News

In light of the ongoing coronavirus (COVID-19) situation, we have decided to close our administrative offices starting Monday, March 16, through Tuesday, April 7, 2020.  EECS administrative reception offices will be closed (253 Cory Hall and 387 Soda Hall) and building access will be restricted to those who have card keys.  Classes are being held remotely.  All events in Cory and Soda Halls with either be cancelled or held remotely, and staff will be working remotely during this time.

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.

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.

New nonvolatile memory cells shrink circuits and speed searches

The work of Prof. Sayeef Salahuddin and grad student Ava Tan is featured in an article in the IEEE Spectrum titled "New Nonvolatile Memories Shrink Circuits That Search Fast."  Salahuddin, a ferroelectric device pioneer, has been conducting work on a new kind of content-addressable memory cell that could speed searches and enable in-memory computing.   The new nonvolatile memory, which is smaller and potentially much more dense than other experimental designs, relies on ferroelectric field-effect transistors (FeFETs), which store data as an electric polarization within the transistor.

Leon Chua wins 2020 Julius Springer Prize

Prof. Emeritus Leon O. Chua has been awarded the 2020 Julius Springer Prize for Applied Physics.  Chua has contributed to cellular neural and nonlinear networks, nanoelectronics, nonlinear circuits and systems, nonlinear dynamics, bifurcation theory, and chaos theory. In 1971, he postulated a passive component named the memristor as the 4th passive electronic device derived from fundamental considerations.  37 years later, this device--with as predicted electrical characteristics--was experimentally found by a team at HP in 2008.  The award, which recognizes researchers who have made an outstanding and innovative contribution to the field of applied physics, comes with a prize of $5K and will be presented at the Magnus-Haus in Berlin, Germany on 18 September 2020.  The presentation will be accompanied by a public lecture given by Chua.

Aditya Parameswaran and Sanjam Garg win 2020 Sloan Research Fellowships in Computer Science

Assistant Profs. Aditya Parameswaran and Sanjam Garg hav been selected 2020 Alfred P. Sloan Research Fellows in Computer Science.  These awards recognize distinguished performance by young American scientists who show "unique potential to make substantial contributions to their field."   Parameswaran develops systems for "human-in-the-loop" data analytics, and Garg's research interests are in cryptography and security.  As two of the nine UC Berkeley researchers to win the highly competitive fellowship this year, they will each receive a $75,000 award.

EECS 150W: Valerie Taylor, winner of the 2020 EE Distinguished Alumni Award

Valerie Taylor (EECS Ph.D. '91, advisor: David Messerschmitt), one of the winners of the 2020 EE Distinguished Alumni Award to be presented next week, is also the subject of our February EECS 150W profile in honor of Black History Month.  Taylor grew up in a STEM-forward family and attended Purdue before coming to Berkeley for her doctorate.    She had never seen a black woman professor before she began teaching at Northwestern University in 1992.  She is now the Director of the Mathematics and Computer Science Division of Argonne National Laboratory (where she is a Distinguished Fellow), and the Executive Director of the Center for Minorities and People with Disabilities in IT (CMD-IT).  Her honors include the CRA A. Nico Habermann Award and the  Richard A. Tapia Achievement Award.

Alvin Cheung wins VMware Early Career Faculty Award

CS Assistant Prof. Alvin Cheung has won a VMware Early Career Faculty Award.  The award recognizes recently appointed faculty "whose research interests and accomplishments seem poised to have significant impact within the industry and academia."  Cheung's research interests include program analysis, program synthesis, improving database application performance, and building large-scale data systems in general. The award comes with a $50K grant and opportunities to collaborate with VMware.

Meet the new Division of Computing, Data Science, and Society

The Berkeley data science division, which was launched in July 2019, has just announced its new name: the Division of Computing, Data Science, and Society.  The name reflects the division's broad, cross-disciplinary approach to education, and encompasses the School of Information, BIDS, the Data Science Education Program, and the Data Science Commons, as well as involvement with the departments of Statistics and EECS.  The announcement follows the arrival of the new Associate Provost for the Division, Jennifer Chayes, who took the reins in January.

EECS Remembers Jason Rossilli

The Department is sorry to share the news that EECS major Jason Rossilli passed away at his home late last week.  Jason was close with many of his peers, staff and faculty in the department.  We are deeply saddened by his loss.   News of the passing of a member of our community will no doubt impact all of us. Talking to a professional counselor can be very helpful.  Students may reach a Tang Center Counselor at 510-642-9494, or may visit them at 2222 Bancroft Way for drop-in counseling between 10am-4:30pm. ESS also has three dedicated psychologists available with drop-in counseling hours in 241 Bechtel on Tuesdays 2-4pm, and Wednesdays and Thursdays 10am-12pm.  Faculty and staff may reach a counselor through the campus Employee Assistance Program: (510) 643-7754.

Covariant-enabled robots go live

Pieter Abbeel, the co-founder, president and chief scientist of the start-up Covariant, is featured in a number of articles appearing in major publications this week.  The New York Times, the Wall Street Journal, Wired Magazine, the Verge, the MIT Technology Review, and the IEEE Spectrum all feature articles about robots trained using Covariant's AI technologies that will be deployed  to perform complex tasks in live warehouse environments in the next few years.  Covariant uses deep reinforcement learning techniques to train robots to distinguish between materials that are particularly difficult to discern through a lens, like highly reflective metallic surfaces, transparent plastics, and easily deformable surfaces like cloth and polypropylene, with an unparalleled 99% accuracy.