Campus Reopening Notice

Starting June 16th, vaccinated EECS faculty, staff, and students can voluntarily return to their offices, labs and other research spaces in Cory and Soda Halls if they follow the procedures outlined in the EECS Safety Manual.  Building restrictions for non-affiliated collaborators, event attendees, and visitors will continue but be loosened over time. Cory and Soda Halls will open during the first week in August.  We are not hosting events or activities until we receive more clarity about regulatory requirements and are able to resume full operations. Most employees will return to campus on July 12th, and in-person instruction will resume for the Fall semester on August 25th, unless otherwise specified by campus. Please continue to check the University Coronavirus Updates and Resources for latest information.

Steven Cao and Stephen Tian

Steven Cao wins CRA 2021 Outstanding Undergraduate Researcher Award

Senior EECS student Steven Cao has won a Computing Research Association (CRA) 2021 Outstanding Undergraduate Researcher Award, and senior Stephen Tian was named runner-up.    The award recognizes significant contributions to computing research projects.  Cao (nominated by Prof. Dan Klein)  worked with the Berkeley Natural Language Processing group, where he developed new methods in syntactic parsing for one project, and contributed to the development and testing of new methods to provide more accurate translations between languages in another.  He also worked on developing new and provably correct blockchain protocols and on several projects related to medical imaging.  He co-authored seven papers, including first authorship on papers at three conferences.  He served as Teaching Assistant for two courses while also acting as a research mentor for the group.  Tian (nominated by Prof. Sergey Levine) demonstrated how a robotic finger with a touch sensor could perform myriad tasks using the same reinforcement learning algorithm in one project, and proposed a novel algorithm to allow a robot to achieve a variety of goals indicated as goal images in another.  He co-authored several papers at at least three conferences,  and served as a TA, while also volunteering at events for local high school students.   Ryan Lehmkuhl  (nominated by Prof. Raluca Ada Popa)a was a finalist, and Joey Hejna (nominated by Prof. Pieter Abbeel) received an honorable mention.

Randy Katz to step down as Vice Chancellor for Research

EECS Prof. and alumnus Randy Katz (M.S. '78 / Ph.D. '80) has announced that he will be retiring in June 2021, and will step down as UC Berkeley's Vice Chancellor for Research.  During his tenure as vice chancellor, Katz demonstrated a deep commitment to research excellence at Berkeley, helping to expand the annual research funding budget from $710M to over $800M by vigorously supporting major, multi-year, federally and industrially funded research centers. Philanthropic support for research on campus has also greatly expanded under his guidance with the creation of the Weill Neurohub and Bakar BioEnginuity Hub.   He established the position of a central chief innovation and entrepreneurship officer and encouraged new approaches to managing the University’s intellectual property assets, thereby generating substantial campus revenue.  He oversaw the repatriation of sacred belongings to the Native American community, and revitalized the leadership of campus Organized Research Units (ORUs); leading the campus through complex but orderly ramp-down and ramp-up of research activities in the face of major disruptions, including Public Safety Power Shutdowns, air quality emergencies, and the ongoing COVID-19 pandemic.  He also helped lead the International Engagement Policy Task Force to foster international collaboration while safeguarding the campus against undue foreign influence.  During his time in the EECS department, Katz oversaw 52  Ph.D. dissertations and has been honored with the campus Distinguished Teach Award.

Sanjit Seshia and John Canny named ACM Fellows

CS Profs. Sanjit Seshia and John Canny have been named to the 2020 class of fellows of the Association for Computing Machinery (ACM) in recognition of their fundamental contributions to computing and information technology.  Seshia, whose PhD thesis work at Carnegie Mellon on the UCLID verifier and decision procedure helped pioneer the area of satisfiability modulo theories (SMT) and SMT-based verification, and who has led the development of technologies for cyber-physical systems education based on formal methods, was cited "for contributions to formal verification, inductive synthesis, and cyber-physical systems."  Canny, who has explored roofline design for machine learning, improved inference and representations for deep learning, and is best known for creating the widely used Canny edge detector, was cited "for contributions in robotics, machine perception, human-computer interaction, and ubiquitous computing."  Prof. Emeritus Manuel Blum (now at Carnegie Mellon) was also among the 95 scientists inducted into the 2020 class who represent the top 1% of ACM members.

Ambidextrous wins SVR 'Good Robot' Excellence Award

Ambidextrous, a company co-founded in 2018 by CS Prof. Ken Goldberg, his graduate student Jeffrey Mahler (CS Ph.D. '18), and AutoLab postdocs (and ME alumni) Stephen McKinley (M.S. '14/Ph.D. '16) and David Gealy (B.S. '15), has won the inaugural Silicon Valley Robotics (SVR) ‘Good Robot’ Innovation and Overall Excellence Industry Award.  Ambidextrous utilizes an AI-enhanced operating system, Dexterity Network (Dex-Net) 4.0, that empowers versatile robots for automated e-commerce order fulfillment by allowing them to learn to pick, scan, and pack a wide variety of items in just a few hours.  This universal picking (UP) technology has enabled new levels of robotic flexibility, reliability, and accuracy.

5 questions for Michael Jordan and Rediet Abebe

CS Prof. Michael Jordan and Assistant Prof. Rediet Abebe are featured in the Center for Data Innovation's "5 Questions" series, in which data innovators discuss their research focus areas and careers.  Jordan, whose research spans computational, statistical, cognitive, and social sciences, discusses how economic concepts can help advance AI as well as the challenges and opportunities of coordinating decision-making in machine learning.  Abebe, who will begin teaching in the spring, is the co-founder of Mechanism Design for Social Good (MD4SG), an initiative that uses techniques from algorithms, optimization, and mechanism design (a field in economics that studies the mechanisms through which a particular outcome or result can be achieved), along with insights from other disciplines, to improve access to opportunity for historically underserved and disadvantaged communities.

Ken and Blooma Goldberg show you "How to Train Your Robot"

A 15-minute video version of the children's book "How to Train Your Robot," written by CS Prof. Ken Goldberg and his daughter, Blooma, has been released by the CITRIS Banatao Institute.  Aimed  at children ages six to eleven, it tells the story of a group of 4th graders who decide to build a robot to clean their workshop.  Designed to inspire girls and members of other under-represented groups to explore engineering, robotics, and coding for themselves, it's the perfect introduction for kids who are curious about robots and want to know more about how they work.    The video utilizes animatics with story narration, and is subtitled in English, Spanish, Japanese, Hindi, and simplified Chinese.   Co-written by Ashley Chase and illustrated by Dave Clegg, the book was published with support from the NSF and the the Lawrence Hall of Science in 2019.

Deep learning helps robots grasp and move objects with ease

CS Prof. Ken Goldberg is the co-author of a study published in Science Robotics which describes the creation of a new artificial intelligence software that gives robots the speed and skill to grasp and smoothly move objects, making it feasible for them to soon assist humans in warehouse environments.  He and postdoc Jeffrey Ichnowski had previously created a Grasp-Optimized Motion Planner that could compute both how a robot should pick up an object and how it should move to transfer the object from one location to another, but the motions it generated were jerky.  Then they, along with EECS graduate student Yahav Avigal and undergraduate (3rd year MS) student Vishal Satish, integrated a deep learning neural network into the motion planner, cutting the average computation time from 29 seconds to 80 milliseconds, or less than one-tenth of a second.  Goldberg predicts that, with this and other advances in robotic technology, robots could be assisting in warehouse environments in the next few years.

Jelani Nelson shrinks Big Data and expands CS learning opportunities

Since computers cannot store unlimited amounts of data, it is important to be able to quickly extract patterns in that data without having to remember it in real time. CS Prof. Jelani Nelson, who is profiled in a Q&A session for Quanta magazine, has been expanding the theoretical possibilities for low-memory streaming algorithms using a technique called sketching, which compresses big data sets into smaller components that can be stored using less memory and analyzed quickly.  He has used this technique to help devise the best possible algorithm for monitoring things like repeat IP addresses accessing a server.  “The design space is just so broad that it’s fun to see what you can come up with,” he said.  Nelson also founded AddisCoder, a free summer program which has taught coding and computer science to over 500 high school students in Addis Ababa, Ethiopia.  "A lot of the students have never been outside of their town, or their region," he said.  "So AddisCoder is the first time they’re seeing kids from all over the country, and then they’re meeting instructors from all over the world.  It’s very eye-opening for them."

Jake Tibbetts wins Bulletin of the Atomic Scientists’ 2020 Leonard M. Rieser Award

EECS grad student and alumnus Jake Tibbetts (B.S. EECS/Global Studies '20) has won the Bulletin of the Atomic Scientists’ 2020 Leonard M. Rieser Award.   Winners of the award have published essays in the Bulletin's Voices of Tomorrow column, and are selected by the Bulletin’s editorial team for recognition as "outstanding emerging science and security experts passionate about advancing peace and security in our time."  Tibbetts received the award for his article “Keeping classified information secret in a world of quantum computing,” published in the Bulletin on February 11, 2020.  “In his piece, Jake Tibbetts accomplished the kind of deep, thoughtful, and well-crafted journalism that is the Bulletin's hallmark," said editor-in-chief John Mecklin. "Quantum computing is a complex field; many articles about it are full of strange exaggerations and tangled prose. Tibbetts' piece, on the other hand, is an exemplar of clarity and precision and genuinely worthy of the Rieser Award.”  Tibbetts is a fellow at the NNSA-supported Nuclear Science and Security Consortium, and has previously worked as a research assistant at the LBNL Center for Global Security Research.  He has made contributions to the Nuclear Policy Working Group and the Project on Nuclear Gaming at Cal, and made the EECS news last year for his involvement in creating the online three-player experimental wargame "SIGNAL," which was named the Best Student Game of 2019 by the Serious Games Showcase and Challenge (SGS&C).  The Rieser Award comes with a $1K prize.

LOGiCS project receives $8.4M DARPA grant

Learning-Based Oracle-Guided Compositional Symbiotic Design of CPS (LOGiCS), a project led by Prof. Sanjit Seshia with a team that includes Profs. Prabal Dutta, Björn Hartmann, Alberto Sangiovanni-Vincentelli, Claire Tomlin, and Shankar Sastry, as well as alumni Ankur Mehta (EECS Ph.D. '12, advisor: Kris Pister) and Daniel Fremont (CS Ph.D. '20, advisor: Sanjit Seshia), has been awarded an $8.4M Defense Advanced Research Projects Agency (DARPA) grant as part of their Symbiotic Design of Cyber-Physical Systems (SDCPS) program.  CPS has applications not only for DARPA missions but also in areas such as agriculture, environmental science, civil engineering, healthcare, and transportation. SDCPS is a four-year program which aims to "develop AI-based approaches that partner with human intelligence to perform 'correct-by-construction' design for cyber-physical systems, which integrate computation with physical processes."  LOGiCS takes a novel approach that blends AI and machine learning with guidance from human and computational oracles to perform compositional design of CPS such as autonomous vehicles that operate on the ground, in the air and in water to achieve complex missions.  “Our primary role is to develop algorithms, formalisms and software for use in the design of CPS,” said Seshia. “These techniques allow designers to represent large, complex design spaces; efficiently search those spaces for safe, high-performance designs; and compose multiple components spanning very different domains — structural, mechanical, electrical and computational.”