Google Summer of Code page

docs/make.jl

@@ -69,6 +69,7 @@ makedocs(;
         "More on distributed PT" => "distributed.md",
         "Interfaces" => Pigeons.informal_doc(@__DIR__, Pigeons),
         "Reference" => "reference.md",
+        "Google Summer of Code" => "gsoc.md"
     ],
 )
 

docs/src/gsoc.md

@@ -0,0 +1,121 @@
+```@meta
+CurrentModule = Pigeons
+```
+# [Pigeons Projects - Google Summer of Code](@id gsoc)
+
+## Python and R Interface for Pigeons
+
+Pigeons allows users to scale their Bayesian computation on up to thousands of 
+machines. At the moment, the only available API is through the Julia programming 
+language. To reach a wider audience, we would like to extend this to Python and R.
+Work on this project would include: 
+
+- Development of a new Pigeons interface in Python and/or R.
+
+- Testing of the new interface to ensure identical output to Julia.  
+
+- Engaging with researchers interested in using a Python/R interface and implementing additional suggested features. 
+
+**Recommended Skills:** Familiarity with Python and/or R. A basic knowledge of 
+statistical concepts and a desire to learn the basics of Julia and Bayesian inference.
+
+**Expected Results:** An interface for Pigeons in either Python or R (or both). 
+
+**Mentors:** [Alexandre Bouchard-Côté](https://github.com/alexandrebouchard), 
+[Trevor Campbell](https://github.com/trevorcampbell/), and 
+[Nikola Surjanovic](https://github.com/nikola-sur).
+
+**Expected Project Size:** 175 hours or 350 hours. 
+
+**Difficulty:** Medium.
+
+<br>
+
+## Automated Parameter Tuning
+
+The core algorithm behind Pigeons, parallel tempering, has recently had [major developments](https://arxiv.org/abs/1905.02939). 
+Some questions remain regarding the selection of tuning parameters in parallel tempering. 
+While these have been partially theoretically resolved, it remains to automate 
+the selection procedure in software such as Pigeons.
+Work on this project would include:
+
+- Development of an automated parameter selection procedure (e.g., the number of chains in parallel tempering). 
+
+- Simulations to compare theoretical results and empirical performance.
+
+- Further work on the parallelization of Pigeons (e.g., automated selection of number of machines and instances of parallel tempering).
+
+**Recommended Skills:** Familiarity with Julia and distributed/parallel computing. 
+A basic knowledge of statistical concepts. A desire to learn about the parallel tempering algorithm.
+
+**Expected Results:** An automated tuning parameter selection procedure and a simplified user interface. 
+
+**Mentors:** [Alexandre Bouchard-Côté](https://github.com/alexandrebouchard), 
+[Trevor Campbell](https://github.com/trevorcampbell/), and 
+[Nikola Surjanovic](https://github.com/nikola-sur).
+
+**Expected Project Size:** 175 hours or 350 hours. 
+
+**Difficulty:** Medium to Hard, depending on the chosen tasks.
+
+<br>
+
+## Automated Families for Variational Inference and MCMC 
+
+The core algorithm behind Pigeons, parallel tempering, has recently had [major developments](https://arxiv.org/abs/1905.02939). 
+In particular, [recent work](https://arxiv.org/abs/2206.00080) combines variational inference methods with 
+parallel tempering to improve the performance of both. 
+At the moment, Pigeons only implements basic variational families (e.g., mean-field Gaussians). 
+Work on this project would include:
+
+- Incorporating new and existing variational families within Pigeons. 
+
+- Automated selection of variational families depending on the given computational problem. 
+
+- Experimental comparison of the performance of various variational families on 
+given computational tasks.
+
+**Recommended Skills:** Familiarity with Julia. 
+A basic knowledge of Bayesian statistical concepts. A desire to learn about parallel tempering and variational inference. 
+
+**Expected Results:** An implementation of a rich collection of variational families within Pigeons, 
+and an automated variational family selection procedure. 
+
+**Mentors:** [Alexandre Bouchard-Côté](https://github.com/alexandrebouchard), 
+[Trevor Campbell](https://github.com/trevorcampbell/), and 
+[Nikola Surjanovic](https://github.com/nikola-sur).
+
+**Expected Project Size:** 175 hours or 350 hours. 
+
+**Difficulty:** Medium to Hard, depending on the chosen tasks.
+
+<br>
+
+## Library of Difficult Sampling Problems 
+
+The fields of Bayesian statistical inference and statistical physics abound with 
+difficult sampling problems. In the field of machine learning, it is common to compare 
+methods across several standard data sets. 
+In contrast, such collections of standard data sets and models do not exist or 
+are limited in scope in the field of statistics.
+(For example, the current, most commonly used library of difficult sampling problems, 
+[posteriordb](https://github.com/stan-dev/posteriordb), does not emphasize 
+difficult distributions such as non-log-concave targets.) Work on this project would include:
+
+- Searching for difficult sampling problems in the literature and implementing some examples in Julia.
+
+- Numerical experiments to compare the performance of Pigeons with other state-of-the-art sampling algorithms.
+
+**Recommended Skills:** Familiarity with Julia, Markdown, and some basics of website development. 
+A basic knowledge of statistical concepts. A desire to learn about the parallel tempering algorithm.
+
+**Expected Results:** A collection of difficult sampling problems and implementations in Julia. 
+
+**Mentors:** [Alexandre Bouchard-Côté](https://github.com/alexandrebouchard), 
+[Trevor Campbell](https://github.com/trevorcampbell/), and 
+[Nikola Surjanovic](https://github.com/nikola-sur).
+
+**Expected Project Size:** 175 hours or 350 hours. 
+
+**Difficulty:** Easy to Medium, depending on the chosen tasks.
+