tree/main/talk_5){target="_blank"}Martin Modrák
* Organizer:  Luiz Max Carvalho, PhD - Getúlio Vargas Foundation


### Ecology, models for biological survey data (2021)

A 2-day event showcasing applications of Stan for ecological analyses

* Day 1: September 30, 2021
* Day 1 Recorded Talks: [YouTube](https://youtu.be/yn7jIHzvqXU?si=MypOndaKmKOvLDo9){target="_blank"}
* Program Day 1:
   + *Using Stan to build better community* - Ara Winter
   + *Using Stan to diagnose and fit high-dimensional multispecies abundance models* - Harold Eyster
   + *250000 parameters: the story of an occupancy model for Colombia’s birdlife in Stan* - Jacob Socolar 
   + *MLOps in a Bayesian workflow: tracking experiments with MLFlow* - Maxwell Joseph

* Day 2: October 4, 2021
* Program Day 2:
* Day 2 Recorded Talks: [YouTube](https://youtu.be/Ie0WGa6vAws?si=KmJsUtQrv_k7Xed-){target="_blank"}
   + *Evolution of habitat use and coexistence under intraguild predation: a principled Bayesian approach* - Josh Goldberg
   + *Drift algae controls the consumption of kelp: using data from in-situ subtidal experimentation and ordinary differential equations to mechanistically model the sea urchin behavioral switch* - Zach Randell
   + *Linking the SPDE method with Stan* - Joaquin Cavieres
   + *Using Stan to characterize large-scale morphological change in North American birds* - Casey Youngflesh
   + *Fitting hidden Markov models to ecological time series data in Stan* - Vianey Leos Barajas
* Organizer: Jacob B. Socolar, Cornell Lab of Ornithology
* Panelist Day 1: Vianey Leos Barajas: Assistant Professor, University of Toronto, Department of Statistical Sciences