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When observing metal deformation at the microscopic scale, team observed foreshocks, mainshocks, and aftershocks similar to earthquakes.

With her expertise in machine learning, Alexis Battle is working to sift through vast amounts of genetic and clinical data, revealing hidden patterns that can inform treatment strategies and improve patient care.

Developed by a Johns Hopkins-led research team, the Pancreatic Tumor Segmentation Dataset may be the key for training AI models to detect pancreatic cancer early enough to make a difference in patients' survival.

Johns Hopkins researchers have created organoid-encapsulating devices to deliver high-resolution 3D recordings of heart tissue activity.

The Johns Hopkins Turbulence Database from mechanical engineering and the Phase-Field Fracture Simulation Dataset from civil and systems are two of 10 chosen by NSF to train AI

A Johns Hopkins study reveals the strengths and pitfalls of incorporating chatbots into middle and high school classrooms as a "co-tutor."

The award supports Schulman's efforts to create a programmable language for biomaterials that can bring us closer to making lab-grown organs and tissues.

New research shows how ML methods can cut costs and save lives in clinical research.

SafeTraffic Copilot, created by Johns Hopkins team, aims to provide experts with crash analyses and predictions to reduce the rising number of fatalities and injuries that happen on U.S. roads each year

Using machine learning, Johns Hopkins researchers identified healthy and unhealthy patterns based on electrical activity

Breakthrough method uses polymer coatings to engineer customized nanomaterials

Johns Hopkins professor Kamal Choudhary has created a new AI tool for materials scientists, providing accurate answers to complex questions in the field.