AI model created by Johns Hopkins computer scientists imagines in-depth scenarios based on a single image to make informed decisions. Read the story on the HUB.
New Tool Blends Classic Math and AI to Tackle Complex Challenges
Learned Proximal Networks offer reliable solutions for tasks like image restoration, reconstruction of medical scans, and other estimation problems. By Dino Lencioni Researchers at the Whiting School of Engineering’s Mathematical Institute for Data Science have developed an innovative tool that merges traditional mathematical methods (like variational formulations for inverse problems) with machine learning
MINDS-affiliated Faculty Members Receive National Awards
Two faculty members from MINDS have received Chan Zuckerberg Initiative Collaborative Pairs Pilot Project Awards, highlighting Johns Hopkins researchers’ national impact in advancing interdisciplinary approaches to neurodegenerative disease and fundamental neuroscience. Adam Charles, an assistant professor in the Department of Biomedical Engineering and an affiliate of MINDS and the Center for Imaging Science, collaborates with Kaspar Podgorski from
Jeremias Sulam Receives NSF CAREER Award
The award recognizes early-stage scholars with high levels of promise and excellence. Jeremias Sulam, assistant professor in the Department of Biomedical Engineering and an affiliate of MINDS, has been named a recipient of the National Science Foundation’s Early CAREER Award, which recognizes early-stage scholars with high levels of promise and excellence. Read
Chellappa Honored with the 2024 Edwin H. Land Medal
Rama Chellappa, Bloomberg Distinguished Professor in electrical and computer engineering and biomedical engineering at Johns Hopkins University, has won the 2024 Edwin J. Land Medal, given by Optica and the Society for Imaging Science and Technology. Chellappa is recognized for significant contributions to image/video processing, computer vision, and related fields. The Edwin H. Land Medal, established
New Johns Hopkins Institute Aims to Make Baltimore an AI Hub
Johns Hopkins University has launched an ambitious endeavor that the school’s leaders say will make Baltimore a hub of the booming artificial intelligence industry. The new Data Science and Translation Institute, announced several months ago and planned for the western edge of the Homewood Campus, is expected to be “the leading
Villar receives National Science Foundation’s Early CAREER Award
Soledad Villar, assistant professor of applied mathematics and statistics, has been selected to receive the National Science Foundation (NSF)’s Early CAREER Award, which recognizes early stage scholars with high levels of promise and excellence. Villar’s five-year project “Symmetries and Classical Physics in Machine Learning for Science and Engineering,” will blend invariant theory, representation theory,
Putting trust to the test
Hopkins researchers unveil new uncertainty quantification methods in an effort to promote appropriate trust in AI use. Artificial intelligence and machine learning can help users sift through terabytes of data to arrive at a conclusion driven by relevant information, prior results, and statistics. But how much should we trust those conclusions—especially
Striking the Balance: Diving into the world of network inference
Innovative new method could make computer calculations more efficient A team of Johns Hopkins applied mathematicians has devised a new method for untangling the intricate web of connections in complex networks such as social media and the internet. The researchers’ findings, described in The Journal of Computational and Graphical Statistics, have the potential to
Brain imaging technique allows researchers to achieve more with less data
A Johns Hopkins team has developed a new algorithm that can create ‘super-scans’ of the brain Magnetic resonance imaging (MRI) uses magnetic fields to create images of the body that allow doctors to diagnose injury or illness more accurately. Susceptibility tensor imaging (STI), a specialized MRI technique, measures the magnetic susceptibility