#FlatironCCM's Teresa Huang explores how symmetries and structure can reveal new insights into the universe, aiding discovery in everything from cosmology to the development of fusion energy. https://www.simonsfoundation.org/teresa-huang-bridging-science-and-machine-learning-at-cosmic-scales/
do mine eyes deceive me? a release date in the present year?
StanCon 2026 is this August 17-21 in Upsala, Sweden 🇸🇪
www.stancon2026.org
⏰ Abstracts for contributed talks are due Feb 25
⏰ Abstracts for posters are due May 27
And just to be clear: Yes, StanCon is my favorite conference to attend!! Can't wait for this one!
**Part 1: From Bayesian inference to Bayesian workflow** 1. Bayesian theory and Bayesian practice 2. Statistical modeling and workflow 3. Computational tools 4. Introduction to workflow: Modeling performance on a multiple choice exam **Part 2: Statistical workflow** 5. Building statistical models 6. Using simulations to capture uncertainty 7. Prediction, generalization, and causal inference 8. Visualizing and checking fitted models 9. Comparing and improving models 10. Statistical inference and scientific inference **Part 3: Computational workflow** 11. Fitting statistical models 12. Diagnosing and fixing problems with fitting 13. Approximate algorithms and approximate models 14. Simulation-based calibration checking 15. Statistical modeling as software development
**4. Case studies** 16. Coding a series of models: Simulated data of movie ratings 17. Prior specification for regression models: Reanalysis of a sleep study 18. Predictive model checking and comparison: Clinical trial 19. Building up to a hierarchical model: Coronavirus testing 20. Using a fitted model for decision analysis: Mixture model for time series competition 21. Posterior predictive checking: Stochastic learning in dogs 22. Incremental development and testing: Black cat adoptions 23. Debugging a model: World Cup football 24. Leave-one-out cross validation model checking and comparison: Roaches 25. Model building and expansion: Golf putting 26. Model building with latent variables: Markov models for animal movement 27. Model building: Time-series decomposition for birthdays 28. Models for regression coefficients and variable selection: Student grades 29. Sampling problems with latent variables: No vehicles in the park 30. Challenge of multimodality: Differential equation for planetary motion 31. Simulation-based calibration checking in model development workflow **Appendices** A. Statistical and computational workflow for Bayesians and non-Bayesians B. How to get the most out of Bayesian Data Analysis
Bayesian Workflow by
Andrew Gelman, Aki Vehtari, @rmcelreath.bsky.social with @danpsimpson.bsky.social, @charlesm993.bsky.social, @yulingy.bsky.social, Lauren Kennedy, Jonah Gabry, @paulbuerkner.com, @modrakm.bsky.social, @vianeylb.bsky.social
(in production, estimated copy-editing time 6 weeks)
Simons Foundation president David Spergel recently spoke to @issuesinst.bsky.social about the future of science philanthropy: https://issues.org/american-science-simons-spergel-interview/ #science #math #philanthropy
What is "workflow" and why is it important?
The latest blog post by Andrew Gelman: statmodeling.stat.columbia.edu/2026/01/08/w...
thank you for the excellent talk!
🇪🇸 This week I'm attending ICSDS (International Conference on Stats & Data Science) in Sevilla, Spain.
🤝 Looking forward to connecting with colleagues, old and new!
💡On Wednesday, I'll give a talk on "Variational Inference in the Presence of Symmetry" at the 9 am session on Bayesian learning.
Check out my poster today (Thurs) at 11am--2pm session. Exhibit Hall C,D,E Poster Location: #602
"Fisher meets Feynman: score-based variational inference with a product of experts" (NeurIPS spotlight)
with Robert Gower, David Blei, and Lawrence Saul
@flatironinstitute.org #NeurIPS2025
... and a short blog post with some additional details.
🌎 statmodeling.stat.columbia.edu/2025/11/07/m...
Applications for the PhD and MSc programs in statistics at UBC are now open!
📆 Deadline for PhD program is December 1st
📆 Deadline for MSc program is January 5th
The department covers all areas of statistics and we have a lot of momentum in Bayesian computation!
Application for a postdoc research fellowship in computational mathematics at the Flatiron Institute in New York are now open!
apply.interfolio.com/173401
📆 Deadline is December 1st.
🔭 This is an excellent place to do research at the interface of ML, stats and the natural sciences.
I also like to describe this paper as a discussion on what is the best circle to approximate an ellipse :)
🧵 4/4
This paper contributes to the foundational theory of VI, and dives deep into both conceptual and practical questions such as: How do we measure uncertainty in high-dimensions? How should we measure discrepancy between probability distributions?
🧵 3/
The two main results of the paper are:
1️⃣ An impossibility theorem that shows that any factorized (mean-field) approximation of VI can at beast learn one of three measures of uncertainty
2️⃣ An ordering of divergences used as objectives for VI based on the uncertainty in their approximation.
🧵 2/
My paper with Loucas Pillaud-Vivien and Lawrence Saul, “Variational Inference for Uncertainty Quantification: An Analysis of Trade-offs”, has been accepted for publication in the Journal of Machine Learning Research.
📃 arxiv.org/abs/2403.13748
🧵 1/
Yes, in principle, I start at UBC Statistics today. But right now, I'm running around the Frankfurt airport to catch my flight to Vancouver .... 🏃♂️🧳✈️
www.stat.ubc.ca/news/charles...
💻 This was also my first time using Stan playground (github.com/flatironinst...) to teach a class! Thank you Brian Ward for creating this tool and helping me set it up for the class!
📔 My course: "Bayesian Statistics: a practical introduction." We covered Bayesian models (priors and likelihoods), Markov chain Monte Carlo and uncertainty aware cross-validation. Most of our discussion was motivated by an example from epidemiology.
Earlier this month, I taught at the summer school on "cryptography, statistics and machine learning" (mathschool.ysu.am) hosted by Yerevan State University in Armenia 🇦🇲
🙏 Thank you to the organizers for putting together such a wonderful event! I truly enjoyed interacting with the students.
👨💻 Credit also to Brian Ward and Steve Bronder for their contribution to the C++ implementation and integration with the Stan ecosytem. (From what I understand, WALNUTS is not part of the next Stan release but you can use it on models written in Stan!!)
New manuscript by Nawaf Bou-Rabee, Bob Carpenter, Tore Kleppe and Sifan Liu on the WALNUTS algorithm which improves of the NUTS sampler by introducing a locally adaptive step size.
📜 Paper: arxiv.org/pdf/2506.18746
💻 Code: github.com/bob-carpente...
🇸🇬 Next stop: Singapore for BayesComp'25 (bayescomp2025.sg) The organizers put together a wonderful program!
I'll be:
🪑 chairing the session on "Parallel comp for MCMC"
🎙️ speaking at the session on "Advances in VI"
Looking forward to meeting researchers and catching up with colleagues.
Research opportunity for a graduate student in ecology 🌳 at UBC 🇨🇦 with Lizzie Wolkovich and the Temporal Ecology lab (temporalecology.org).
📝 Apply here: temporalecology.org/joining-the-... by July 1st 2025!
The abstract sounds fascinating (see attached).
🧑💻 Candidate release for Stan 2.37 is out: discourse.mc-stan.org/t/cmdstan-st.... Lots of exciting features to try out, including:
- embedded/integrated Laplace approximation
- new constrained types (e.g. sum_to_zero_matrix)
- built-in constraint transformations exposed
Taking our Models Seriously (my talk at StanBio Connect, this Friday 9am)
statmodeling.stat.columbia.edu/2025/05/27/t...
🙏This award is this much more meaningful to me in that it celebrates my collaboration with the amazing Lawrence Saul (users.flatironinstitute.org/~lsaul/).
💡We provide theory on VI's ability to recover certain statistics, despite misspecification---that is in settings where we do NOT drive the KL-divergence to 0.
👉 VI is provably good at recovering the mean and correlation matrix.
