cfp.qmd

---
title: "Call for Proposals"
---

## Thanks to everyone for submitting!

The Call for Proposals for **R+AI 2026** is now closed. We wanted to hear about your experience and specific workflows at the intersection of R and AI—whether you’re just getting started with machine learning, experimenting with large language models in R, using AI tools to accelerate your code, deploying AI solutions in industry, or researching deep learning and responsible AI.

The deadline was September 7, 2026.

## Key dates

- CFP closed: September 7, 2026  
- Conference: November 10-11, 2026  

## R+AI 2026 Call for Proposals: Technical Areas

**Foundations of Modern AI for R Users**  
Tutorials and primers that help R users understand today’s AI stack in practice: foundation models, embeddings, vector search, provider and model selection, cost and latency trade-offs, local versus hosted models, and how modern AI fits alongside classical machine learning in R.

**LLM Engineering in R**  
Sessions on building reliable LLM workflows in R, including structured outputs, tool and function calling, retrieval, prompt and context design, batching, caching, file and image inputs, and hybrid R/Python patterns.

**Agents, MCP, and Tool Ecosystems**  
Talks on agentic workflows in R: MCP servers and clients, multi-agent orchestration, skills, memory, tool registries, package-aware assistants, and hybrid architectures that keep R at the center of the workflow.

**AI-Assisted R Development and Coding Workflows**  
How AI is changing R development: code generation, refactoring, debugging, documentation, test creation, review, migration, package maintenance, legacy-code modernization, and IDE-native assistance.

**Machine Learning, Deep Learning, and Multimodal AI in R**  
Not every AI workflow is an LLM. This track covers tabular ML, forecasting, causal ML, tidymodels, torch, geospatial deep learning, computer vision, multimodal pipelines, and other predictive workflows where R remains strong.

**AI-Powered Data Products and Conversational Analytics**  
Building chat-enabled Shiny apps, analyst copilots, natural-language dashboards, reactive data interfaces, and human-in-the-loop tools that turn questions into auditable analysis and visual output.

**Evaluation, Observability, Responsible AI, and Governance**  
How to evaluate AI systems in R, compare performance, cost, and latency, instrument applications, manage prompt and tool regressions, document behavior, and address privacy, reproducibility, bias, fairness, and regulatory expectations.

**Production AI and Industry Case Studies**  
Frameworks, architectures, and case studies for shipping AI with R in real organizations: secure deployment, private or managed models, enterprise platforms, monitoring, cost control, procurement constraints, and lessons learned in healthcare, pharma, finance, marketing, manufacturing, research, and the public sector.

## Formats

- Talks (20–25 min)  
- Lightning talks (5–7 min)  
- Workshops (2–3 hours)  
- Panels (45–60 min)

## Important notes

All speakers are required to adhere to our [Code of Conduct](attend.qmd).

Talks will be recorded and posted to the [R Consortium YouTube channel](https://www.youtube.com/@RConsortium).