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Event

Media Lab @ IUI Conference 2026

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Monday — Thursday
July 13, 2026 —
July 16, 2026

A variety of Media Lab community members will participate in the 2026 ACM Conference on Intelligent User Interfaces (ACM IUI), the premier annual forum where researchers and practitioners discuss state-of-the-art advances at the intersection of Artificial Intelligence (AI) and Human-Computer Interaction (HCI). 

This year's conference takes place in Limassol, Cyprus.

Tuesday, July 14

Paper: Agents in Concert: A Case-Study of Bringing AI to the Stage in Practice 

A longitudinal account of codesigning live human-AI musical improvisation with top local jazz musicians, culminating in a public concert featuring three pieces co-improvised with AI agents. The work uncovers design interventions for bringing generative AI systems to the stage and the unique practice emerging through musician-AI live improvisation. Presented by Responsive Environments graduate students Lancelot Blanchard and Perry Naseck, Opera of the Future graduate student Kimaya Lecamwasam, alum Anna Huang, and collaborators from MIT EECS, MIT DMSE, and Georgia Institute of Technology.

Demo: Neural Notes: An Online Platform for Real-Time Structured Musical Improvisation 

A browser-based platform for real-time musical improvisation supporting human–human, human–AI, and AI–AI interaction, serving as both a creative tool and a research testbed for studying improvisational dynamics and multi-agent musical collaboration. Presented by Viral Communications graduate student Mike Hao Jiang and senior research scientist Andrew Lippman.

Wednesday, July 15

Keynote: Designing AI Interaction for Human Flourishing

Pattie Maes, Germeshausen Professor of Media Arts and Sciences and head of the Fluid Interfaces group, delivers the IUI 2026 keynote address.

Thursday, July 16

Paper: Mind Mapper: Modeling and Predicting Behavioral Patterns from Everyday Conversations with Wearable AI Systems and LLMs 

An always-on wearable AI system that mines behavioral patterns from everyday conversations using a multi-stage LLM pipeline. In a field study capturing over 700 hours of real-life conversational data, the system generated patterns that participants rated as accurate, unique, and helpful for reflection and behavior change. Presented by Fluid Interfaces graduate students Valdemar Danry and Yasith Samaradivakara, Professors Paul Pu Liang and Pattie Maes.

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