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.

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.

Two papers co-authored by Berkeley EECS authors won ACM SIGPLAN Distinguished Paper Awards at the Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA) 2019.  "Duet: An Expressive Higher-Order Language and Linear Type System for Statically Enforcing Differential Privacy" co-authored by Prof. Dawn Song (Ph.D. '02, advisor: Doug Tygar), graduate student Lun Wang, undergraduate researcher Pranav Gaddamadugu, and alumni Neel Somani (CS B.A.  '19), Nikhil Sharma (EECS B.S. '18/M.S. '19),  and Alex Shan (CS B.A. '18), along with researchers in Vermont and Utah, and "Aroma: Code Recommendation via Structural Code Search" co-authored by Prof. Koushik Sen (along with authors at Facebook and UC Irvine), won two of the five honors awarded at the top programming language conference, part of the ACG SIGPLAN conference on Systems, Programming, Languages, and Applications: Software for Humanity (SPLASH) in October.  

A computer algorithm co-developed by Vision Group alumnus Weicheng Kuo (Ph.D. '19), post doc Christian Hӓne, their advisor Prof. Jitendra Malik, and researchers at UCSF, bested two out of four expert radiologists at finding tiny brain hemorrhages in head scans, an advance that one day may help doctors treat patients with traumatic brain injuries, strokes and aneurysms.  The algorithm found some small abnormalities that the experts missed, noted their location within the brain, and classified them according to subtype.  The researchers used of a type of deep learning known as a fully convolutional neural network (FCN) to train the algorithm on a relatively small number of images that were packed with data.  Each small abnormality was manually delineated at the pixel level. The richness of this data — along with other steps that prevented the model from misinterpreting random variations, or “noise,” as meaningful — created an extremely accurate algorithm.

EECS Prof. Stuart Russell has penned a New York Times Op-Ed titled "How to Stop Superhuman A.I. Before It Stops Us," in which he explains why we need to design artificial intelligence that is beneficial, not just smart.  "Instead of building machines that exist to achieve their objectives," he writes, we need to build "machines that have our objectives as their only guiding principle..."   This will make them "necessarily uncertain about what these objectives are, because they are in us — all eight billion of us, in all our glorious variety, and in generations yet unborn — not in the machines."  Russell has just published a book titled "Human Compatible: Artificial Intelligence and the Problem of Control" (Viking , October 8, 2019).

Two EECS faculty and one alumnus are the authors with the most number of papers accepted to the upcoming 2019 Thirty-third Conference on Neural Information Processing Systems (NeurIPS), one of the most popular and influential AI conferences in the world.  CS Prof. Sergey Levine took the top spot with 12 papers, alumnus Francis Bach (Ph.D. '05, advisor: Michael Jordan) was the second most prolific contributor with 10 papers, and Prof. Pieter Abbeel placed third with nine.  Only one in five of the 6,743 papers submitted to the conference this year were accepted.  Registration to be one of the 8,000 attendees at  last year's NeurIPS (formerly NIPS) conference sold out in 12 minutes.  A lottery has been implemented for this year's conference, which will take place in December.

Berkeley researchers, including EECS Prof. Ming Wu and his former postdoc Youming Wang,  have created the fastest silicon-based, programmable two-dimensional optical phased array, built on micro-electro-mechanical systems (MEMS).  They achieved a resonance frequency of 55 kilohertz that corresponded to a response time of 5.7 microseconds, almost 1,000 times faster than a traditional optical phased array built on liquid crystal. With a large array of 25,600 pixels packed onto a chip that is 3.1 by 3.2 millimeters, the device can also capture very high-resolution images of its surroundings and lead to cheaper and more efficient medical-imaging devices, optical communications and holographic televisions, as well as more robust LiDAR sensors for self-driving cars.  "Being able to program these chips allows us to go beyond scanning, we can program our arrays to be more like human eyes. This allows us to generate and perceive arbitrary patterns like our eyes do; we can track individual objects instead of just rotating scanning,” said Wu.

EECS students, postdocs, alumni, and faculty were front and center at the 28th USENIX Security Symposium in Santa Clara last week.  In addition to the Test of Time and Distinguished Paper Awards (see below), Keynote Speaker Alex Stamos (B.S. '01), previously the Chief Security Officer of Facebook, highlighted the threat model work of current ICSI postdoc Alisa Frik (advisor: Serge Egelman).  Alumnus Nicholas Carlini (Ph.D. '18, advisor: David Wagner) gave a talk on his neural networks research which was co-authored by CS Prof. Dawn Song and postdoc Chang Liu.  ICSI researchers Primal Wijesekera and Serge Egelman, and former ICSI postdoc Joel Reardon, were awarded a Distinguished Paper Award for "50 Ways to Leak Your Data: An Exploration of Apps' Circumvention of the Android Permissions System." Grad students Frank Li (advisor: Vern Paxson) and Nathan Malkin (advisors: Serge Egelman and David Wagner), received a Distinguished Paper award at the SOUPS '19 technical session for "Keepers of the Machines: Examining How System Administrators Manage Software Updates For Multiple Machines." The Zip Bomb research of alumnus David Fifield (Ph.D. '17, advisor: Doug Tygar) was also awarded a Best Paper award at the WOOT '19 technical session.

Two CS grad students, co-advised by David Culler and Raluca Popa, also made presentations.  Sam Kumar presented "JEDI: Many-to-Many End-to-End Encryption and Key Delegation for IoT" and Michael P. Andersen presented "WAVE: A Decentralized Authorization Framework with Transitive Delegation."

Graduate student Grant Ho and his co-advisors Profs. Vern Paxson and David Wagner, were honored with a Distinguished Paper Award at the 2019 USENIX Security Symposium for "Detecting and Characterizing Lateral Phishing at Scale".  In the paper, they presented "the first large-scale characterization of lateral phishing attacks, based on a dataset of 113 million employee-sent emails from 92 enterprise organizations."  Ho, Paxson, and Wagner previously won the same award at the 2017 USENIX Security Symposium for their paper "Detecting Credential Spearphishing Attacks in Enterprise Settings."