Princeton researchers have uncovered new rules governing how objects absorb and emit light, fine-tuning scientists' control over light and boosting research into next-generation solar and optical devices.

In a pair of papers published earlier this year, researchers showed how to start with a simple design for an AI network, grow the network by adding artificial neurons and connections, then prune away unused portions leaving a lean but highly effective final product. Growing and pruning results in software that requires a fraction of the computational power, and so uses far less energy, to make equally good predictions about the world. Constraining energy use is critical in getting this kind of advanced AI—called machine learning—onto small devices like phones and watches.

The National Academy of Inventors has named Princeton's James Sturm among 168 fellows for 2019.

The School of Engineering and Applied Science honored 16 graduate students for their performance "at the highest level as scholars and researchers." Four of those students came from electrical engineering.

Three graduate students in electrical engineering received the annual Yan Huo Fellowship, supporting their research into technology for e-commerce, healthcare, security and high-performance computing.

Despite the dizzying array of smart technologies now defining modern life, at a deep level the programs running these gadgets come in two basic kinds: those that can steer a car and those that can finish a sentence. But in seeking smarter medical technologies (those that can detect a tumor), Princeton researchers have developed a third way forward that makes artificial intelligence work better for human health.

Undergraduate researchers in electrical engineering have innovated on a new hybrid imaging technology, with potential applications in biomedical sensing, laboratory imaging and short-distance wireless communication.

To keep up with the explosive demands of the modern Internet, data centers must process more power with greater efficiency in less space. Reducing the footprint of that demand lies at the heart of the matter for Ping Wang, graduate student in electrical engineering at Princeton, who was recently awarded a top prize at one of the world's leading conferences on power electronics.

Machines' ability to learn by processing data gleaned from sensors underlies automated vehicles, medical devices and a host of other emerging technologies. But that learning ability leaves systems vulnerable to hackers in unexpected ways, researchers at Princeton University have found.

Princeton University has announced the creation of the Princeton Quantum Initiative to foster research and training across the spectrum from fundamental quantum science to its application in areas such as computing, sensing and communications.

Last year, Princeton researchers identified a disturbing security flaw in which hackers could someday exploit internet-connected appliances to wreak havoc on the electrical grid. Now, the same research team has released algorithms to make the grid more resilient to such attacks.