Multisensory Intelligence Lab

We are a research group at UNIST (Ulsan National Institute of Science and Technology), led by Prof. Arda Senocak. Our work spans the intersection of perception, representation, and learning across multiple sensory modalities.

We develop machine learning models that perceive and reason about the world through multiple senses — as humans do. Our research encompasses multimodal learning with the goal of building AI systems that are grounded in rich, diverse streams of real-world signals.

Multimodal Learning Audio-Visual Learning Visuo-Tactile Learning Visuo-Olfactory Learning Audio/Speech Processing

People

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Arda

Arda Senocak

Principal Investigator

Yewon Kim

Yewon Kim

PhD Student

Assan

Assanali Salem

Undergraduate Intern

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Is That You?

Join Us!

Join MILab

We are actively looking for motivated PhD/MS students and Undergraduate interns passionate about multimodal learning and sensory AI. If you are interested, please send your CV and a brief research statement to multisensory.intelligence.lab@gmail.com.

Contact Prof. Senocak
May 2026
Award

Prof. Senocak is selected as an Outstanding Reviewer at CVPR 2026.

May 2026
Award

Prof. Senocak is serving as Area Chair at NeurIPS 2026.

March 2026
Award

MILab has been awarded a 3-year research grant from the National Research Foundation of Korea (NRF) under the Outstanding Young Scientist Grant (우수신진연구).

Feb 2026
Paper

Three main conference papers (1 Highlight ⭐) are accepted to CVPR 2026.

Publications

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* Equal Contribution   ·   Corresponding Author

Seeing Through Touch teaser

Seeing Through Touch: Tactile-Driven Visual Localization of Material Regions

Seongyu Kim, Seungwoo Lee, Hyeonggon Ryu, Joon Son Chung, Arda Senocak

CVPR 2026 († Corresponding Author)

Synthetic SSL teaser

How Far Can We Go With Synthetic Data for Audio-Visual Sound Source Localization?

Arda Senocak*, Sooyoung Park*, Tae-Hyun Oh, Joon Son Chung

CVPR 2026 Selected as Highlight Paper ⭐ (* Equal Contribution)

CASS teaser

Cinematic Audio Source Separation Using Visual Cues

Kang Zhang*, Suyeon Lee*, Arda Senocak†, Joon Son Chung†

CVPR 2026 († Co-Corresponding Author)