Michael Laskey talks DART in Robohub podcast

EECS graduate student Michael Laskey (advisor: Ken Goldberg) is interviewed by Audrow Nash for a Robohub podcast titled "DART: Noise injection for robust imitation learning."  Laskey works in the AUTOLAB where he develops new algorithms for Deep Learning of robust robot control policies and examines how to reliably apply recent deep learning advances for scalable robotics learning in challenging unstructured environments.  In the podcast, he discusses how DART relates to previous imitation learning methods, how this approach has been used for folding bed sheets, and on the importance of robotics leveraging theory in other disciplines.

Lea Kissner leads Google's internal privacy strike force

EECS alumna Lea Kissner (B.S. '02) is the subject of a Gizmodo article describing her visit to a class at Berkeley this week where she discussed her job as a Principal Engineer at Google leading the security and privacy teams for infrastructure and social products.  One team of 90 employees with different backgrounds and skill sets, called NightWatch, reviews almost all of the products that Google launches for potential privacy flaws.  The article also covers some of the obstacles she has faced and her involvement chairing a discussion topic on Practical Privacy Protection at the OURSA conference in San Francisco today. “I want to tell people things we’ve learned. I want to build the world I want to live in, and the world I want to live in includes things like products being designed respectfully of users and systems being designed respectfully for users. I don’t think everybody has to learn everything the hard way,” Kissner tells me later. Then, the mathematician in her kicks in and she adds, “It’s very inefficient if nothing else.”

Allan Jabri named 2018 Soros Fellow

CS graduate student Allan Jabri has been named a 2018 Paul & Daisy Soros Fellow.   Soros Fellowships are awarded to outstanding immigrants and children of immigrants from across the globe who are pursuing graduate school in the United States.  Recipients are chosen for their potential to make significant contributions to US society, culture, or their academic fields, and will receive up to $90K in funding over two years.  Jabri was born in Australia to parents from China and Lebanon and was raised in the US.   He received his B.S. at Princeton where his thesis focused on probabilistic methods for egocentric scene understanding, and worked as a research engineer at Facebook AI Research in New York before joining Berkeley AI Research (BAIR).  He  is interested in problems related to self-supervised learning, continual learning, intrinsic motivation, and embodied cognition. His long-term goal is to build learning algorithms that allow machines to autonomously acquire visual and sensorimotor common sense. During his time at Berkeley, he also hopes to mentor students, contribute to open source code projects, and develop a more interdisciplinary perspective on AI.

Stephen Tu wins Google Fellowship

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.

Making computer animation more agile, acrobatic — and realistic

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.

Atomically thin light emitting device opens the possibility for ‘invisible’ displays

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.

5 questions for Randy Katz

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.

Research breakthrough StimDust is the smallest volume, most efficient wireless nerve stimulator to date

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.

A step forward in Stephen Derenzo's search for dark matter

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.”

John Kubiatowicz and Group's (Circa 2000) Paper Named Most Influential at ASPLOS 2018

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).