Human Behavior Modeling

"What I cannot create, I do not understand." -- Richard Feynman.

My research advances AI systems by deepening their understanding of human behavior through the development of computational models that predict and simulate human behavior in interactive tasks. This work extends beyond task-specific expertise to encompass the underlying cognitive mechanisms involved in perception, decision-making, and motor control. By accurately modeling realistic human behaviors, these computational models form a foundation for enabling AI systems to better interpret and respond to humans in dynamic interactive environments.

Selected research projects:

[1] Chartist [website], [2] CRTypist [website], [3] Typoist [website], [4] Modeling Workflow [paper]

Human-AI Alignment

To align AI more closely with human preferences, another line of my research centers on the design of interactive systems that directly integrate human feedback into agent behavior alignment. I focus on advancing alignment processes by developing human-centered interactive interfaces that comprehensively visualize AI agent behaviors and facilitate effective and efficient user control. Leveraging visualizations and interactive interface techniques, my work empowers human users to intuitively explore and guide the behaviors of AI agents.

Selected research projects:

[1] DxHF [website], [2] Interactive RLHF [website], [3] Interactive Reward Tuning [website]

Human-AI Co-Design

My research in Human-AI co-design focuses on developing AI techniques that lower the barriers of the design process for general users to produce informative and aesthetically appealing designs. I aim to incorporate design intelligence into software like Excel and PowerPoint to improve usability.

Selected research projects:

[1] Calliope [website], [2] REIP [website], [3] Talk2Data [paper] [4] AutoClips [website]