A UC Davis student team is one of eight teams worldwide recently selected to compete in Amazon’s 2018 Alexa Prize Challenge – an artificial intelligence competition to advance the technology behind the company’s popular social bot.
Team Gunrock includes 12 graduate students and two undergraduate students with diverse, interdisciplinary backgrounds related to artificial intelligence. Advised by Zhou Yu, an assistant professor of computer science in the College of Engineering, the team has received a $250,000 research stipend, Alexa-enabled devices and support from Amazon’s web services team to assist with their development efforts during the competition. The team also has access to Alexa’s application programming interfaces as well as additional tools, data and support from Amazon’s Alexa team.
What does the future of plant biology education and research look like? That’s the question on the mind of Siobhan Brady, associate professor of plant biology at UC Davis.
Big data approaches will be key to advances in plant biology, so students need to be trained in these areas. Unknown author/Wikipedia (CC BY 2.5)
In a Plant Physiology commentary paper, Brady, along with 37 other plant biologists from around the world, call for universities to integrate more quantitative and computational techniques into biology-oriented academic curricula. Introducing these skills early, the group advises, will help prepare tomorrow’s plant biologists for the next era of genomics research.
The Center for Integrated Computing and STEM Education at the University of California, Davis, has released version 4 of its popular C-STEM Studio software suite. In addition to free breakthrough tools for teaching math, coding, robotics and making, this major update includes expanded support with textbooks and curriculum for Lego Mindstorms NXT and EV3 robots, Raspberry Pi computers and Arduino control boards as well as Barobo Linkbots. These hardware platforms and related curriculum are seamlessly integrated in C-STEM Studio for learning math with hands-on physical computing and real-world projects.
C-STEM Studio is compatible with robots widely used in school classrooms.
As the Juno space probe approached Jupiter in June last year, researchers with the Computational Infrastructure for Geodynamics’ Dynamo Working Group were starting to run simulations of the giant planet’s magnetic field on one of the world’s fastest computers. While the timing was coincidental, the supercomputer modeling should help scientists interpret the data from Juno, and vice versa.
Video: Simulation of Jupiter’s magnetic fields
“Even with Juno, we’re not going to be able to get a great physical sampling of the turbulence occurring in Jupiter’s deep interior,” Jonathan Aurnou, a geophysics professor at UCLA who leads the geodynamo working group, said in an article for Argonne National Laboratory news. “Only a supercomputer can help get us under that lid.”
Jim Crutchfield wants to teach a machine to “see” in a new way, discovering patterns that evolve over time instead of recognizing patterns based on a stored template.
It sounds like an easy task – after all, any animal with basic vision can see a moving object, decide whether it is food or a threat and react accordingly, but what comes easily to a scallop is a challenge for the world’s biggest supercomputers.
CORI at Lawrence Berkeley Lab is one of the world’s fastest computers. It is named after Gerty Theresa Cori, the first woman to win a Nobel Prize for Physiology or Medicine. (NERSC/LBL photo)
2016 saw an unprecedented use of cyberattacks during a U.S. presidential election. According to the U.S. Department of Homeland Security and the Office of the Director of National Intelligence, the Russian government directed theft of emails and release of information in an apparent attempt to influence the election.
What does this mean for the coming year? I asked Professors Karl Levitt, Matt Bishop, Hao Chen, and Felix Wu of the UC Davis Computer Security Laboratory for some thoughts about cybersecurity in the wake of the 2016 election hack. Here’s what they had to say.
In the latest episode of the Three Minute Egghead podcast, Ilias Tagkopoulos talks about a computer model that predicts the metabolism of the bacteria Escherichia coli. While E. coli might be one of the most-studied organisms both in labs and as a cause of disease, there is still much we don’t know about it, he notes.
Tagkopoulos and his team spent two years pulling together all the data they could find on E. coli, from DNA sequences to metabolism, and assembling it into a single database. They then used computer clusters and the Blue Waters supercomputer to create their model. You can access their data here.
By Linda Vu, Lawrence Berkeley National Laboratory
Getting a better picture of connections between brain areas is the goal of a new tool developed by researchers at the U.S. Department of Energy’s Lawrence Berkeley National Laboratory and UC Davis. Their “Brain Modulyzer” software allows researchers to visualize and explore brain activity, either while a subject is performing tasks or at rest.
We’re adding a new element to the Egghead blog this month with Three Minute Egghead, a podcast about research at UC Davis. While we figure out a few details about RSS feeds and XML, I’ll be posting these audio files to the Egghead blog, usually with an accompanying blog post.
Our first piece is about two UC Davis computer scientists who are using data from the open-source programming website GitHub to learn about coder’s work habits and in particular, how multitasking affects productivity.
Study author Bogdan Vasilescu will be presenting the study at the International Conference on Software Engineering in Austin, Texas tomorrow, May 20.
Audio: Listen to a version of this story on the Three Minute Egghead podcast.
How many projects can you work on at the same time, before losing efficiency? There are many reasons to get involved in multiple projects – impress your boss, gain personal satisfaction, help out colleagues or just because you’re interested. But at some point, there must be one project too many.
“There is a limit,” said Bogdan Vasilescu, postdoctoral researcher in the DECAL lab at the UC Davis Department of Computer Science. “Multitasking fills time that’s otherwise unused, but there is a limit at four or five projects in a week.”