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About

Daniel Ullman is a prominent researcher in human-robot interaction (HRI), best known for his foundational contributions to understanding trust, anthropomorphism, and social dynamics between humans and robots. His most influential work, "A Multidimensional Conception and Measure of Human-Robot Trust" (2020, 232 citations), introduced the Multi-Dimensional Measure of Trust (MDMT), a rigorous framework capturing reliable, capable, ethical, and sincere dimensions of trust that has become a standard tool in the field. Complementing this, his investigations into trust repair and trust gains and losses (144 and 108 citations, respectively) have shaped how researchers think about maintaining and restoring human confidence in robotic systems. Ullman's equally impactful work on robot appearance — including what makes robots seem "human-like" and how users envision robot design — has brought empirical rigor to questions previously left to designer intuition. Beyond trust and appearance, he has explored socially assistive robots in educational settings, demonstrating their effectiveness in emotional storytelling and narrative comprehension for children. His research spans both theoretical frameworks and practical applications, including virtual reality interfaces for robot teleoperation. With over 1,000 cumulative citations across a decade of work, Ullman stands as a defining voice in making human-robot collaboration safer, more intuitive, and more socially meaningful.