Call for Proposals

Submit a proposal

The Call for Proposals for R+AI 2026 is now open. We want 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, leveraging GenAI tools to accelerate your code, deploying AI solutions in industry, or researching deep learning and responsible AI.

The deadline is September 7, 2026.

Key dates

• CFP closes: 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.

Talks will be recorded and posted to the R Consortium YouTube channel.