معرفی
Stephanie Tulk Jesso is an Assistant Professor at the School of Systems Science and Industrial Engineering at Binghamton University. Her research focuses on human-centered design principles applied to healthcare technology, human-AI interaction, and social robotics. She holds a joint appointment in systems engineering and has collaborated extensively with clinical partners to address real-world healthcare challenges. Her work emphasizes translational research, aiming to bridge gaps between technological innovation and practical healthcare applications.
Key research themes include improving patient-clinician interactions through AI tools like VizTrust, reducing hospital fall risks via human-centered robotics, and evaluating clinician involvement in AI development. She has pioneered studies on trust dynamics in human-AI/robot interactions using experimental methods in virtual environments and healthcare settings. Notable projects include co-designing AI chatbots with hospitals and exploring ethical implications of autonomous medical robotics.
Her multidisciplinary approach integrates insights from social cognition, behavioral economics, and engineering design. Recent work examines how perceived competence and context influence human-AI collaboration, with implications for healthcare system design. She actively participates in translational research initiatives to ensure technologies are developed with end-user needs central to the process.
Dr. Jesso's research trends show strong focus on: (1) Healthcare technology adoption challenges, (2) Ethical frameworks for AI/robotics in medicine, (3) Quantitative evaluation of human-technology collaboration, and (4) Co-design methodologies involving clinicians and patients. Her work frequently appears in top journals addressing human factors, medical informatics, and robotics applications.
In advising, she mentors students working at the intersection of technology and healthcare ethics. Her lab collaborates with regional hospitals and tech companies to develop practical solutions for clinical environments. Current projects include AI-driven patient monitoring systems and designing intuitive interfaces for collaborative surgical robots.
