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."

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.

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 Prof. Bin Yu is the subject of a 150W profile by the Department of Statistics, where she holds a joint appointment. Yu found refuge from the tumult of Mao Tse Tung's Cultural Revolution in the orderly tables of a math textbook.  Although she placed first in the math section of the graduate school entrance exam, she failed to be accepted as a pupil by the professor she hoped to work with at Peking University because she was a woman.  As a result of this difficult rejection, she switched to studying probability and statistics, where a new world of new opportunities opened to her.  The profile covers Bin Yu's journey from her childhood in China to her days as a graduate student at UC Berkeley,  a career in both academia and industry on the east coast, her return to Berkeley as a professor, and her important contributions to the field of data science.  150W is the year-long celebration of 150 years of women at UC Berkeley.