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Xuan Wang received a five-year, $640,000 Faculty Early Career Development (CAREER) award from the National Science Foundation (NSF) to ask: What if a robot could learn from another robot's experience?

George Mason’s AI in Public Health Summit examined what artificial intelligence could mean for health equity, education, and the future workforce.

A study published in the prestigious Proceedings of the National Academy of Sciences validates a decades-old hypothesis by Nobel laureate Francis Crick, with key computational work performed by researchers at ¾ÅÉ«ÊÓƵ.

By developing a new machine learning algorithm to apply variational inference to spatial statistics, Jin Hyung Lee has significantly improved high-dimensional data analysis efficiency and accuracy. The PhD candidate in the Department of Statistics received the 2024 Korean International Statistical Society Outstanding Student Paper Award for his work.

Assistant Professor Zhuangdi Zhu is collaborating with the University of Virginia’s Wajih Ul Hassan to develop scalable host-based intrusion detection systems using federated learning, enhancing cybersecurity while ensuring data privacy.

João Felipe Sousa Pereira’s research on solar flares could one day help us safeguard astronauts and technology in space and even prevent widespread blackouts here on Earth. Pereira, a second-year Physics PhD student at George Mason, is studying solar physics with the goal of uncovering the precise triggers of solar flares. 

George Mason researcher Erdogan Kaya is part of an interdisciplinary research team that is planning to introduce elementary students to the foundational concepts of artificial intelligence (AI) and machine learning using educational robotics.

From degrees in engineering and geoinformation science to her current role as a director in AI and machine learning, Adjunct Professor Aisha Behr brings a wealth of industry experience to the classroom, inspiring students with real-world knowledge and innovative teaching methods. 

¾ÅÉ«ÊÓƵ has been awarded funding from the National Institutes of Health through its Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) program.

A new study from the College of Public Health, led by Professor Janusz Wojtusiak and Health Services Research doctoral candidate Ghaida Alsadah explores the use of machine learning to predict social isolation among caregivers of individuals with Alzheimer’s Disease and related disorders.