faculty

Students learn to think like hackers for 'cyberwar' course

CS students enrolled in CS 194, an experimental “cyberwar” course led by Prof. Doug Tygar,  have joined forces with the white hat hackers at HackerOne, a vulnerability coordination and bug bounty platform.  This is the first time that HackerOne--which connects hackers with tech companies, private businesses and federal agencies to hunt for vulnerabilities--has partnered with a university.  Students are gaining real-world cyberwar experience. “Unless students can learn to ‘think like a hacker,’ they will not be able to effectively defend systems” says Tygar.

BRETT training with VR

EECS-affiliated startup uses virtual reality to show robots how to perform

The start-up Embodied Intelligence and its founders, Prof. Pieter Abbeel and grad students Peter Chen, Rocky Duan, and Tianhao Zhang, are the focus of two news articles: one from the New York Times titled "A.I. Researchers Leave Elon Musk Lab to Begin Robotics Start-Up," and one from Berkeley News titled "Berkeley startup to train robots like puppets."  The start-up is backed by $7 million in funding from Amplify Partners and other investors and will specialize in complex algorithms that allow machines to learn new tasks on their own through extreme trial and error.  The researchers are augmenting the algorithms with a wide range of techniques, like using virtual reality tools to show a robot how to perform a task--translating the movements into digital data.  “With our advances in machine learning, we can write a piece of software once — machine learning code that enables the robot to learn — and then when the robot needs to be equipped with a new skill, we simply provide new data.” Abbeel explains.

Case studies in forward thinking: Pieter Abbeel, Claire Tomlin, Alexandre Bayen, Ken Goldberg, Ren Ng, and Ana Claudia Arias

Six EECS faculty are profiled in a Berkeley Engineering article titled "Case studies in forward thinking."  Pieter Abbeel's vision of the future is one of goal-oriented AI, where machines are learning responsibly.  Claire Tomlin envisions a future of airspace management, where intelligent robots can quickly and safely react to dynamic situations, and maybe even deliver packages on time.   Alexandre Bayen sees transit data trends, where data is used to reveal the previously unobservable.  Ken Goldberg envisions a future of dexterous robots, where machines work together with humans to refine their respective skills sand expertise.  Ren Ng's vision of the future is one where better optics--integrating both hardware and software--improve everything from computer vision and medical diagnostics to family photos and immersive entertainment.   Ana Claudia Arias sees a future which includes electronics and diagnostic equipment that fits the body of a patient and is capable of quickly producing high resolution images, all the while providing a more comfortable experience, particularly for children.

Dan Garcia

Dan Garcia praises educators working to expand CS learning in Alabama's schools

Teaching Prof. Dan Garcia is quoted in a WBRC Fox 6 News article  which discusses how a group of Alabama teachers are working to expand computer science education opportunities for students in rural Alabama and inner-city Birmingham.  Garcia, who was part of the 2017 Alabama Teachers Computer Science Summit at The University of Alabama, praised efforts of teachers and advocates in Alabama and across the country, for their work to expand CS education. "Graduation day is the happiest day of my life, when I see all of the people who took my course four years ago, and got hooked on it," Garcia said. "They can do anything. Every single industry is being affected by data."

Pieter Abbeel, Sergey Levine & Chelsea Finn (Peter Earl McCollough)

Pieter Abbeel on building A.I. that can build A.I.

Prof. Pieter Abbeel is featured in a New York Times article titled "Building A.I. That Can Build A.I.," about how Google and others, in competition for a small pool of qualified researchers, are looking for automated ways to deal with a shortage of artificial intelligence experts.   The key might be to build algorithms that analyze the development of other algorithms, learning which methods are successful and which are not--leading to  more effective machine learning.  This could help companies build systems with artificial intelligence even if they don’t have extensive expertise in that area.  Abbeel and his team demonstrate techniques that could allow robots to learn new tasks based on what they have learned in the past. “Computers are going to invent the algorithms for us, essentially,” said  Abbeel. “Algorithms invented by computers can solve many, many problems very quickly — at least that is the hope.”

Schematic of a magnetic memory array

EECS-affiliated team develops new, ultrafast method for electrically controlling magnetism in certain metals

A UC Berkeley/UC Riverside research group that includes Prof. Jeffrey Bokor, Prof. Sayeef Salahuddin, postdoc Charles-Henri Lambert, postdoctoral fellow Jon Gorchon, and EE graduate student Akshay Pattabi have developed a new, ultrafast method for electrically controlling magnetism in certain metals, a breakthrough that could lead to greatly increased performance and more energy-efficient computer memory and processing technologies.  Their findings are published in both Science Advances (Vol. 3, No. 49, Nov. 3, 2017) under the title Ultrafast magnetization reversal by picosecond electrical pulses and Applied Physics Letters (Vol. III, No. 4, July 24, 2017) under the title Single shot ultrafast all optical magnetization switching of ferromagnetic Co/Pt multilayers.  “The development of a non-volatile memory that is as fast as charge-based random-access memories could dramatically improve performance and energy efficiency of computing devices,” says Bokor. “That motivated us to look for new ways to control magnetism in materials at much higher speeds than in today’s MRAM.”

Pramod Subramanyan and Rohit Sinha

"A Formal Foundation for Secure Remote Execution of Enclaves" wins Best Paper Award at ACM CCS 2017

A paper co-authored by postdoc Pramod Subramanyan, grad student Rohit Sinha, alumnus Ilia Lebedev (B.S. '10), alumnus and MIT Prof. Srinivas Devadas (M.S. '86/Ph.D. '88), and EECS Prof. Sanjit A. Seshia has won Best Paper Award at the 2017 ACM Conference on Computer and Communications Security (CCS).  The paper, A Formal Foundation for Secure Remote Execution of Enclaves, introduces a formal modeling and verification methodology for secure remote execution based on the notion of a trusted abstract platform.  CCS is the flagship annual conference of the Special Interest Group on Security, Audit and Control (SIGSAC) of the Association for Computing Machinery (ACM).

Three EECS-affiliated papers win Helmholtz Prize at ICCV 2017

Three papers with Berkeley authors received the Helmholtz Prize at the International Conference on Computer Vision (ICCV) 2017 in Venice, Italy.  This award honors  papers that have stood the test of time (more than ten years after first publication) and is bestowed by the IEEE technical committee on Pattern Analysis and Machine Intelligence (PAMI).    Seven papers won this year, among them: "Recognizing action at a distance," by A Efros, A Berg, G Mori and J Malik, ICCV 2003; "Discovering objects and their location in images," by J Sivic, B Russell, A Efros, A Zisserman and W Freeman, ICCV 2005; and "The pyramid match kernel: Discriminative classification with sets of image features," by K Grauman and T Darrell, ICCV 2005."

(photo Tiberio Uricchio)

Caffe team wins Everingham Prize at ICCV 2017

The Caffe team researchers ('13 alumnus and current GSR Yangqing Jia, grad student Evan Shelhamer,  '17 alumnus Jeff Donahue, '15 alumnus Sergey Karayev, grad student Jonathan Long, former postdocs Ross Girshick and Sergio Guadarrama, and Prof. in Residence Trevor Darrell) have been awarded the Mark Everingham Prize at the International Conference on Computer Vision (ICCV) 2017.  Caffe is a deep learning framework made with expression, speed, and modularity in mind,  developed by Berkeley AI Research (BAIR) and by community contributors. The Everingham Prize is bestowed by the IEEE technical committee on Pattern Analysis and Machine Intelligence (PAMI) and is given to individuals or groups "who have made a selfless contribution of significant benefit to other members of the computer vision community."  The Caffe team won "for providing an open-source deep learning framework that enabled the community to use, train and share deep convolutional neural networks. Caffe has had a huge impact, both academic and commercial. "
Mattel Kamigami (TechCrunch)

Mattel releases Dash foldable robot bugs

Mattel has launched a line of biologically inspired foldable robot bugs designed in collaboration with Dash Robots, a spin-off of the Biomimetic Millisystems Lab (BML).  The researchers at BML, under the direction of Prof. Ron Fearing, draw inspiration from nature to build more efficient robotics.  The new toys, called  Kamigami, let kids build their own robotic bugs, like mantises, ladybugs and scorpions.  Each $50 kit contains parts of a six-legged robot (with an accelerometer, gyroscope, and IR transmitter/receiver) and foldable plastic origami sheets to transform each robot into a different creature.