A team of researchers, including EECS graduate students Ali Moin, Andy Zhou, Alisha Menon, George Alexandrov, Jonathan Ting and Yasser Khan, Profs. Ana Arias and Jan Rabaey, postdocs Abbas Rahimi and Natasha Yamamoto, visiting scholar Simone Benatti, and BWRC research engineer Fred Burghardt, have created a new flexible armband that combines wearable biosensors with artificial intelligence software to help recognize what hand gesture a person intends to make based on electrical signal patterns in the forearm.  The device, which was described in a paper published in Nature Electronics in December, can read the electrical signals at 64 different points on the forearm.  These signals are then fed into an electrical chip, which is programmed with an AI algorithm capable of associating these signal patterns in the forearm with 21 specific hand gestures, including a thumbs-up, a fist, a flat hand, holding up individual fingers and counting numbers. The device paves the way for better prosthetic control and seamless interaction with electronic devices.

Computer Science Assistant Prof. Rediet Abebe and Prof. and alumna Shafi Goldwasser (M.S. '81/Ph.D. '84, advisor: Manuel Blum) are slated to speak at the inaugural  24-hour virtual Women in Data Science (WiDS) conference, hosted by Stanford University on International Women's Day, March 8th.  WiDS first took shape at Stanford in 2015 as a way to inspire and educate data scientists worldwide, regardless of gender, and to support women in the field.  Abebe, who began at Berkeley this spring, is a specialist in artificial intelligence and algorithms, with a focus on equity and justice concerns.  Goldwasser, currently the Director of the Simons Institute for the Theory of Computing, is a pioneer in probabilistic encryption, interactive zero knowledge protocols, elliptic curve primality and combinatorial property testings, and hardness of approximation proofs for combinatorial problems.   Andrea Goldsmith (B.A. '86/M.S. '91/Ph.D. '94, advisor: Pravin Varaiya), the 2018 Berkeley EE Distinguished Alumna and Dean of Engineering and Applied Science at Princeton University, and Meredith Lee, the Berkeley CDSS Chief Technical Advisor, will also be speaking.  The WiDS Berkeley regional event will follow the WiDS Worldwide event, featuring additional speakers on March 9-10.   Register for the WiDS conference now!

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.

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, 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.

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.

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.

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.

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."