Campus Shutdown Notice

In light of the ongoing coronavirus (COVID-19) situation, we have decided to close our administrative offices starting Monday, March 16, 2020 until further notice.  Cory and Soda Hall are closed.  Classes are being held remotely.  All events in Cory and Soda Halls will either be cancelled or held remotely, and staff will be working remotely during this time.

Introducing the world’s thinnest, most efficient, broadest band, flat lens

EECS Assoc. Prof. Boubacar Kanté, his graduate students Liyi Hsu, Jeongho Ha and Jun-Hee Park, postdoctoral researcher Abdoulaye Ndao, and Prof. Connie Chang-Hasnain, have demonstrated a revolutionary, ultrathin and compact, flat optical lens that spans wavelengths from the visible to the infrared with record-breaking efficiencies.  Their paper, “Octave bandwidth photonic fishnet-achromatic-metalens,” published in Nature Communications, is the first time a photonic system with the entire rainbow has been proposed and demonstrated with efficiencies larger than 70% in the visible-infrared region of the spectrum.  Attempts to make traditional lenses flatter and thinner, so that they can be deployed in increasingly smaller applications, have been hampered by the way that lens curvature and thickness are used to direct light.  The Fishnet-Achromatic-Metalens (FAM) utilizes a complex “fishnet” of tiny, connected waveguides with a gradient in dimensions, which focuses light on a single point on the other side of the lens, regardless of the incident wavelength.  As the world’s thinnest, most efficient, and broadest band, flat lens, its use in applications like solar energy, medical imaging, and virtual reality, is just the beginning.  As Kanté explains, “We have overcome what was regarded as a fundamental roadblock.”  One idea for a possible implementation would be to integrate the miniature lens into microrobots being developed at the Berkeley Sensor & Actuator Center (BSAC).

Paper by Peter Mattis to be presented at ACM SIGMOD conference

A paper co-written by EECS alumnus Peter Mattis (B.S. '97) is being presented at the 2020 Association for Computing Machinery (ACM) Special Interest Group on Management of Data (SIGMOD) International Conference on Management of Data this month.  The paper, titled "CockroachDB: The Resilient Geo-Distributed SQL Database," describes a cloud-native, distributed SQL database called CockroachDB, that is designed to store copies of data in multiple locations in order to deliver speedy access.  The database is being developed at Cockroach Labs, a company co-founded in 2015 by a team of former Google employees that included Mattis, who is also the current CTO, and fellow-alumnus Spencer Kimball (CS B.A. '97), currently the company CEO.  Cockroach Labs employs a number of Cal alumni including Ceilia La (CS B.A. '00) and Yahor Yuzefovich (CS B.A. '18).

11 EECS faculty among the top 100 most cited CS scholars in 2020

The EECS department has eleven faculty members who rank among the top 100 most cited computer science & electronics scholars in the world. UC Berkeley ranked #4  in the global list of universities with the highest number of influential scholars in 2020 (35, up from 24 in 2018).  Profs. Michael Jordan, Scott Shenker, Ion Stoica, Jitendra Malik, Trevor Darrell, David Culler, Shankar Sastry, Randy Katz, Alberto Sangiovanni-Vincentelli, Lotfi Zadeh and Dawn Song all ranked in the top 100 with an H-index score of 110 or higher, a measure that reflects the number of influential documents they have authored.   Jordan ranks fourth in the world, with an H-index of 166 and 177,961 citations.  The H-index is computed as the number h of papers receiving at least h citations among the top 6000 scientist profiles in the Google Scholars database. 

Michael McCoyd uses polio history to shed light on Coronavirus vaccine in NY Times Op-Ed

CS graduate student Michael McCoyd (advisor: David Wagner) has co-authored an op-ed piece in the New York Times titled "What to Expect When a Coronavirus Vaccine Finally Arrives," which offers sobering lessons from the history of the polio vaccine. It took over 60 years from the onset of the first polio epidemic for a safe and effective vaccine to be developed and attempts to hasten the process often led to tragedy. McCoyd, who is in the Secure Computing group, says the article arose from a class he took in the J-school to learn more about fighting disinformation titled "Science Denial: Role of the Media."  When the J-school shifted focus to COVID-19 coverage, Prof. Elena Conis, an historian of vaccination, suggested story ideas for the students to pitch.  With their pitch accepted by the New York Times, McCoyd and classmate Jessie Moravek, a graduate student in environmental science, wrote what became the op-ed with Prof. Conis.

Four papers authored by EECS faculty win Test-of-Time Awards at 2020 IEEE-SP

Four papers co-authored by EECS faculty (3 of which were co-authored by Prof. Dawn Song) have won Test-of-Time awards at the IEEE Symposium on Security and Privacy today: "Efficient Authentication and Signing of Multicast Streams Over Lossy Channels," co-authored by Song (Ph.D. '02) and the late Prof. Doug Tygar (with Perrig and Canetti) in 2000, "Practical Techniques for Searches on Encrypted Data," co-authored by Song and Prof. David Wagner (with Perrig) in 2000, "Random Key Predistribution Schemes for Sensor Networks," co-authored by Song (with Chan and Perrig) in 2003, and "Outside the Closed World: On Using Machine Learning For Network Intrusion Detection" co-authored by Prof. Vern Paxson (with Sommer) in 2010.    IEEE-SP is considered the premier computer security conference and this four-fold achievement demonstrates Berkeley's preeminence in the field.

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.

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.

Researchers develop novel way to shrink light to detect ultra-tiny substances

EE Associate Prof. Boubacar Kanté and his graduate student Junhee Park have been profiled in a Berkeley Engineering article titled "Researchers develop novel way to shrink light to detect ultra-tiny substances."  They are part of a team of researchers who have created light-based technology that can detect biological substances with a molecular mass more than two orders of magnitude smaller than previously possible.  Their device, which would shrink light while exploiting mathematical singularities known as exceptional points (EP), could lead to the development of ultra-sensitive devices that can quickly detect pathogens in human blood and considerably reduce the time needed for patients to get results from blood tests. Their work was published in Nature Physics last week. “Our goal is to overcome the fundamental limitations of optical devices and uncover new physical principles that can enable what was previously thought impossible or very challenging,” Kanté said.