Democratizing discovery through data science

Cathy Wu, the Unidel Edward G. Jefferson Chair in Engineering and Computer Science at the University of Delaware, has been elected a 2025 Fellow of the American Association for the Advancement of Science (AAAS), one of the world's largest general scientific societies.

The class of 449 scientists, engineers and innovators was selected for their achievements across a broad range of disciplines, including research, teaching and technology, administration and science communication.

"Cathy Wu's election as an AAAS Fellow is a testament to her exceptional scholarship in data science and the impact of her work at the forefront of discovery," said Fabrice Veron, interim vice president for research, scholarship and innovation. "Her contributions have truly been transformative, and she continues to inspire colleagues and students alike through her vision, excellence and commitment to advancing knowledge."

Wu, a professor in the Department of Computer and Information Sciences, was elected "for distinguished contributions to the field of bioinformatics data science, particularly using computational biology and natural language processing for protein annotation and knowledge discovery in biomedicine."

Her work focuses on harnessing vast amounts of biomedical data to drive insights that benefit human health, including disease prediction and drug discovery and repurposing. This requires a team science approach, bringing together computational researchers developing foundational methods in natural language processing, artificial intelligence and machine learning with biomedical scientists who ask important biological questions to produce meaningful answers.

Wu's previous honors include election as a fellow of the Association for Computing Machinery, Institute of Electrical and Electronics Engineers and International Society for Computational Biology. But she considers her election as an AAAS Fellow especially meaningful, given the association's broad reach across all fields of science.

"It's a recognition not just for me, but for the whole team that we have-the faculty collaborators, the staff scientists and the students, the mentees," said Wu, who has an affiliated appointment in the Department of Biological Sciences. "It underpins our broader philosophy of making research more accessible across disciplines."

A central example of Wu's efforts to improve data accessibility is her leadership of the UniProt Consortium, widely regarded as the leading free-access knowledge base of protein sequences and functions. Wu serves as a founding principal investigator of this international project, which provides biological datasets and analysis tools to researchers worldwide. One current focus, she said, is making these data "AI-ready" by codifying complex biological information into formats that can be readily used by AI algorithms.

Beyond disseminating resources to the research community, Wu sees the broader impact of her work in training and educating the next generation. She aims to foster a rich learning environment for students and collaborators alike.

"In my mind, we are all mentors and we are all mentees," she said. "I feel very blessed that through team science I get to learn new things every single day."

That commitment to team science is reflected in two major hubs of interdisciplinary collaboration and training that Wu has established since coming to UD in 2009: the Center for Bioinformatics and Computational Biology and the Data Science Institute. She has also led the development of master's, doctoral and graduate certificate programs in bioinformatics data science.

At the national level, Wu co-chaired a meeting at the National Institutes of Health last year that brought together thought leaders to discuss knowledge graphs-approaches to organizing scientific data in graphical formats that enable data discovery and prediction. In a perspective article, she and colleagues explored how connecting biomedical data into knowledge networks, combined with AI, could accelerate discovery, while emphasizing the need for shared standards, strong validation and responsible oversight to ensure results are accurate and trustworthy.

"My dream is to have these kinds of tools available for average biologists, and even average users, to be able to ask questions and find accurate, well-informed answers," Wu said. "It's about democratizing what we are able to discover in a way that is robust, reliable and grounded in real scientific knowledge."