News

In light of the ongoing coronavirus (COVID-19) situation, we have decided to close our administrative offices starting Monday, March 16, through Sunday, March 29.  EECS administrative reception offices will be closed (253 Cory Hall and 387 Soda Hall).  Class are being held remotely.  All events in Cory and Soda Halls are cancelled and staff will be working remotely during these two weeks.

Olivia Hsu to give speech at national IEEE-HKN virtual graduation celebration

EECS alumna Olivia Hsu (B.S. '19) will be giving a speech at the national 2020 IEEE-Eta Kappa Nu (HKN) virtual graduation celebration on Saturday, May 30, 2020.  Hsu is the winner of the 2019 IEEE-HKN Alton B. Zerby and Carl T. Koerner Outstanding Electrical or Computer Engineering Student of the Year Award, and is the representative for the Mu (Berkeley) Chapter, which has won the IEEE-HKN Outstanding Chapter Award every year since 2001.   While at Berkeley, Hsu co-founded the student group Space Technologies at Cal (STAC) and won the 2019 EECS Arthur M. Hopkin award, which recognizes outstanding EE undergraduates who "demonstrate seriousness of purpose and high academic achievement."  She is currently pursuing a Ph.D. at Stanford with a focus on computer architecture, digital circuits, and computer systems.  IEEE-HKN will host the event for the first time this year in place of the campus commencement ceremonies which  have been cancelled nationwide. 

Celebrate 2020 EECS Graduates on Tuesday, May 19

The College of Engineering will be hosting a Celebration of Graduates on Tuesday, May 19, 2020.   The site will go live at 9 a.m. and visitors will be allowed to engage with the content as they wish.  The online, self-guided program is intended to acknowledge and celebrate our graduates’ accomplishments and will include recorded video remarks from the dean, department chairs and other speakers, as well as personalized slides for each graduate.   Plans for a formal graduation ceremony will be announced at a later date.  Congratulations messages to graduates posted on social media using the hashtag #becelebration2020 will appear on the celebration site.   Contact bears@berkeley.edu for more information.

EECS researchers discover ferroelectricity at the atomic scale

A team of researchers led by EE Prof. Sayeef Salahuddin and his graduate student, Suraj Cheema, have managed to grow an ultra-thin material on silicon that can power tiny electronic devices at the atomic scale.  Prior to this fundamental breakthrough, the thinnest conventional material that could demonstrate stable ferroelectricity was 3 nanometers thick.  The new ultrathin material, made of doped hafnium oxide just 1 nanometer thick (equivalent to the size of two atomic building blocks), can demonstrate even stronger ferroelectricity than material several times thicker.  This means it can efficiently power increasingly smaller devices, including memory and logic chips, batteries and sensors, with lower amounts of energy.  The findings were published in the April 22 issue of Nature.

Andrea Goldsmith named dean of engineering at Princeton University

EECS alumna Andrea Goldsmith (B.A. ’86/M.S. ’91/Ph.D. ’94, advisor: Pravin Varaiya), who was named Berkeley EE Distinguished Alumna in 2018, has been named dean of Princeton University’s School of Engineering and Applied Science.  Goldsmith has been a professor of electrical engineering at Stanford since 1999 and is a leader in the fields of information theory and communications. She helped lay the mathematical foundations for increasing the capacity, speed and range of wireless systems, and among her 29 patents are many inventions central to cell phone and Wi-Fi networks.  Earlier this month, Goldsmith became the first woman to win the Marconi Prize, said to be the highest honor in telecommunications research.  She has also been active in efforts to increase diversity in STEM fields and is the founding chair of the IEEE Board of Directors Committee on Diversity, Inclusion and Ethics.  When she starts her tenure as dean in September, she will oversee a school comprising six departments and four research centers, including new initiatives in bioengineering, data science and robotics, among others.

Chenming Hu took transistors into the third dimension to save Moore's Law

EECS Prof. and alumnus Chenming Hu (M.S. '70/Ph.D. '73) is the subject of an IEEE Spectrum article titled "The father of FinFets: Chenming Hu took transistors into the third dimension to save Moore's Law" (Volume: 57 , Issue: 5 , May 2020).    Hu devised a way around a limitation in semiconductor design that threatened to keep transistors from getting smaller by innovating a technology called FinFET (Fin Field-Effect Transistors) in the IEEE Spectrum.  His idea was to raise  the channel through which current flows so that it sticks out above the surface of the chip like the fin of a shark.   Hu, who extended manufacturers' ability to miniaturize chips beyond what was expected by decades, won the National Medal of Technology and Innovation in 2016 and the IEEE Medal of Honor in 2020.   The article charts Hu's winding career path, the nature of his research and the impact of his contributions.



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."

Low-cost, readily deployable respirators could help frontline healthcare workers

EECS Associate Profs. Prabal Dutta and Robert Pilawa-Podgurski have embarked on a project with doctors at UCSF to develop cost-effective powered air-purified respirators (PAPR) that will offer greater protection for healthcare workers from the coronavirus during higher risk medical procedures.  They are using an approach originally proposed to them by Oakland resident Lakin Moser, to explore a do-it-yourself PAPR concept that would be medically acceptable, inexpensive to build and rapidly scalable for regional, national and global needs.  Their prototype, which is made with a combination of off-the-shelf components and custom electronic circuits and mechanical parts sourced from Bay Area manufacturers and major electronics distributors, will cost $200 per unit--ten times less than standard devices--and can be manufactured at scale in weeks. “A key aspect of the design was to source components that are widely available, and to provide modularity to enable swap-in of alternatives if supply chain issues arise,” said Pilawa-Podgurski.  The latest version of their prototype, which was built in the team's garages and basements,  is currently undergoing usability testing at UCSF Medical Center to ensure that it meets clinical standards.  The team plans to post their design on the web as soon as it is finalized for production.

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.