Dates: September 9 - 13th 2024

Recordings of the talks are now on the Stan Youtube Channel!

Stan is a widely used Bayesian inference software, which has found many applications across academia and industry, including political science, pharmacometrics, epidemiology, astrophysics, advertising, and more. The conference brings together both veteran and novice users, and serves as a focused event to discuss practical deployment and application of Bayesian modeling.

The main conference is Tuesday 10th - Thursday 12th.

Monday 9th afternoon we will have a half-day of workshops, including an introductory tutorial on Stan, and we will have an additional day of workshops on Friday 13th.

The conference comprises one and half day of tutorial (with introductory and advanced courses) and three days of contributed talks.

For any questions, please reach out to stancon2024 at mc-stan dot org.

Schedule

The full schedule for the conference is available here: https://docs.google.com/spreadsheets/d/1UcnY_ItdctMjWbuNsfGdjOLW_kCQp6Iy/edit?gid=204460881#gid=204460881

Sponsors

Sponsors support StanCon in various ways and help us book venues, create high quality online content, provide scholarships for early career researchers and participants from underrepresented groups in the sciences, and more.

If you’re interested in sponsoring StanCon, please email board@mc-stan.org.

We thank our current sponsors and supporting institutions: Daiichi-Sankyo, Metrum Research Group, Generable, Jumping Rivers, Taylor and Francis, NumFocus, the Oxford Internet Institute, and the Max Planck Institute for Evolutionary Anthropology.

Keynote speakers
Call for proposals
Talks
Tutorials
Registration
Scholarships
Lodging
Travelling
Organizers

Keynote speakers

These are the confirmed keynote speakers:

Chris Wymant (Senior Researcher, Pandemic Sciences Institute, Oxford University)
Bayesian multilevel causal modelling of the frequency and implications of having two HIV infections
Vianey Leos Barajas (Assistant Professor, Department of Statistical Sciences & School of the Environment, University of Toronto)
From the Depths to the Stars: How Modeling Shark Movements Illuminates Star Behavior
Sebastian Weber (Director Statistical Methods & Consulting, Advanced Exploratory Analytics, Novartis Pharma AG, Basel)
Applied modeling for drug development
Mitzi Morris (Stan Developer, Columbia University)
The Pragmatic Probabilistic Programmer

Talk titles and abstracts will be posted soon!

Call for proposals

We’re now accepting proposals for contributed talks and tutorials. Proposals are reviewed by the organization committee on a rolling basis.

Contributed talks are 15 minutes long, with an additional 5 minutes for questions. Talks can cover a diversity of topics, including:

Data analysis and modeling using Stan in any field
Software development within Stan or the Stan ecosystem, and more broadly software relevant to Stan users
Methodological development for Bayesian modeling

To submit a proposal for a talk, please fill out this form.

Tutorials are either half-a-day or one day long, with each half-day 2 or 3 hours long. Tutorials should be introductory, though certain pre-requisites may be assumed (e.g. basic knowledge of Stan, R, Python, probability, etc.). When submitting a proposal be specific about the pre-requisites.

We will provide an “introduction to Stan” tutorial, aimed at participants who have not used Stan before.

To submit a proposal for a tutorial, please fill out this form.

Talks

Below are the confirmed talks (in no particular order). These are talks which have been accepted by the scientific committee and for which speakers have confirmed their attendance.

Ethan Budge (Brigham Young University)
Sharing the Spotlight: How Artist Collaboration affects Song Popularity
Brynjólfur Gauti Guðrúnar Jónsson
Copulas in Stan: Modeling Spatial Dependence in Generalized Extreme Value Distributions
Xingyao (Doria) Xiao (UC Berkeley)
Bayesian Identification, Estimation, and Diagnostic Techniques for Growth Mixture Models using Stan
Bob Carpenter (Flatiron Institute)
What’s your favorite sushi? Combining ranking and rating models in Stan
Chirag Modi (Flatiron Institute)
Atlas: Adapting Trajectory Length and Stepsize
Jari Turkia (University of Eastern Finland)
Inferring Personalized Diet Recommendations Using a Conditional Mixture of Health Outcomes and Personal Preferences
Simon Maskell (Liverpool University)
Running Multiple Short MCMC Chains on a GPU Using JAX for Fast Inference with Stan
Brian Ward (Flatiron Institute)
Stan without installing Stan? How (and why) to sample inside your browser
Conor Goold (Ledger Investing)
Joint estimation of body and tail loss development factors in insurance: a case study using hidden Markov models in Stan
David Kohns (Aalto University)
The ARR2 prior: flexible predictive prior definition for Bayesian auto-regressions
Laura Jenniches (Helmholtz Institute for RNA-based Infection Research)
Advancing functional genomics analysis with Stan: Case studies in RNA decay and pathogen invasion models
Aditya Ravuri (Cambridge University)
GPLVM Equivalences in UMAP, Isomap and Beyond
Nicolas Irons (University of Washington) Evaluating and optimizing non-pharmaceutical interventions to combat infectious disease transmission
Causally sound priors for binary experiments
Adam Gorm Hoffmann (University of Copenhagen)
Efficient Hierarchical Gaussian Process Regression
Jonas Mikhaeil (Columbia University)
Hierarchical Bayesian Models to Mitigate Systematic Disparities in Prediction with Proxy Outcomes
Jouni Helske (University of Turku)
dynamite: An R Package for Dynamic Multivariate Panel Models
Yann McLatchie (Univeristy College London)
Predictive performance of power posteriors
Judith Bouman (University of Bern)
Bayesian workflow for time-varying transmission in stratified compartmental infectious disease transmission models
Sean Pinkney (Stan Developer)
Structured Correlation Matrices
Aki Vehtari (Aalto University)
Pareto-k diagnostic and sample size needed for CLT to hold
Gabriel Riutort-Mayol (FISABIO)
Bayesian Gaussian processes with correlated group effects
Sophie Van Den Neucker (Maastricht University)
A Bayesian approach to combined chemical exposure assessment for more informed public health decisions
Ziyuan Zhag (Boston University and Shanghai University of Finance and Economics)
Collaborative Translation and Continuous Updates: Advancing the Stan Chinese Documentation
Zhi Ling (National University of Singapore)
New fast heavy-tail count models in Stan
Charlotte Wilhelm-Benartzi (Daiichi-Sankyo)
The use of Bayesian Hierarchical Modelling using simulated data
Adrian Seybolt (PyMC Labs)
Nutpie: Fast and Efficient Bayesian Inference with Rust and Python
Paul Buerkner (TU Dortmund University)
Generative Bayesian Modeling with Implicit Priors
Jesse Piburn (Oak Ridge National Laboratory)
Priors, Posteriors, and Office Politics: Implementing Bayesian Workflow in a Large Organization
Jacqueline Buros (Generable)
A Semi-Mechanistic Longitudinal Gaussian Process Regression Model for Estimating Tumor Response to Treatment
Enzo Cerullo (University of Leicester)
Efficiently estimating latent class (and standard) multivariate probit models
Marco Antonio Gallegos Herrada (University of Toronto)
Exploring a Bayesian inference approach for the analysis of blue whale movement data
Matti Vuorre (Tilburg University)
Understaning psychological heterogeneity with Bayesian hierarchical models using the brms R package
Angie Moon (MIT)
Integrating Bayesian Inference and System Dynamics with Case Studies in Epidemiology

Tutorials

Confirmed tutorials will be posted here as they get accepted. The schedule for tutorials is tentative and subject to change.

Monday 9th

Richard McElreath (Max Planck Institute for Evolutionary Anthropology)
Introduction to Stan
Aki Vehtari, Noa Kallioinen and Teemu Säilynoja (all Aalto University)
Model selection

Friday 13th

Sean Pinkney (Stan Developer) Bayesian hierarchical models in Stan
Anna Elisabeth Riha (Aalto University), Adam Howes (CDC), Seth Flaxman (University of Oxford), Elizaveta Semenova (Imperial College London)
Bayesian optimisation with Stan
Will Pearse (Imperial College London)
Biodiversity modelling and forecasting
Juliette Unwin (University of Bristol)
Infectious Disease Modelling using Stan

Registration

At this point, registration for the conference is closed. If you are still interested in attending, please contact the organization committee at board@mc-stan.org.

Prices for early bird registration are listed below:

Students (Conference only) £199.0
Students (Conference and tutorials) £299.0
Academics (Conference only) £299.0
Academics (Conference and tutorials) £449.0
Industry (Conference only) £599.0
Industry (Conference and tutorials) £799.0

The early bird registration has been extended from July 1st to July 15th!
After July 15th, all ticket prices for non-student participants increase by £100.0 (prices for students will not increase).

Scholarships

At this time, all scholarships for StanCon 2024 have been awarded, and so we are no longer accepting applications.

The purpose of the StanCon scholarship is to make StanCon a more accessible and inclusive event.

The StanCon scholarship covers registration for the tutorial and the main conference, as well as local lodging. Scholarships are awarded on a need-base, and prioritize early career scientists, including students and post-docs, and members of underrepresented groups in STEM.

Applications are accepted and reviewed on a rolling basis, and scholarships are awarded based on available funds.

Lodging

The conference will be held at the Department of Engineering Science, University of Oxford, Thom Building, Parks Road, Oxford OX1 3PJ.

There are several options for securing your accommodation nearby. These include B&Bs, colleges, or hotels. Please see some examples below at various price points.

We also recommend looking into any available rooms at Oxford Colleges or SpeedyBooker, many of which offer ensuite accommodation with breakfast in an Oxford Hall and are an idyllic way to experience Oxford. Please note not all college accommodation is located in central Oxford, not all rooms offer hotel style amenities, and many have shared bathrooms, so please ensure you look for these when booking.

Travelling

If you are travelling from within the UK, taking a bus or train directly into Oxford would be the easiest option. Check the national providers for appropriate routes.

If you are travelling from outside the UK, we would suggest first to travel into London via train or plane. If you are travelling via plane, and arriving/departing via Gatwick or Heathrow airport, we recommend booking a return ticket on the airline – a bus service that will take you directly from the airport to the central Oxford bus station at Glouster Green. If you are arriving into London via train, and arriving/departing from St Pancras Station, we recommend taking the underground tube to Paddington Station and then a train to Oxford Station. Tickets can be bought at Great Western Railway, Trainline, and many other online providers. You can also purchase train tickets at the station if preferred. Travelling via the London underground is easiest using a contactless card. Alternatively, there are regular buses from London and Oxford through the “Oxford Tube” that provide travel at an affordable price.

For further information, please see the university’s advice on How to get to Oxford and some advice for international travel into Oxford, however, please check any travel independently as some information may be outdated.

Organizers

Michael Collyer (University of Oxford)
Joss Wright (University of Oxford)
Richard McElreath (Max Planck Institute for Evolutionary Anthropology)
Elizaveta Semenova (Imperial College London)
Ben Lambert (University of Oxford)
Seth Flaxman (University of Oxford)
Charles Margossian (Flatiron Institute)
Juliette Unwin (University of Bristol)
Will Pearse (Imperial College London)
Makkunda Sharma (University of Oxford)