Assistant Teaching Prof. John DeNero and CS major Tammy Nguyen are featured in a Mercury News article titled "Forget tech’s bad bros: Stanford, Berkeley boost female computing grads."   Between 2010 and 2017, UC Berkeley doubled the percentage of women receiving degrees in CS, from 11% to 22%, which runs counter to a national trend in which the proportion of women receiving degrees in computer and information sciences dropped from a high of 37% in 1984 to about 18% in 2016.  DeNero talks about some of the hurdles women must overcome if they are interested in pursuing careers in computer science.  The problems facing women in the tech industry, brought to light by the "Me Too" movement, is a concern. “It comes up even on the first day of class,” he said. “The students are very keen to talk about it, understand it. They really want to know, ‘Are all companies the same? Is this something I’m going to see everywhere?'”  Berkeley has taken a number of steps to improve the representation of women in the field.  “We have invested a lot of time and energy in figuring out what our introductory curriculum should look like, how we teach our courses, and in particular what kind of support mechanisms can we put in place to make sure that somebody who wants to study computer science has a good chance of being successful,” he said.

EE graduate student Stephen Tu (advisor: Ben Recht) has been awarded a 2018 Google Fellowship.  Google Fellowships are presented to exemplary PhD students in computer science and related areas to acknowledge contributions to their chosen fields and provide funding for their education and research. Tu's current research interests "lie somewhere in the intersection of machine learning and optimization" although he previously worked on multicore databases and encrypted query processing.  Tu graduated with a CS B.A./ME B.S. from Berkeley in 2011 before earning an EECS S.M. from MIT in 2014.

Graduate student Xue Bin “Jason” Peng (advisors Pieter Abbeel and Sergey Levine) has made a major advance in realistic computer animation using deep reinforcement learning to recreate natural motions, even for acrobatic feats like break dancing and martial arts. The simulated characters can also respond naturally to changes in the environment, such as recovering from tripping or being pelted by projectiles.  “We developed more capable agents that behave in a natural manner,” Peng said. “If you compare our results to motion-capture recorded from humans, we are getting to the point where it is pretty difficult to distinguish the two, to tell what is simulation and what is real. We’re moving toward a virtual stuntman.”  Peng will present his paper at the 2018 SIGGRAPH conference in August.

Prof. Ali Javey,  postdoc Der-Hsien Lien, and graduate students Matin Amani and Sujay Desai have built a bright-light emitting device that is millimeters wide and fully transparent when turned off.  The light emitting material in this device is a monolayer semiconductor, which is just three atoms thick.  It opens the door to invisible displays on walls and windows – displays that would be bright when turned on but see-through when turned off — or in futuristic applications such as light-emitting tattoos.  “The materials are so thin and flexible that the device can be made transparent and can conform to curved surfaces,” said  Lien. Their research was published in the journal Nature Communications on March 26.

EECS professor and UC Berkeley's new Vice Chair for Research, Randy Katz, is interviewed in Cal Alumni's California Magazine about his approach to his new job.  The article covers how one might go about creating a nurturing environment for pursuing innovative research, his predictions about future technologies, the integration of Big Data in new research, examples of some exciting projects,  and the problem of funding.

A research team led by Assistant Prof. Rikky Muller and Prof. Michel Maharbiz have created StimDust (stimulating neural dust), the smallest volume, most efficient wireless nerve stimulator to date.  The innovation adds more sophisticated electronics to neural dust (tiny, wireless sensors first implanted by Maharbiz and Prof. Jose Carmena in 2016) without sacrificing the technology’s size or safety, greatly expanding its range of applications.   Powered by ultrasound at an efficiency of 82%, and with a volume of 6.5 cubic millimeters, StimDust can be used to monitor and treat disease in a real-time, patient-specific approach.  “StimDust is the smallest deep-tissue stimulator that we are aware of that’s capable of stimulating almost all of the major therapeutic targets in the peripheral nervous system,” said Muller. “This device represents our vision of having tiny devices that can be implanted in minimally invasive ways to modulate or stimulate the peripheral nervous system, which has been shown to be efficacious in treating a number of diseases.” The research will be presented April 10 at the IEEE Custom Integrated Circuits Conference in San Diego.

Prof. Stephen Derenzo is quoted in an article for Australia’s Particle about a new material for a proposed detector of weakly interactive massive particles (WIMPs).  Derenzo is the lead author of a study published March 20 in the Journal of Applied Physics about a crystal called gallium arsenide (GaAs) which features added concentrations, or “dopants,” of silicon and boron.  This material possesses a scintillation property--it lights up in particle interactions that knock away electrons. According to Derenzo, who is a senior physicist in the Molecular Biophysics and Integrated Bioimaging Division at Berkeley Lab, the new ultrasensitive detector technology could scan for dark matter signals at energies thousands of times lower than those measurable by more conventional WIMP detectors. “It’s a privilege to be working on such an important problem in physics, but the celebration will have to wait until clear signals are seen,” he says. “It’s possible that dark matter particles are even lighter than what we can see with GaAs, and their discovery will have to wait for even more sensitive experiments.”

At the ASPLOS conference in late March, John Kubitowicz and his group from 2000 were celebrated for their paper, "OceanStore: an architecture for global-scale persistent storage." The paper was named Most Influential Paper 2018, and the authors receiving the award included David Bindel, Yan Chen, Steven Czerwinski, Patrick Eaton, Dennis Geels, Ramakrishna Gummadi, Sean Rhea, Hakim Weatherspoon, Chris Wells, and Ben Zhao, as well as Kubi, a long-time Berkeley CS faculty member. The paper was originally published in the Proceedings of the ninth international conference on Architectural support for programming languages and operating systems (ASPLOS IX). 

The Foundations of Data Science (Data 8X), which is being offered free online this spring for the first time through the campus’s online education hub, edX, is the fastest-growing course in UC Berkeley’s history.  Taught by Prof. David Wagner,  Assistant Teaching Prof. John DeNero (recipient of the 2018 Distinguished Teaching Award), and a statistics professor, Data 8X is based on CS C8: Foundations of Data Science and now has more than 1,000 students enrolling every semester.  “You’ll learn to program when studying data science — but not for the primary purpose of building apps or games,” says DeNero. “Instead, we use programming to understand the world around us.”

The Association of Computing Machinery (ACM) announced today that the winners of the 2017 ACM Turing Award are UC Berkeley's David A. Patterson and Stanford University's John L. Hennessy for "pioneering a systematic, quantitative approach to the design and evaluation of computer architectures with enduring impact on the microprocessor industry. Hennessy and Patterson created a systematic and quantitative approach to designing faster, lower power, and reduced instruction set computer (RISC) microprocessors." (ACM) Since then, computer architects have been using principles derived from their approach in a wide variety of projects for industry and academia.  Today, 99% of the more than 16 billion microprocessors produced annually are RISC processors, to be found in most smartphones, tablets, and the billions of embedded devices that comprise the Internet of Things (IoT).