A National Science Foundation grant will help University of Delaware researchers create more secure computer chips by using AI and other tools to predict and counter hardware threats early in design.

Los Angeles, CA – November 14, 2025 -   The Terasaki Institute for Biomedical Innovation (TIBI) is pleased to announce its collaboration with the California Institute of Technology (Caltech) on a newly awarded $2.8 million Discovery Stage Research (DISC0) grant from the California Institute for Regenerative Medicine (CIRM).

Space manipulators play an important role in the on-orbit services and planetary surface operation whose reliability is a key issue. In the extreme environment of space, space manipulators are susceptible to a variety of unknown disturbances. Since it is difficult for the manipulator to be repaired immediately, once it fails, it will mean that it cannot complete the mission as expected, which may cause serious losses and dangers. How to have a resilient guarantee, i.e., the manipulator’s ability to resiliently recover and continue to complete tasks, in failure or disturbance is the core capability of its future development. The motion planning unit is used as the computing terminal of the manipulator’s joint motion trajectory. Compared with traditional motion planning, learning-based motion planning has gradually become a hot spot in current research. However, no matter what kind of research ideas, the single robotic manipulator is studied as an independent agent, making it unable to provide sufficient flexibility under conditions such as external force disturbance, observation noise, and mechanical failure.

Melanoma is the deadliest form of skin cancer, responsible for thousands of deaths each year; but early detection can dramatically increase survival rates. Now, scientists have developed an advanced artificial intelligence (AI) model that can detect melanoma more accurately by combining skin images with patient metadata. The study achieved 94.5% accuracy, marking a breakthrough in AI-powered early detection of melanoma, thereby advancing smart healthcare systems.

This comprehensive review systematically links ML’s application in metal AM quality control, from multi-physics field prediction to real-time closed-loop control. It offers a novel roadmap for researchers, demonstrating how ML can decode complex process-structure-property relationships and enable adaptive, intelligent manufacturing.