Pingmei Xu
Authored Publications
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DialogLab: Authoring, Simulating, and Testing Dynamic Group Conversations in Hybrid Human-AI Conversations
Erzhen Hu
Mingyi Li
Alex Olwal
Seongkook Heo
UIST '25: Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology, ACM (2025), 210:1-20
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Designing compelling multi-party conversations involving both humans and AI agents presents significant challenges, particularly in balancing scripted structure with emergent, human-like interactions. We introduce DialogLab, a prototyping toolkit for authoring, simulating, and testing hybrid human-AI dialogues. DialogLab provides a unified interface to configure conversational scenes, define agent personas, manage group structures, specify turn-taking rules, and orchestrate transitions between scripted narratives and improvisation. Crucially, DialogLab allows designers to introduce controlled deviations from the script—through configurable agents that emulate human unpredictability—to systematically probe how conversations adapt and recover. DialogLab facilitates rapid iteration and evaluation of complex, dynamic multi-party human-AI dialogues. An evaluation with both end users and domain experts demonstrates that DialogLab supports efficient iteration and structured verification, with applications in training, rehearsal, and research on social dynamics. Our findings show the value of integrating real-time, human-in-the-loop improvisation with structured scripting to support more realistic and adaptable multi-party conversation design.
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Accelerating eye movement research via accurate and affordable smartphone eye tracking
Na Dai
Ethan Steinberg
Kantwon Rogers
Venky Ramachandran
Mina Shojaeizadeh
Li Guo
Kai Kohlhoff
Nature Communications, 11 (2020)
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Eye tracking has been widely used for decades in vision research, language and usability. However, most prior research has focused on large desktop displays using specialized eye trackers that are expensive and cannot scale. Little is known about eye movement behavior on phones, despite their pervasiveness and large amount of time spent. We leverage machine learning to demonstrate accurate smartphone-based eye tracking without any additional hardware. We show that the accuracy of our method is comparable to state-of-the-art mobile eye trackers that are 100x more expensive. Using data from over 100 opted-in users, we replicate key findings from previous eye movement research on oculomotor tasks and saliency analyses during natural image viewing. In addition, we demonstrate the utility of smartphone-based gaze for detecting reading comprehension difficulty. Our results show the potential for scaling eye movement research by orders-of-magnitude to thousands of participants (with explicit consent), enabling advances in vision research, accessibility and healthcare.
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On-device Few-shot Personalization for Real-time Gaze Estimation
Khoi Pham
Chase Riley Roberts
Dmitry Lagun
ICCV 2019 Gaze workshop
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Recent research has demonstrated the ability to estimate user’s gaze on mobile devices, by performing inference from an image captured with the phone’s front-facing camera, and without requiring specialized hardware. Gaze estimation accuracy is known to improve with additional calibration data from the user. However, most existing methods require either significant number of calibration
points or computationally intensive model fine-tuning that is practically infeasible on a mobile device. In this paper, we overcome limitations of prior work by proposing a novel few-shot personalization approach for 2D gaze estimation. Compared to the best calibration-free model [11], the proposed method yields substantial improvements in gaze prediction accuracy (24%) using only 3 calibration
points in contrast to previous personalized models that offer less improvement while requiring more calibration points. The proposed model requires 20x fewer FLOPS than the state-of-the-art personalized model [11] and can be run entirely on-device and in real-time, thereby unlocking a variety of important applications like accessibility, gaming and human-computer interaction.
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