I am an experienced human-computer interaction (HCI) researcher who loves to use qualitative and quantitative methods. I like to understand how people think, learn, and work, design interfaces to improve their experiences, and evaluate those experiences using qualitative and quantitative methods. My goal has been to better understand how people learn about and effectively work with complex and sociotechnical systems.
I gain insight into user needs, mental models, perspectives, and workflows using methods like interviews, surveys, and think-aloud studies. I design and prototype systems based on discovered user needs. I evaluate systems through user studies, interviews, and surveys, gathering data such as how people use systems, their cognitive load, trust, and their ability to effectively evaluate the accuracy of AI systems. Throughout my research, I leverage theoretical frameworks, such as cognitive load theory and sensemaking theories, to reason about how people understand and use complex systems.
Human-centered AI, responsible AI, AI developer experience, future-of-work, developer/end-user programmer experience, mental models and learning with complex systems, mission-driven.
My work at IBM Research for the past five years has centered around human-centered, trustworthy, and responsible AI for business users and developers and the future of work. I focused on enabling appropriate trust in AI systems and the impact of generative AI on technical and non-technical workers.
As a tenure-track Assistant Professor of Computer Science at UMass Lowell for two years, I led a lab of graduate and undergraduate students researching programmer needs around learning APIs (Application Programming Interfaces).