Human-AI coevolution: how sustained interaction between humans and AI systems reshapes both user behavior and model outputs over time

Emergent safety challenges in long-horizon human-AI interactions (e.g., trust calibration drift, subtle failure modes that only surface over extended use)

Understanding and supporting how non-technical users adopt and use AI agents, including barriers, mental models, and intervention design to improve effective use

Building and validating LLM-based user simulators to study human-AI interaction patterns, stress-test agent behaviors, and complement real human studies

Science of evaluation for AI agents: uncertainty estimation, robust benchmarking under distribution shift, and methods for assessing when AI outputs should (and shouldn't) be trusted

Genuine interest in human-AI interaction and AI's broader societal impact; motivated by questions about how people and AI systems shape each other, not just model performance

Strong curiosity about AI agents and a habit of exploring new tools hands-on; familiarity with major AI tools (e.g., ChatGPT, Claude, coding agents)

Strong programming fundamentals: comfortable building prototypes, running experiments, and wrangling data in Python

Willingness and ability to design and conduct human studies, including participant recruitment, and IRB protocols

Self-motivated and high-initiative; able to own a research direction, identify gaps and next steps, and push through ambiguity