Brian Codding

Professor, Anthropology, College of Social and Behavioral Science​
Thematic Areas: Environment + Teaching & Learning​

Codding’s work applies machine learning and deep learning models to aid Indigenous-led conservation and restoration projects. He will work with community partners around the use of AI for understanding and addressing critical human-environmental challenges. “This will include building partnerships, inclusive training data sets, and models that responsibly represent the environmental management needs and goals of tribal members,” he said. Ultimately, he hopes to help inform restoration and conservation efforts that improve ecosystems and livelihoods in Utah.

​Isabelle Freiling

Assistant Professor, Communication, College of Humanities​
Thematic Areas: Environment + Healthcare & Wellness​

Freiling’s publications identify the challenges that algorithms pose in understanding online information environments, such as social media, where researchers don’t know who sees what. “For example, if we do not know who saw a message about climate change, we cannot measure its effect on people’s perceptions of climate change,” she said. She is advancing understanding of how this lack of access to data hinders theory building. Science communication is critical to advancing AI responsibly, she said, because it helps to reach diverse audiences affected by AI and identify ethical issues.

​Maggie French

Assistant Professor, Physical Therapy and Athletic Training, College of Health​
Thematic Area: Healthcare & Wellness​

French brings a foundational knowledge of AI and machine learning; a unique combination of skills related to neurorehabilitation, bioinformatics, and data science; and expertise related to patient-centered outcomes.​ She uses biomedical data and AI to examine complex patterns in rehabilitation and develop predictive models of outcomes, particularly for stroke recovery. “Curating a robust dataset is essential to setting the stage for my research goals and for ensuring the responsible application of AI to high-quality data in healthcare,” she said.

Chenglu Li

Assistant Professor, Educational Psychology, College of Education​
Thematic Area: Teaching & Learning​

Li has developed and investigated numerous AI models in teaching and learning, including responsible large language models (LLMs) for scalable online-learning support. He focuses on the personalization of education through fair, accountable, and transparent AI tools and learning analytics grounded in rich learning theories. “I intend to collaborate on research and grant proposals promoting educational equity through AI and organize or contribute to venues to raise awareness of AI’s societal impacts among educators and students,” Li said.

​Ana Marasović

Assistant Professor, Kahlert School of Computing, Price College of Engineering​
Thematic Area: Teaching & Learning​

Marasović develops and rigorously evaluates frontier AI models, such as LLMs, to empower people through effective AI-assisted decision-making, communication, and creativity. “I advocate for simulations where dynamic and complex interactions unfold over time, where risks are evident, and accountability is promoted, as in real-life decision making,” she said. “I will, as a faculty fellow, create a robust testbed to assess the readiness of LLMs and the human-AI collaboration methods developed by my lab, ultimately enhancing both their development and their ability to improve student learning.”

Vineet Pandey

Assistant Professor, Kahlert School of Computing, Price College of Engineering​
Thematic Area: Healthcare & Wellness​

Vineet expects to use his training in human-centered design to create AI-powered digital health platforms, such as smartphone-based tools that measure movement and cognitive performance to reduce expert load in clinical assessment and rehabilitation. “I see a massive opportunity to do foundational work bringing together two areas—human-computer interaction and AI—that have diverged over time,” he said. “Responsible AI technologies need human-centered design with multiple stakeholders to be useful in complex situations like healthcare.”

Mark Yandell

Professor, Human Genetics, Spencer Fox Eccles School of Medicine
Thematic Area: Healthcare & Wellness​

For the last eight years, Yandell has worked almost exclusively on the development of machine learning and AI tools in genomics and population health. A major focus of his work is genetic diagnosis for critically ill newborns. “Responsible AI is the common theme running through all these projects, as healthcare applications involving AI entail complex ethical and legal responsibilities,” Yandell said.

Distinguished Visitor

In the first cycle of applications, the One-U RAI Faculty Engagement and Executive committees recommended one distinguished visitor from an applicant pool of six. This program supports extended visits by leading researchers. It covers travel costs and provides an honorarium tied to the length of stay. One-U RAI expects to invite three to five visitors each year and is accepting applications by Jan. 1 for the next cycle. Read more.

David Danks

Professor of Data Science, Philosophy, and Policy
University of California, San Diego
Thematic Area: Healthcare & Wellness

Danks’s research ranges broadly across data science, AI, ethics, policy, cognitive science, and more. He develops AI algorithms and systems, primarily around causal learning and reasoning for biomedical and social challenges. Danks works extensively on AI governance, particularly methods, policies, and education to produce trustworthy, responsible AI systems. He also conducts philosophical research on the nature of AI responsibility, trustworthiness, and related concepts.

“I actively work to translate this research for the public benefit, whether through my service on committees such as the National AI Advisory Committee, or public speaking and writing for non-academic offices,” Danks said. “My research, teaching, and service are all closely aligned with the mission of One-U RAI. I am excited about the possibility of being a distinguished visitor. I have no doubt that I will find many connections across the University of Utah. I have been impressed by all of the people I have met there.”

Danks’s first visit will include a distinguished lecture​. He’ll return to identify clear research questions and resources,​ and again to kick off research collaborations​.