Marylee Williams

CMU researchers worked with conservation scientists to improve machine learning models trained to ID harmful, non-native species

July 29, 2025 by Marylee Williams, Carnegie Mellon University edited by Gaby Clark , reviewed by Andrew Zinin

Media Inquiries Name Aaron Aupperlee Title School of Computer Science Email Phone 412-268-9068 An AI-powered tool from Carnegie Mellon University and collaborators is helping uncover genetic clues to rare diseases, potentially accelerating diagnoses and treatments for conditions that affect only a fraction of the population.

A new AI-powered tool created by researchers at Carnegie Mellon University's School of Computer Science could change the way we manufacture and build things.

Machine learning software helps agencies make important decisions, such as who gets a bank loan or what areas police should patrol. But if these systems have biases, even small ones, they can cause real harm. A specific group of people could be underrepresented in a training dataset, for example, and as the machine learning (ML) model learns that bias can multiply and lead to unfair outcomes, such as loan denials or higher risk scores in prescription management systems.

A new AI-powered tool created by researchers at Carnegie Mellon University's School of Computer Science could change the way we manufacture and build things.

A friend's struggles with arthritis and the finger braces used to manage it inspired research by a Carnegie Mellon University student that could make it easier for patients to follow rehabilitation plans, speed up recovery times and help people manage chronic conditions.

(A) Illustration of the four regimes compared using the model. (B) Ten mutant trajectories for each of the four regimes considered.