Invisible tagging system enhances 3D object tracking
Developed by MIT researchers, BrightMarkers are invisible fluorescent tags embedded in physical objects to enhance motion tracking, virtual reality, and object detection.
Developed by MIT researchers, BrightMarkers are invisible fluorescent tags embedded in physical objects to enhance motion tracking, virtual reality, and object detection.
The former director of LIDS was a beloved professor who blended intellectual rigor with curiosity.
A Lincoln Laboratory team visited Hill Air Force Base in Utah to determine how susceptible the latest-generation mobile network is to detection, geolocation, and jamming.
“PhotoGuard,” developed by MIT CSAIL researchers, prevents unauthorized image manipulation, safeguarding authenticity in the era of advanced generative models.
Researchers develop a machine-learning technique that can efficiently learn to control a robot, leading to better performance with fewer data.
A new technique helps a nontechnical user understand why a robot failed, and then fine-tune it with minimal effort to perform a task effectively.
EECS professor appointed to new professorship in the MIT Schwarzman College of Computing.
PIGINet leverages machine learning to streamline and enhance household robots' task and motion planning, by assessing and filtering feasible solutions in complex environments.
Researchers create a privacy technique that protects sensitive data while maintaining a machine-learning model’s performance.
“FrameDiff” is a computational tool that uses generative AI to craft new protein structures, with the aim of accelerating drug development and improving gene therapy.
PhD student Will Sussman studies wireless networks while fostering community networks.
This AI system only needs a small amount of data to predict molecular properties, which could speed up drug discovery and material development.
A new computational method facilitates the dense placement of objects inside a rigid container.
A new dataset can help scientists develop automatic systems that generate richer, more descriptive captions for online charts.
MAGE merges the two key tasks of image generation and recognition, typically trained separately, into a single system.