updated publications and lab members

Author: nuoyihan

_people/2025-Aiden-Li.md

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+---
+layout: article
+
+publication-date: 2025-03-24
+title: "A Dynamic Bayesian Network Based Framework for Multimodal Context-Aware Interactions"
+authors:
+  - Violet Yinuo Han
+  - Tianyi Wang
+  - Hyunsung Cho
+  - Kashyap Todi
+  - Ajoy Savio Fernandes
+  - Andre Levi
+  - Zheng Zhang
+  - Tovi Grossman
+  - Alexandra Ion
+  - Tanya Jonker
+venue: In Proceedings of IUI ’25. Cagliari, Italy. March 24 -27, 2025
+type:
+  - Conference
+  - Full Paper
+  - Peer-reviewed
+tags: 
+  - Computational Interaction
+  - Dynamic Bayesian Networks
+  - Multimodal Interaction
+  - Context-Aware Interaction
+  - Bayesian Inference
+  - Large Language Models
+  - User Modeling
+
+video: https://youtu.be/rb8KfYchya8
+video-thumb: https://youtu.be/rb8KfYchya8
+
+image: teaser.png
+pdf: paper.pdf
+doi: https://dl.acm.org/doi/full/10.1145/3708359.3712070
+
+
+---
+
+<p>
+  Multimodal context-aware interactions integrate multiple sensory inputs, such as gaze, gestures, speech, and environmental signals, to provide adaptive support across diverse user contexts. Building such systems is challenging due to the complexity of sensor fusion, real-time decision-making, and managing uncertainties from noisy inputs. To address these challenges, we propose a hybrid approach combining a dynamic Bayesian network (DBN) with a large language model (LLM). The DBN offers a probabilistic framework for modeling variables, relationships, and temporal dependencies, enabling robust, real-time inference of user intent, while the LLM incorporates world knowledge for contextual reasoning. We demonstrate our approach with a tri-level DBN implementation for tangible interactions, integrating gaze and hand actions to infer user intent in real time. A user evaluation with 10 participants in an everyday office scenario showed that our system can accurately and efficiently infer user intentions, achieving 0.83 per frame accuracy, even in complex environments. These results validate the effectiveness of the DBN+LLM framework for multimodal context-aware interactions.
+</p>

_people/2025-Ziru-Wei.md

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+---
+layout: article
+
+publication-date: 2025-09-27
+title: "Transforming Everyday Objects into Dynamic Interfaces using Smart Flat-Foldable Structures"
+authors:
+  - Violet Yinuo Han
+  - Amber Yinglei Chen
+  - Mason Zadan
+  - Jesse T. Gonzalez
+  - Dinesh K. Patel
+  - Wendy Fangwen Yu
+  - Carmel Majidi
+  - Alexandra Ion
+venue: In Proceedings of UIST ’25. Busan, Republic of Korea. Sept. 28 - Oct. 01, 2025
+type:
+  - Conference
+  - Full Paper
+  - Peer-reviewed
+tags: 
+  - Physical Interfaces
+  - Robotic Objects
+  - Compliant Structures
+  - Kirigami
+
+video: https://youtu.be/XPE4Pmyh2fM
+video-thumb: https://youtu.be/l0rnG6rc5NA
+video-preview: https://youtu.be/l0rnG6rc5NA
+
+image: teaser.png
+pdf: paper.pdf
+doi: https://doi.org/10.1145/3746059.3747720
+
+
+---
+
+<p>
+  Dynamic physical interfaces are often dedicated devices designed
+  to adapt their physical properties to user needs. In this paper, we
+  present an actuation system that allows users to transform their
+  existing objects into dynamic physical user interfaces. We design
+  our actuation system to integrate as a self-contained locomotion
+  layer into existing objects that are small-scale, i.e., hand-size rather
+  than furniture-size. We envision that such objects can act as col-
+  laborators: as a studio assistant in a painter’s palette, as tutors in a
+  student’s ruler, or as caretakers for plants evading direct sunlight.
+</p>
+<p>
+  The key idea is to decompose the actuation into (1) energy input
+  and (2) steering to achieve a flat form factor. The energy input
+  is provided by simple vibration. We implement steering through
+  differential friction controlled by flat-foldable compliant structures
+  that can be activated electrically. We study the mechanism and its
+  performance, and show its application scenarios enabling dynamic
+  interactions with objects.
+</p>

_people/2025-Christina-Yang.md renamed to _people/alumni/2025-Christina-Yang.md

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+---
+layout: article
+
+publication-date: 2025-09-27
+title: "Towards Unobtrusive Physical AI: Augmenting Everyday Objects with Intelligence and Robotic Movement for Proactive Assistance"
+authors:
+  - Violet Yinuo Han
+  - Jesse T. Gonzalez
+  - Christina Yang
+  - Zhiruo Wang
+  - Scott E. Hudson
+  - Alexandra Ion
+venue: In Proceedings of UIST ’25. Busan, Republic of Korea. Sept. 28 - Oct. 01, 2025
+type:
+  - Conference
+  - Full Paper
+  - Peer-reviewed
+tags: 
+  - physical AI
+  - tangible interfaces
+  - human-AI Interaction
+  - agents
+  - robotic objects
+  - intention inference
+  - proactive assistance
+  - large language models
+
+
+video: https:https://youtu.be/x3IRVDR3SjM
+video-thumb: https://youtu.be/sWMiPVagiBs
+video-preview: https://youtu.be/sWMiPVagiBs
+
+image: teaser.jpg
+pdf: paper.pdf
+doi: https://doi.org/10.1145/3746059.3747726
+
+
+---
+
+<p>
+Users constantly interact with physical, most often passive, objects.
+Consider if familiar objects instead proactively assisted users, e.g.,
+a stapler moving across the table to help users organize documents,
+or a knife moving away to prevent injury as the user is inatten-
+tively about to lean against the countertop. In this paper, we build
+on the qualities of tangible interaction and focus on recognizing
+user needs in everyday tasks to enable ubiquitous yet unobtrusive
+tangible interaction. To achieve this, we introduce an architecture
+that leverages large language models (LLMs) to perceive users’
+environment and activities, perform spatial-temporal reasoning,
+and generate object actions aligned with inferred user intentions
+and object properties. We demonstrate the system’s utility provid-
+ing proactive assistance with multiple objects and in various daily
+scenarios. To evaluate our system components, we compare our
+system-generated output for user goal estimation and object action
+recommendation with human-annotated baselines, with results
+indicating good agreement.
+</p>