Yiwen Yin

evenyin59@gmail.com  ·  +86 18811311775  ·  Beijing, China
Website: https://evennaire.github.io/
Email: mailto:evenyin59@gmail.com
Publications: https://evennaire.github.io/publications/

EDUCATION
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Tsinghua University   (2021 - Present)
Direct Ph.D. Candidate, Human-Computer Interaction

Tsinghua University   (2017 - 2021)
Bachelor's Degree, Computer Science and Technology

EXPERIENCE
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Pervasive Human-Computer Interaction Lab, Tsinghua University — Ph.D. Researcher, AI Agents and Human-AI Interaction   (2021 - Present)
  - Research AI agents for task automation and collaboration, focusing on interactive knowledge learning, task modeling, UI agents, and Human-AI alignment.
  - Build systems that move GUI automation beyond action replay toward interpretable cognitive task models, enabling agents to generalize user demonstrations to new goals and contexts.
  - Study how proactive AI can support creative collaboration while preserving human agency, using empirical HCI methods and system-oriented research.
  - Advised by Prof. Yuanchun Shi and Prof. Chun Yu.

PROJECTS
========

TaskMind: From Operation to Cognition — Research System   (2024 - 2025)
CHI 2025
https://evennaire.github.io/publications/
  - Proposed an LLM-enhanced GUI task automation system that identifies operation semantics and cognitive dependencies from user demonstrations.
  - Represented tasks as user-interpretable graphs that can be edited for new goals and dynamically grounded to new parameters and interface contexts.
  - Evaluated with 20 participants across predefined and customized tasks; TaskMind significantly outperformed an end-to-end LLM baseline in success rate and controllability.

TaskSense: Cognitive Chain Modeling and Difficulty Estimation — Research Framework   (2024 - 2025)
https://evennaire.github.io/publications/
  - Designed Cognitive Chain, a framework that decomposes GUI task execution into cognitive steps such as finding, deciding, and computing.
  - Developed an LLM-based pipeline to extract cognitive chains from task execution traces and estimate cognitive difficulty grounded in information theory.
  - Showed that estimated cognitive difficulty correlates with user completion time and reveals capability gaps in state-of-the-art GUI agents.

Proactive AI as a Catalyst for Creativity? — Human-AI Collaboration Study   (2025 - 2026)
CHI 2026
https://evennaire.github.io/publications/
  - Investigated proactive AI support for collaborative story writing through a Wizard-of-Oz study comparing intrusive and non-intrusive suggestion styles.
  - Found that proactive AI can accelerate writing and enhance creativity, while introducing a trade-off between AI involvement and perceived human agency.
  - Derived design guidance for proactive suggestion timing and style to better balance creativity support with user control.

IMU Ring Interaction for Eyes-Free Smartphone Operation — Accessibility Research   (2019 - 2020)
IMWUT 2020
https://evennaire.github.io/publications/
  - Co-authored a ring-based input technique enabling visually impaired users to operate smartphones while keeping the phone in a pocket.
  - Contributed to a participatory design and evaluation process for subtle one-handed gestures with audio feedback.
  - The recognition model achieved 95.5% mean accuracy across 15 gesture classes, and the interaction was evaluated as efficient, private, and easy to use.

TaskMind: Cognitive Task Modeling for GUI Automation  (https://evennaire.github.io/publications/)
A CHI 2025 system that automatically models operation semantics and cognitive dependencies from user demonstrations, then executes interpretable task graphs with LLM support for robust GUI task automation.

TaskSense: Cognitive Chain Modeling and Difficulty Estimation  (https://evennaire.github.io/publications/)
A framework for estimating GUI task difficulty from cognitive chains rather than only motor actions, supporting user behavior analysis, GUI agent evaluation, and human-agent delegation.

Proactive AI for Collaborative Story Writing  (https://evennaire.github.io/publications/)
A CHI 2026 study on proactive AI suggestion styles in creative collaboration, identifying design trade-offs between AI contribution, creativity, and perceived human agency.

SKILLS
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Research Areas: AI agents, GUI task automation, task and cognitive modeling, human-AI collaboration, Human-AI alignment, HCI user studies
Methods: LLM-based system prototyping, Wizard-of-Oz studies, controlled user studies, task trace analysis, agent evaluation, interactive system design
Languages: Chinese, English

cv.ha7ch.com/even