Mia Huong Nguyen

I am a PhD candidate at Augmented Human Lab, School of Computing, National University of Singapore under the supervision of Prof. Suranga Nanayakkara. I am broadly interested in the human mind, emotion, and artificial intelligence. My research focuses on designing technologies that expand people’s affective capacity to enhance their emotional resilience and well-being. Before joining NUS, I spent 4 years at Ulsan National Institute of Science and Technology in South Korea studying Computer Science and Mathematical Sciences, and 2 years in Vietnam working as a Research Engineer at Trusting Social.

I have submitted my thesis and is on job market for academic roles as well as industry research roles. Please email to mia@ahlab.org if you would like to connect!

news

Mar 8, 2026	VisceroHaptics got Honourable Mention Award at CHI!!! I’m thrilled and honored! 🥳🥳🥳
Jan 15, 2026	Two papers got accepted to CHI 2026 🥳! Barcelona I’m cominggggggg!
Dec 22, 2025	Extremely happy to receive an early Christmas presen 🎁! Our paper titled “Beyond Happy and Sad: Exploring Granular Affect Labeling to Enhance Emotion Regulation Ability” is accepted to IJHCS 🎉🎉!
Aug 20, 2025	I’m visiting Inami Lab, University of Tokyo for 3 months in Tokyo as Visiting Researcher! Can’t wait to stuff my stomach with sushi🍣😋! (and explore exciting research projects, of course!)
Jun 16, 2025	Successfully defended my Thesis Proposal! One step closer to get Permanent head Damage!
Apr 28, 2025	I’m attending CHI 2025 in Japan! Happy to visit Japan again after 10 years!
Feb 23, 2025	Finally, after all the last minute dramas, our paper titled: “GutIO: Toward Sensing and Inducing Gut Feelings with Abdominal Sounds” is accepted to CHI LBW 2025!
Jan 16, 2025	Our paper titled: “Human Robot Interaction for Blind and Low Vision People: A Systematic Literature Review” is accepted to CHI 2025!
Aug 2, 2024	Our poster titled: “Catch that butterfly: A Multimodal Approach for Detecting and Simulating Gut Feelings” is accepted to UIST 2024!
Jul 1, 2024	Enjoy a fresh update to Mia’s page!

selected publications

CHI

🏆 VisceroHaptics: Investigating the Effects of Gut-based Audio-Haptic Feedback on Gastric Feelings and Gastric Interoceptive Behaviors

Mia Huong Nguyen, Moritz Alexander Messerschmidt, Jochen Huber, and 1 more author

In Proceedings of the CHI Conference on Human Factors in Computing Systems. 2026
CHI

“My Tummy Has a Little Dragon”: From Everyday Experiences of Gut Sounds to Interoceptive Interaction Design

Nandini Pasumarthy, Mia Huong Nguyen, Ashika Ramesh Krishnan, and 6 more authors

In Proceedings of the CHI Conference on Human Factors in Computing Systems 2026
IJHCS

Beyond Happy and Sad: Exploring Granular Affect Labelling to Enhance Emotion Regulation Ability

Mia Huong Nguyen, Dixon Daniel Prem Rajendran, and Suranga Nanayakkara

International Journal of Human-Computer Studies 2026
CHI

GutIO: Toward Sensing and Inducing Gut Feelings with Abdominal Sounds

Mia Huong Nguyen, Moritz Alexander Messerschmidt, Chitralekha Gupta, and 1 more author

In Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems 2025

PDF
EMNLP

EMO-KNOW: A Large Scale Dataset on Emotion-Cause

Mia Huong Nguyen, Yasith Samaradivakara, Prasanth Sasikumar, and 2 more authors

In Findings of the Association for Computational Linguistics: EMNLP Dec 2023

Abs PDF Code

Emotion-Cause analysis has attracted the attention of researchers in recent years. However, most existing datasets are limited in size and number of emotion categories. They often focus on extracting parts of the document that contain the emotion cause and fail to provide more abstractive, generalizable root cause. To bridge this gap, we introduce a large-scale dataset of emotion causes, derived from 9.8 million cleaned tweets over 15 years. We describe our curation process, which includes a comprehensive pipeline for data gathering, cleaning, labeling, and validation, ensuring the dataset’s reliability and richness. We extract emotion labels and provide abstractive summarization of the events causing emotions. The final dataset comprises over 700,000 tweets with corresponding emotion-cause pairs spanning 48 emotion classes, validated by human evaluators. The novelty of our dataset stems from its broad spectrum of emotion classes and the abstractive emotion cause that facilitates the development of an emotion-cause knowledge graph for nuanced reasoning. Our dataset will enable the design of emotion-aware systems that account for the diverse emotional responses of different people for the same event.