Rochester scientists reveal the limits of machine learning for hydrogen models
Research from the Laboratory for Laser Energetics paves the way for more accurate computer models, which are needed to understand the interior of planets and the physical properties of nuclear fusion.
A cure for blindness? A next-generation solar concentrator?
Two new Rochester fellows of the National Academy of Inventors take aim at transformative discoveries in the world of optics.
Andrew Berger honored for novel ways to monitor biological cells and tissues
The Rochester optics professor has been elected a fellow of Optica, the international society for optics and photonics.
Rochester students’ award-winning device instantly detects sepsis via sweat
Rochester undergraduates have developed a fast, noninvasive, affordable, and eco-friendly way to diagnose the life-threatening medical complication.
Joseph Eberly honored as a ‘true visionary’ in optics
Joseph Eberly, the Andrew Carnegie Professor of Physics and a professor of optics, is recognized for pioneering contributions to quantum optics theory.
Rochester mathematician wins prestigious Veblen Prize
Fayerweather Professor of Mathematic Doug Ravenel wins the prize from the American Mathematical Society for solving a geometry problem that has puzzled mathematicians for 50 years.
Brief period of ‘blindness’ is essential for vision
Rochester vision scientists uncover new information about the role of tiny “fixational” eye movements in enabling us to see clearly.
New imaging technology could buy time for pancreatic cancer patients
Tumor shrinkage is one sign of cancer treatment’s efficacy—but Rochester scientists are exploring elasticity and permeability as well.
Anxiety cues found in the brain despite safe environment
Rochester researcher Benjamin Suarez-Jimenez and his colleagues used virtual reality to better understand how anxiety effects the brain in a ‘real-world-like’ environment.
Software uses selfies to detect early symptoms of Parkinson’s disease
Rochester computer scientist Ehsan Hoque and his colleagues have harnessed machine learning to accurately identify signs of the neurological disease by analyzing facial muscles.