News

Researchers release a new algorithm that promises to help restructure the human reference genome into a more powerful—and inclusive—graph-based representation.

Data gathered using software developed by Johns Hopkins University computer scientists will have “huge implications” for understanding human health and evolution.

Johns Hopkins and Columbia University computer scientists teamed up to combat the inaccurate correlations that artificial intelligence and machine learning models learn from text data.

Johns Hopkins computer scientists demonstrate that yes-or-no questions can aid in the usability of semantic parsing models.

Researchers seek optimal choices at the ski lodge and inside the operating system.

Johns Hopkins computer scientists introduce a new method to reduce the size of multilingual language models.

Johns Hopkins researchers harness the power of machine learning to develop a first approach to X-ray-guided surgical phase recognition.

New safety tests by Johns Hopkins researchers reveal vulnerabilities of popular systems like DALL-E 2.

In collaboration with NIH and Lumo Imaging, Hopkins researchers demonstrate a computer vision framework to track the evolution of skin lesions over time in total-body photography.

Hopkins researchers unveil new uncertainty quantification methods in an effort to promote appropriate trust in AI use.

Misha Kazhdan's new Distributed Poisson Surface Reconstruction algorithm shares its computational load across PCs to deliver fast, accurate, and watertight reconstructions.

Concerned by recent trends in natural language processing research, Hopkins computer scientists emphasize the importance of user-centric evaluation methods and contextual awareness.