I am a human-centered AI researcher who studies how people think, learn, reason, and adapt in increasingly AI-mediated environments. My research sits at the intersection of cognitive science, human behavior, and human-computer interaction, with a focus on understanding what makes AI-supported experiences effective, trustworthy, and meaningful.
Through a combination of empirical research, experimentation, design, and theory-building, my work explores the cognitive and behavioral processes that shape human interactions with AI. I translate these insights into the design and evaluation of AI systems, with a particular focus on promoting critical thinking, informed decision-making, and human agency. While my research has largely focused on AI technologies for learning, teaching, and information seeking, the questions that motivate my work extend more broadly to understanding and improving human-AI experiences.
Across my research, I have often found myself bridging different worlds—academia and industry, pedagogy and scalable educational technologies, and cognitive science and AI. I am especially drawn to problems that require integrating rigorous evidence with practical impact.
My work has been recognized with multiple Best Paper awards in leading learning sciences and technology venues and has reached thousands of learners globally. I hold a PhD in Information from the University of Michigan and a Bachelor's and Master's degree in Mathematics and Computing from IIT Delhi (Indian Institute of Technology Delhi).
Outside of work, I enjoy music, dancing, yoga, reading, and spending time in nature.
Postdoctoral Research Fellow, University of Texas at Austin
