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.

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.”

Ruzena Bajcsy wins 2021 IEEE Medal For Innovations In Healthcare Technology

EECS Prof. Ruzen Bajcsy has won the 2021 Institute of Electrical and Electronics Engineers (IEEE) Medal For Innovations In Healthcare Technology.  The award is presented "for exceptional contributions to technologies and applications benefitting healthcare, medicine, and the health sciences."  Bajcsy, who has done seminal research in the areas of human-centered computer control, cognitive science, robotics, computerized radiological/medical image processing and artificial vision, was cited “for pioneering and sustained contributions to healthcare technology fundamental to computer vision, medical imaging, and computational anatomy.” In addition to her significant research contributions, Bajcsy is also known for her leadership in the creation of the University of Pennsylvania's General Robotics and Active Sensory Perception (GRASP) Laboratory, globally regarded as a premiere research center.  She is especially known for her comprehensive outlook in the field, and her cross-disciplinary leadership in successfully bridging the once-diverse areas of robotics, artificial intelligence, engineering and cognitive science.  EECS Prof. Thomas Budinger previously received the Health Care Innovations medal in 2018.

Michael Jordan wins 2021 AMS Ulf Grenander Prize

CS Prof. Michael I. Jordan has been awarded the 2021 American Mathematical Society (AMS) Ulf Grenander Prize in Stochastic Theory and Modeling.   The prize, which was established in 2016, recognizes "exceptional theoretical and applied contributions in stochastic theory and modeling." It is awarded for "seminal work, theoretical or applied, in the areas of probabilistic modeling, statistical inference, or related computational algorithms, especially for the analysis of complex or high-dimensional systems." Jordan, who has a split appointment in Statistics, was cited for "foundational contributions to machine learning, especially unsupervised learning, probabilistic computation, and core theory for balancing statistical fidelity with computation."  He is known for his work on recurrent neural networks as a cognitive model in the 1980s, formalizing various methods for approximate interference, and popularizing Bayesian networks and the expectation-maximization algorithm in machine learning.  The prize is awarded every three years, making Jordan the second recipient of the honor.

EECS 150W: Cecilia Aragon

2013's CS Distinguished Alumna, Cecilia Aragon (M.S. '87/Ph.D. '04, advisors: Shankar Sastry and Marti Hearst), the first Latina pilot on the United States Unlimited Aerobatic Team, and the first Latina full professor at the University of Washington, is the subject of an EECS 150W profile by Sheila Humphreys.  The child of immigrants, Aragon dreamed of one day becoming a professor.  By the time she had earned her Master's degree , however, her self-confidence had taken a beating from years of racist and sexist antagonism, and she needed to take some time off. She learned to fly, joined the US Unlimited Aerobatic Team, and helped bring home a world championship medal.  She returned to Berkeley invigorated, and became an expert in human-centered data science.  She currently holds multiple appointments at the University of Washington, remains actively engaged in efforts to support women and other underrepresented groups in computing, and has recently published a memoir.   Learn more about Cecilia's journey.