Solar's Big Future With Small Tech
UC Berkeley graduate student Ilan Gur and his colleagues have a profound research goal. "In the long run, we'd like to power the world," he says. A PhD candidate in the Department of Materials Science and Engineering, Gur is leveraging advances in nanoscience to develop ultra-thin and cheap solar cells that someday could be batch produced in bulk, perhaps even roll-to-roll like newspapers are printed. Flexible and durable, the cells might eventually wrap the roofs of buildings or transform a cloth automobile cover into a battery charger.
Electronic Nose that Knows
Imagine that the milk in your refrigerator could sniff itself, changing the color of the carton if its contents were spoiled, or tiny sensors throughout an airport unobtrusively screened for explosives, replacing the bulky, expensive machines that contribute to security checkpoint traffic jams. UC Berkeley graduate student Josephine Chang is building just such an "electronic nose." Fabricated with a modified inkjet printer squirting organic electronic inks, the e-nose could potentially beat the sensitivity of today's commercial gas analyzers while costing tens of dollars instead of tens of thousands.
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New Lab for DIY Web Services
In October, five robotic vehicles drove themselves more than 130 miles across the Mojave Desert, making international headlines and marking a milestone in machine learning. Related to artificial intelligence, statistical machine learning refers to methods that enable a computer to improve its performance by analyzing previous results. Now, a group of UC Berkeley researchers hope to bring the same technology into cyberspace. The new Reliable, Adaptive and Distributed systems Laboratory (RAD Lab), funded with $7.5 million from Google, Microsoft, and Sun Microsystems, is developing technology that leverages the power of statistical machine learning so that one person can create the next eBay, Amazon, or even Google, by herself.
Cool Alumni: Inspiring engineers, one student at a time
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