Announcing the ICLR 2026 keynotes

2026-04-17 · Source: ICLR Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Expert, medium

Summary

ICLR 2026 will feature six keynote speakers covering diverse areas from core machine learning to robotics, neuroscience, and AI for science. Maja Matarić will discuss challenges in human-centered AI and robotics, focusing on human-robot interaction, long-term user modeling, and socially assistive robotics for various populations. Max Welling, a research chair at the University of Amsterdam and Distinguished Scientist at MSR, will deliver an invited talk. Percy Liang will introduce Marin, an open development platform for frontier AI models, emphasizing transparency in training and scientific collaboration. Katie Bouman will explore how physics and machine learning combine to infer images of the hidden universe, including black holes and dark matter. Karen Adolph will present research on how infants acquire intelligent behavior through learning while developing, highlighting the role of spontaneous practice. Finally, Pablo Arbeláez will discuss Artificial Intelligence for Open Science, showcasing collaborative projects in robotic surgery, spatial transcriptomics, drug discovery, geology, and nature conservation.

Key takeaway

For AI scientists and researchers considering new collaboration models or interdisciplinary applications, these ICLR 2026 keynotes offer critical perspectives. You should investigate platforms like Marin for transparent AI development and consider the societal implications of human-centered AI and robotics. Additionally, explore how integrating physics with machine learning can enhance data interpretation in scientific imaging, and recognize the value of open video sharing for behavioral science.

Key insights

ICLR 2026 keynotes highlight diverse AI applications, from human-robot interaction to open science and cosmic imaging.

Principles

• Human-robot interaction is critical, not an afterthought.
• Open development improves scientific rigor and community participation.
• Integrating physics and ML extracts more from limited data.

Method

Maja Matarić's research combines robotics, AI, and ML for long-term user modeling, real-time multimodal behavioral signal processing, and affective computing to enable adaptive human-machine interaction.

In practice

• Explore Marin for open-source frontier AI model development.
• Consider multimodal data for robust human-machine interaction.
• Apply physics-informed ML for scientific imaging problems.

Topics

• Human-Robot Interaction
• Socially Assistive Robotics
• Open AI Development
• Frontier AI Models
• Scientific Imaging

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by ICLR Blog.