News

David Patterson featured in inaugural episode of ACM ByteCast podcast

CS Prof. Emeritus David Patterson is featured in the inaugural episode of the Association for Computing Machinery (ACM) ByteCast podcast series, released today.  The episode also features John Hennessy who, along with Patterson, won the ACM A.M. Turing Award in 2017 for their breakthrough work in RISC microprocessor architecture.  During the interview, they share their experiences, the lessons they’ve learned, and their visions for the future of computing.  The new podcast focuses on "researchers, practitioners and innovators who are at the intersection of computing research and practice."

Daniel Fremont wins ACM SIGBED Dissertation Award

Freshly-graduate CS Ph.D. student Daniel J. Fremont (advisor: Sanjit Seshia) has won the Association for Computing Machinery (ACM) Special Interest Group on Embedded Systems (SIGBED) Paul Caspi Memorial Dissertation Award for his thesis on "Algorithmic Improvisation."  The award, which was established in 2013, recognizes outstanding doctoral dissertations that significantly advance the state of the art in the science of embedded systems.  Fremont's thesis proposes a theory of algorithmic improvisation to enable the correct-by-construction synthesis of randomized systems, and explores its applications to safe autonomy.

professor ruzena bajcsy

Ruzena Bajcsy wins 2020 NCWIT Pioneer in Tech Award

EECS Prof. Ruzena Bajcsy has won the 2020 NCWIT Pioneer in Tech Award which "recognizes technical women whose lifetime contributions have significantly impacted the landscape of technological innovation, amplifying the importance of capitalizing on the diverse perspectives that girls and women can bring to the table. "   Bajcsy pioneered a new area of study within the field of robotics called Active Perception and was the first to argue that robots should be able to autonomously control the movements of their own sensors and other apparatus for interacting with their environment. She  is known for creating the  first 3D computer atlas of the human brain, which revolutionized brain surgery by allowing doctors to more accurately locate tumors.  Bajcsy also pioneered the process of elastic matching "in which computers match defined points in the human body with standardized medical images, enabling non-invasive diagnostics of the brain and other organs."  Like other winners of the award, Bajcsy serves as a role model whose legacy continues "to inspire generations of young women to pursue computing and make history in their own right."

Hany Farid is fighting back against coronavirus misinformation

CS Prof. Hany Farid is launching a major survey of people in the United States and Western Europe to determine how far COVID-19 misinformation has penetrated the population. Using Amazon’s Mechanical Turk survey software, he and his research team hope to interview thousands of people in an effort to better understand how misinformation is being distributed, consumed, and spread.  Farid will work with other researchers and social media platforms to develop strategies on how to stop misinformation before it can take hold.

Enabling robots to learn from past experiences

EECS Prof. Pieter Abbeel and Assistant Prof. Sergey Levine are developing algorithms that enable robots to learn from past experiences — and even from other robots.  They use deep reinforcement learning to bring robots past a crucial threshold in demonstrating human-like intelligence: the ability to independently solve problems and master new tasks in a quicker, more efficient manner.  An article in the Berkeley Engineer delves into the innovations and advances that allow Abbeel and Levine help robots make "good" choices, generalize between tasks, improvise with objects, multi-task, and manage unexpected challenges in the world around them.

Using machine-learning to reinvent cybersecurity two ways: Song and Popa

EECS Prof. and alumna Dawn Song (Ph.D. '02, advisor: Doug Tygar) and Assistant Prof. Raluca Ada Popa are featured in the cover story for the Spring 2020 issue of the Berkeley Engineer titled "Reinventing Cybersecurity."  Faced with the challenge of protecting users' personal data while recognizing that sharing access to that data "has fueled the modern-day economy" and supports scientific research, Song has proposed a paradigm that involves "controlled use" and an open source approach utilizing a new set of principles based on game theory.  Her lab is creating a platform that applies cryptographic techniques to both machine-learning models and hardware solutions, allowing users to keep their data safe while also making it accessible.  Popa's work focuses on using machine-learning algorithms to keep data encrypted in cloud computing environments instead of just surrounding the data with firewalls.  "Sharing without showing" allows sensitive data to be made available for collaboration without decryption.  This approach is made practical by the creation of a machine-learning training system that is exponentially faster than other approaches. "So instead of training a model in three months, it takes us under three hours.”

Pieter Abbeel and Sergey Levine: teaching computers to teach themselves

EECS Prof. Pieter Abbeel and Assistant Prof. Sergey Levine both appear in a New York Times article titled "Computers Already Learn From Us. But Can They Teach Themselves?" which describes the work of scientists who "are exploring approaches that would help machines develop their own sort of common sense."  Abbeel, who runs the Berkeley Robot Learning Lab, uses reinforcement-learning systems that compete against themselves to learn faster in a method called self-play.  Levine, who runs the Robotic AI & Learning Lab, is using a form of self-supervised learning in which robots explore their environment to build a base of knowledge.

Students create online "Coronavirus Tracker" to keep average Americans informed

CS major Jason (XiangJun) Li and a few friends have developed a website designed to provide clear, reliable, up-to-date numbers and trends on the COVID-19 outbreak "for average Americans," particularly those on mobile phones.  LiveCoronaUpdates.org, which was launched last Tuesday, uses data released by the World Health Organization and official government websites, and provides "the simplest and most intuitive dashboard for people to quickly understand the trends and assess risks."  The site includes domestic and global numbers of patients confirmed/recovered/dead, simple graphics and tables, a headline feed, and text alerts using data that is updated every 3 hours.

Women In Tech at Berkeley

The 4th Annual Women In Tech Symposium, part of the Women In Tech Initiative (WITI) will be held at UC Berkeley on Friday, March 6, 2020.  The theme will be "Reimagining Cybersecurity for All."  Many members of the EECS community will be involved, including: alumna and Prof. Dawn Song (PhD '02) - opening remarks; WITI@UC co-founder and dean of Engineering Prof. Tsu-Jae King Liu - fireside chat; Prof. Raluca Ada Popa - Panel: What’s at Stake? Global and Systemic Cyber Threats;  and CITRIS Director Prof. Costas Spanos - Athena Awards presentation. Tickets will be available until Monday, March 2nd.

Keeping classified information secret in a world of quantum computing

Computer Science and Global Studies double major, Jake Tibbetts, has published an article in the Bulletin of the Atomic Scientists titled "Keeping classified information secret in a world of quantum computing."  Tibbetts, who is a research assistant at the LBNL Center for Global Security Research and a member of the Berkeley Nuclear Policy Working Group, argues that instead of worrying about winning the quantum supremacy race against China, U.S. policy makers and scholars should shift their focus to a more urgent national security problem: How to maintain the long-term security of secret information secured by existing cryptographic protections, which will fail against an attack by a future quantum computer.  Some possible avenues include deploying honeypots to misdirect and waste the resources of entities attempting to steal classified information; reducing the deployment time for new encryption schemes; and triaging cryptographic updates to systems that communicate and store sensitive and classified information.