research

UC Berkeley ranks #3 in 2017 U.S. and global CSRankings

UC Berkeley is ranked #3 overall in the U.S. and global computer science rankings (CSRankings) for 2017.  CSRankings is designed as a more meaningful and transparent alternative to the US News and World Report computer science ranking system--which is entirely reputation-based and relies on surveys sent to department heads and directors of graduate studies.  The CSRankings system is entirely metrics-based: it weighs departments by their presence at the most prestigious publication venues.    Berkeley ranked in the top 10 in all 4 fields:  Theory (1), Artificial Intelligence (3), Systems (6) and Interdisciplinary Areas (6).  And of the 26 areas ranked, Berkeley made the top 10 in 11 of them: computer vision(2), robotics(2), machine learning and data mining(3), computer security(3), cryptography(3), design automation(3), operating systems(4), natural language processing(5), software engineering(6), algorithms & complexity(7), computer networks(8).

Embodied Intelligence raises $7M in seed round

Start-up Embodied Intelligence,  founded by Prof. Pieter Abbeel and his grad students Peter Chen, Rocky Duan, and Tianhao Zhang, raised $7M in a seed round yesterday led by venture capital firm Amplify Partners.  VC firms Lux Capital, SV Angels, FreeS, 11.2 Capital, and A. Capital also supplied capital.  Embodied Intelligence is building AI software to enable robots to learn tasks performed by the user via a virtual reality headset.  It claims existing robots will be compatible with the "robot brain," which would supplant coding scripts tailored to each task.  Embodied will use the seed capital to write its first robotics applications.

Bhat and Phadte (Laura A. Oda/Bay Area News Group)

Students help debunk fake news surrounding Texas shooting

EECS junior Rohan Phadte and fellow student Ash Bhat launched their Chrome browser extension, Botcheck.me, on Halloween and it is already proving invaluable.   The app determines whether news posts on Twitter likely came from real people or were generated by a bot.  When an armed gunman attacked the congregants of a Texas church this weekend, all legitmate news accounts agreed that neither race nor religion appeared to play a role.  But a barrage of bots immediately started spreading rumors that the shooter had recently converted to Islam or was a member of Antifa.   According to a simple random sample of 1,500 political propaganda Twitter bots the students posted on their site, #texaschurchmassacre was the bot world’s third favorite hashtag on Monday, after #maga and #antifa.

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.

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).

Ash Bhat and Rohan Phadte (James Tensuan)

Rohan Phadte and Ash Bhat are doing what Twitter won't

EECS undergraduate Rohan Phadte and Interdisciplinary Studies major Ash Bhat are the subjects of a Wired article titled "The College Kids Doing What Twitter Won't," about their creation of a Google Chrome browser extension that checks whether Twitter profiles are bots.  It describes the genesis of their partnership, which they call RoBhat Labs, and their efforts to stop the proliferation of fake Twitter accounts from flooding the internet with propaganda. It also highlights the roles played by CS Assistant Prof. Joseph Gonzalez and the class Data Science 100.