{
  "username": "even",
  "header": {
    "name": "Yiwen Yin",
    "tagline": "AI Agent Researcher | Ph.D. Candidate in Human-Computer Interaction at Tsinghua University"
  },
  "personalInfo": {
    "email": "evenyin59@gmail.com",
    "phone": "+86 18811311775",
    "location": "Beijing, China"
  },
  "experience": [
    {
      "company": "Pervasive Human-Computer Interaction Lab, Tsinghua University",
      "role": "Ph.D. Researcher, AI Agents and Human-AI Interaction",
      "startDate": "2021",
      "endDate": "Present",
      "bullets": [
        "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."
      ],
      "tags": [
        "AI Agents",
        "Human-Computer Interaction",
        "GUI Automation",
        "Human-AI Collaboration",
        "LLMs"
      ]
    }
  ],
  "education": [
    {
      "school": "Tsinghua University",
      "major": "Human-Computer Interaction",
      "degree": "Direct Ph.D. Candidate",
      "startDate": "2021",
      "endDate": "Present"
    },
    {
      "school": "Tsinghua University",
      "major": "Computer Science and Technology",
      "degree": "Bachelor's Degree",
      "startDate": "2017",
      "endDate": "2021"
    }
  ],
  "projectsRecent": [
    {
      "title": "TaskMind: Cognitive Task Modeling for GUI Automation",
      "description": "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.",
      "url": "https://evennaire.github.io/publications/",
      "tags": [
        "CHI 2025",
        "GUI Agents",
        "Programming by Demonstration",
        "LLMs"
      ]
    },
    {
      "title": "TaskSense: Cognitive Chain Modeling and Difficulty Estimation",
      "description": "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.",
      "url": "https://evennaire.github.io/publications/",
      "tags": [
        "AI Agent Evaluation",
        "Task Modeling",
        "Cognitive Modeling",
        "GUI Tasks"
      ]
    },
    {
      "title": "Proactive AI for Collaborative Story Writing",
      "description": "A CHI 2026 study on proactive AI suggestion styles in creative collaboration, identifying design trade-offs between AI contribution, creativity, and perceived human agency.",
      "url": "https://evennaire.github.io/publications/",
      "tags": [
        "CHI 2026",
        "Human-AI Collaboration",
        "Creativity Support",
        "User Study"
      ]
    }
  ],
  "projectsDetailed": [
    {
      "title": "TaskMind: From Operation to Cognition",
      "type": "Research System",
      "startDate": "2024",
      "endDate": "2025",
      "url": "https://evennaire.github.io/publications/",
      "award": "CHI 2025",
      "bullets": [
        "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."
      ],
      "tags": [
        "GUI Automation",
        "LLMs",
        "Task Graphs",
        "Human-AI Interaction"
      ],
      "externalLink": {
        "label": "Publication",
        "url": "https://evennaire.github.io/publications/"
      }
    },
    {
      "title": "TaskSense: Cognitive Chain Modeling and Difficulty Estimation",
      "type": "Research Framework",
      "startDate": "2024",
      "endDate": "2025",
      "url": "https://evennaire.github.io/publications/",
      "bullets": [
        "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."
      ],
      "tags": [
        "Agent Evaluation",
        "Cognitive Modeling",
        "GUI Tasks",
        "LLMs"
      ],
      "externalLink": {
        "label": "Publication",
        "url": "https://evennaire.github.io/publications/"
      }
    },
    {
      "title": "Proactive AI as a Catalyst for Creativity?",
      "type": "Human-AI Collaboration Study",
      "startDate": "2025",
      "endDate": "2026",
      "url": "https://evennaire.github.io/publications/",
      "award": "CHI 2026",
      "bullets": [
        "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."
      ],
      "tags": [
        "Proactive AI",
        "Creativity Support",
        "Human Agency",
        "User Study"
      ],
      "externalLink": {
        "label": "Publication",
        "url": "https://evennaire.github.io/publications/"
      }
    },
    {
      "title": "IMU Ring Interaction for Eyes-Free Smartphone Operation",
      "type": "Accessibility Research",
      "startDate": "2019",
      "endDate": "2020",
      "url": "https://evennaire.github.io/publications/",
      "award": "IMWUT 2020",
      "bullets": [
        "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."
      ],
      "tags": [
        "Accessibility",
        "Wearable Interaction",
        "IMU",
        "Mobile HCI"
      ],
      "externalLink": {
        "label": "Publication",
        "url": "https://evennaire.github.io/publications/"
      }
    }
  ],
  "skills": [
    {
      "name": "Research Areas",
      "items": [
        "AI agents",
        "GUI task automation",
        "task and cognitive modeling",
        "human-AI collaboration",
        "Human-AI alignment",
        "HCI user studies"
      ]
    },
    {
      "name": "Methods",
      "items": [
        "LLM-based system prototyping",
        "Wizard-of-Oz studies",
        "controlled user studies",
        "task trace analysis",
        "agent evaluation",
        "interactive system design"
      ]
    },
    {
      "name": "Languages",
      "items": [
        "Chinese",
        "English"
      ]
    }
  ],
  "contact": [
    {
      "label": "Website",
      "url": "https://evennaire.github.io/"
    },
    {
      "label": "Email",
      "url": "mailto:evenyin59@gmail.com"
    },
    {
      "label": "Publications",
      "url": "https://evennaire.github.io/publications/"
    }
  ],
  "meta": {
    "updatedAt": "2026-05-12T10:08:08.618Z"
  }
}