Call for invited session proposals for the upcoming BayesComp conference

2026-06-16 · Source: Statistical Modeling, Causal Inference, and Social Science · Field: Science & Research — Mathematics & Computational Sciences, Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

The BayesComp 2027 conference, set to take place in College Station, Texas, from May 18–20, 2027, has officially announced its call for invited session proposals. The scientific committee is actively soliciting submissions that highlight timely, important, and broadly engaging topics within the domain of Bayesian computation and its related areas. Each proposed invited session must consist of three distinct speakers, and a key guideline states that each individual speaker may only be listed in one invited or contributed session proposal across the entire conference. The firm deadline for submitting these invited session proposals is August 15, 2026. This announcement was shared by Lu Zhang, a notable figure in Bayesian statistics and computing, recognized for contributions such as the Pathfinder paper.

Key takeaway

For research scientists or AI students engaged in Bayesian computation, consider submitting an invited session proposal for BayesComp 2027. You should focus your proposal on timely and broadly engaging topics, ensuring it includes three speakers. Remember the August 15, 2026, deadline and that you can only be listed as a speaker in one session. This is an opportunity to present your work and contribute to the field.

Key insights

The BayesComp 2027 conference seeks invited session proposals on Bayesian computation topics by August 15, 2026.

Principles

• Focus on timely, important, and broadly engaging topics.
• Each invited session requires three speakers.
• Speakers are limited to one session proposal listing.

In practice

• Prepare a three-speaker session proposal.
• Ensure topic relevance to Bayesian computation.
• Submit by August 15, 2026.

Topics

• BayesComp 2027
• Bayesian Computation
• Conference Proposals
• Invited Sessions
• Statistical Computing

Best for: AI Scientist, Research Scientist, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Statistical Modeling, Causal Inference, and Social Science.