Prof. David Patterson is interviewed in the podcast Recode Decode for an episode titled "Meet John Hennessy and Dave Patterson, Silicon Valley’s first disruptors."  He and Hennessy won the 2018 Turing Award--the computer science equivalent of the Nobel Prize--in recognition of their development of RISC, a more efficient computer processor found today in billions of devices.   In the episode, hosted by Kara Swisher, they talk about how they overcame resistance from their peers and made RISC a reality.  “This year, there will be 20 billion microprocessors sold,” Patterson said. “And 99 percent of those will be RISC.”

CS Prof. Eric Paulos and his graduate students in the Hybrid Ecologies Lab, Sarah Sterman, Molly Nicholas, and Christine Dierk, have created a prototype of a wearable color- and shape-changing braid called HäirIÖ.  The hair extension is built from a custom circuit, an Arduino Nano, an Adafruit Bluetooth board, shape memory alloy, and thermochromic pigments.  The bluetooth chip allows devices such as phones and laptops to communicate with the hair, causing it to change shape and color, as well as respond when the hair is touched. Their paper "Human Hair as Interactive Material," was presented at the ACM International Conference on Tangible, Embedded and Embodied Interaction (TEI) last week. They have posted a how-to guide and instructable videos which include comprehensive hardware, software, and electronics documentation, as well as information about the design process. "Hair is a unique and little-explored material for new wearable technologies," the guide says.  "Its long history of cultural and individual expression make it a fruitful site for novel interactions."

2018 EE Distinguished Alumna Andrea Goldsmith (B.A. ’86/M.S. ’91/Ph.D. ’94) has been named the 2018-19 Association for Computing Machinery (ACM) Athena Lecturer for contributions to the theory and practice of adaptive wireless communications, and for the successful transfer of research to commercial technology.  Goldsmith, who is currently the Stephen Harris Professor in the School of Engineering at Stanford,  introduced innovative approaches to the design, analysis and fundamental performance limits of wireless systems and networks. The Athena Lecturer Award, which was initiated by the ACM Council on Women in Computing (ACM-W), celebrates women researchers who have made fundamental contributions to computer science. The award carries a cash prize of $25,000, with financial support provided by Google.

In an Op-Ed piece for Medium, CS and Statistics Prof. Michael Jordan examines the limits of AI and argues for the creation of an engineering discipline encompassing data science, intelligent infrastructure (II), and intelligence augmentation (IA).   Principles of analysis and design must be applied when building planetary-scale inference-and-decision-making systems because they will have a profound effect on human lives.   "We need to realize that the current public dialog on AI — which focuses on a narrow subset of industry and a narrow subset of academia — risks blinding us to the challenges and opportunities that are presented by the full scope of AI, IA and II," he writes.

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.

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.”

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.

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.