The Institute of People and Technology at Georgia Tech (IPaT) co-sponsored more than $70,000 in grant awards to five research projects. The other research co-sponsors were the Georgia Tech Research Institute (GTRI) and the Institute for Data Engineering and Science (IDEaS). The IDEaS grant also involved other interdisciplinary research co-sponsors at Georgia Tech. A complete list of IDEaS awardees are listed here.

“Congratulations to this year’s grant awardees, which bring together a diverse set of scholars advancing important new lines of interdisciplinary inquiry,” said Michael Best, executive director of IPaT. “The funded projects in the arts, assistive healthcare, AI, and beyond will further Georgia Tech’s impact at the intersections of people and technology.”

The goal of the IPaT/GTRI co-sponsored research and engagement grants for 2023-2024 is to promote research activities involving faculty and students from many disciplines represented in IPaT. Four winning projects were selected based on their early-stage research which have a high probability of leading to extramural funding and include a strong interdisciplinary component. Engagement grants are also designed to foster new engagements and collaborations, whether internal or external to Georgia Tech.

The goal of the IPaT/IDEaS co-sponsored research include identifying prominent emerging research directions on the topics of artificial intelligence (AI), shaping IDEaS future strategy in this initiative area, and building an inclusive and active community of Georgia Tech researchers. Proposals could include external collaborators, identifying and preparing groundwork for competing in large-scale grant opportunities in AI, and AI use in other research fields.

Congratulations to the winning project teams listed below:

Proposal title: Artificial Intelligence Based Abstract Review Assistant (AIARA)
Team members: Michael Cross, research scientist, GTRI; Paula Gomez, senior research engineer, GTRI; Mark Riedl, professor, associate director of the Georgia Tech Machine Learning Center, School of Interactive Computing
Award and sponsors: $20,000 (IPaT/GTRI)
Overview: Scientific committee members are promoting the use of artificial intelligence tools such as Google’s BARD and OpenAI’s Chat GPT to help with the blind review process to support the peer review process such as articles submitted for annual science-related conferences. Considering that the peer review process is made up of well-structured tasks that include analysis of a set number of abstract components (title, keywords, structure, outcomes, references) or paper components (the introduction, methods, results, discussion, length, clarity and structure), peer review is an excellent candidate for trained AI to address topics such as duplicate submissions, self-plagiarism, incomplete reviews, comment quality assessment, and the overall standardization of scores for the final selection of articles.

Proposal title: Toward Fairer Diagnosis and Care of Type 2 Diabetes: A Long-Term and Pipeline-Level View
Team members: Gabriel Garcia, assistant professor in the H. Milton Stewart School of Industrial and Systems Engineering; Juba Ziani, assistant professor in the H. Milton Stewart School of Industrial and Systems Engineering; Jovan Julien, postdoctoral fellow, Harvard Medical School
Award and sponsors: $16,034 (IPaT)
Overview: Type-2 Diabetes Mellitus (T2DM) is one of the most common chronic diseases in the United States, affecting about 10% of Americans. While T2DM is irreversible, its early disease stages – i.e., pre-diabetes – are reversible. Accordingly, early screening, detection, and treatment are critical to reducing the rates of progression to T2DM and mitigating the adverse effects of T2DM among those who already have it. Yet, in the United States, T2DM can often go undetected until its later stages with each missed detection stage leading to worsening health outcomes and increasing financial burden. Further, people from disadvantaged and underserved groups often face lower access to care, leading to more missed detection and greater downstream disease burden. In this research, our goal is to build a mathematical model to optimize investments across screening and treatment resources while reducing disparities across disadvantaged populations.

Proposal title: ASTRO! - Manysourcing the Design and Behavior of Future Robotic Guide Dogs
Team members: Bruce Walker, professor, School of Psychology and School of Interactive Computing
Award and sponsors: $15,375 (IPaT)
Overview: ASTRO! is an interdisciplinary collaborative project to engage many people in the ideation and creative design of future robotic guide dogs. As the technology and engineering advance towards a robotic assistant, we also must consider design and human-robot interaction issues. We will ask many people--through interviews, focus groups, and surveys--what capabilities a robotic guide should have. We will also ask how they should look and feel. We will consider how they will behave. And finally, we will investigate how humans and robotic assistants will communicate. Students in many classes at Georgia Tech and beyond will study various aspects of this research and design challenge. We will also host a weekend “design-a-thon” for ideating and brainstorming robot designs and interaction patterns, and crafting up all kinds of prototypes and mockups. The outcomes of this project will influence the design of robotic assistants, and more broadly will help us design advanced technology so it is accepted into society.

Proposal title: Data-Driven Platform for Transforming Subjective Assessment into Objective Processes for Artistic Human Performance and Wellness
Team members: Milka Trajkova, research scientist, School of Literature, Media, and Communication; Brian Magerko, professor, School of Literature, Media, and Communication
Award and sponsors: $15,000 (IPaT/IDEaS)
Overview: Artistic human movement at large, stands at the precipice of a data-driven renaissance. By leveraging novel tools, we can usher in a transparent, data-driven, and accessible training environment. The potential ramifications extend beyond dance. As sports analytics have reshaped our understanding of athletic prowess, a similar approach to dance could redefine our comprehension of human movement, with implications spanning healthcare, construction, rehabilitation, and active aging. Georgia Tech, with its prowess in AI, HCI, and biomechanics is primed to lead this exploration. To actualize this vision, we propose the following research questions with ballet as a prime example of one of the most complex types of artistic movements: 1) What kinds of data - real-time kinematic, kinetic, biomechanical, etc. captured through accessible off-the-shelf technologies, are essential for effective AI assessment in ballet education for young adults?; 2) How can we design and develop an end-to-end ML architecture that assesses artistic and technical performance?; 3) What feedback elements (combination of timing, communication mode, feedback nature, polarity, visualization) are most effective for AI- based dance assessment?; and 4) How does AI-assisted feedback enhance physical wellness, artistic performance, and the learning process in young athletes compared to traditional methods?

Proposal title: Voice+: Locating the Human Voice in a Technology-Driven World
Team members: Andrea Jonsson, assistant professor, School of Modern Languages; Stuart Goldberg, associate professor, School of Modern Languages
Award and sponsors: $3,800 (IPaT)
Overview: The Voice + Research Lab is an Interdisciplinary Voice Studies Lab that explores the human voice from a variety of perspectives and integrates knowledge and methodologies from different disciplines. It encompasses a wide range of topics related to the voice, including vocal production, vocal health, cultural and historical aspects of vocal expression, and the artistic and expressive use of the voice. Interdisciplinary voice studies aim to provide a holistic understanding of the voice and its multifaceted aspects, fostering collaboration among experts in various fields to explore sound and structures of the human voice.