REU Site: Computational Sensing for Human-centered AI

Rochester Institute Of Tech, Rochester NY

Investigators

Abstract

The Research Experiences for Undergraduates (REU) Site in Computational Sensing for Human-centered Artificial Intelligence recognizes that the boundaries between human-computer interaction (HCI) and artificial intelligence (AI) are blurring and that AI is growing increasingly agile and pervasive. Thus, the next generation of computational scientists must be capable of responsibly and effectively leveraging a spectrum of sensing data from data-generating humans. With this focus, the REU Site will expand its trajectory as an attractor for highly diverse students, including women or gender minorities and students with disabilities, who will gain experience with sensing hardware and software towards transformative advances in intelligent systems focused on human behaviors and cognitive processes. Enabling diverse stakeholders early in their careers to discover how to collect, fuse, make inference with, and visualize multimodal human data helps nurture a workforce that can transform how humans and machines engage and collaborate. The research in the REU Site will address two limitations in AI: first, that underserved populations are at risk of being marginalized with the present focus on big data AI and, second, that AI trainees often lack experience with human data collection and critical thinking about human-elicited datasets. The REU Site will stimulate novel, safe ways for systems to team up with people to address society's vexing problems while remaining fair, attuned to bias, and representative of the diverse fabric of the general population. The REU Site in Computational Sensing for Human-centered Artificial Intelligence sets three ambitious and attainable goals and will publish widely on the findings at computational and Science, Technology, Engineering, and Mathematics (STEM) research and education venues: (1) to advance basic research in computational sensing for human-centered AI, integrating human study with sound AI experimentation focused on modestly-sized, inclusive datasets; (2) to develop programmatic mechanisms for aiding students to transition confidently from being taught to being mentored by research faculty and ensure their readiness for entering computer science/STEM PhD programs; and (3) to enhance and intensify our recruiting strategies with the aim of doubling Native American or Latina/o students and students with disabilities among our applicant pool, as well as reach even more demanding selection targets for exceptionally diverse cohorts. We link these objectives to four associated Site aims: (a) to offer team-based research with real-world problems that emphasize social good and impact; (b) to cost-effectively enable global awareness and skills-building, by facilitating on-campus international cultural exchange for REU cohorts; (c) to provide outreach enrichment for cohorts to learn how to communicate research broadly; and (d) to disseminate lessons learned from the REU Site to the scientific community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.