EECS Prof. Emeritus Lotfi Zadeh (1921 - 2017) is being honored with a Google Doodle feature today.  In 1964, Zadeh conceived a new mathematical concept called fuzzy logic which offered an alternative to rigid yes-no logic in an effort to mimic how people see the world.  He proposed using imprecise data to solve problems that might have ambiguous or multiple solutions by creating sets where elements have a degree of membership. Considered controversial at the time, fuzzy logic has been hugely influential in both academia and industry, contributing to, among other things, "medicine, economic modelling and consumer products such as anti-lock braking, dishwashers and elevators."   Zadeh's seminal paper, "Fuzzy Sets -- Information and Control," was submitted for publication 57 years ago today.

CS Prof. Michael Jordan has co-written an article in Wired titled "The Turing Test Is Bad for Business" in which he argues that now that "computers are able to learn from data and...interact, infer, and intervene in real-world problems, side by side with humans," humans should not try to compete with them but "focus on how computers can use data and machine learning to create new kinds of markets, new services, and new ways of connecting humans to each other in economically rewarding ways."  Jordan wrote the article because many AI investors are focusing on technologies with the goal of exceeding human performance on specific tasks, such as natural language translation or game-playing. “From an economic point of view, the goal of exceeding human performance raises the specter of massive unemployment,” he said. “An alternative goal for AI is to discover and support new kinds of interactions among humans that increase job possibilities.”

CS alumni Xiaoye Sherry Li (Ph.D. '96, advisor: James Demmel) and Richard Vuduc (Ph.D. '03, advisor: James Demmel) have, along with Piyush Sao of Georgia Tech, won the 2022 Society for Industrial and Applied Mathematics (SIAM) Activity Group on Supercomputing (AG/SC) Best Paper Prize.  This prize recognizes "the author or authors of the most outstanding paper in the field of parallel scientific and engineering computing published in English in a peer-reviewed journal." Their paper, "A communication-avoiding 3D algorithm for sparse LU factorization on heterogeneous systems,” was published in 2018 in the IEEE International Parallel and Distributed Processing Symposium (IPDPS).  Li is now a Senior Scientist at Lawrence Berkeley National Laboratory (LBNL) where she works on diverse problems in high performance scientific computations, including parallel computing, sparse matrix computations, high precision arithmetic, and combinatorial scientific computing.  Vuduc, now an Associate Professor in the School of Computational Science and Engineering at Georgia Tech, is interested in high-performance computing, with an emphasis on algorithms, performance analysis, and performance engineering.

EECS alumna Hani Gomez (Ph.D. '20, advisor: Kris Pister) is the subject of a Berkeley Computing, Data Science, and Society (CDSS) profile titled "Hani Gomez, Ph.D.: Computing Pedagogy at the Nexus of Technology and Social Justice."  Gomez was born in Bolivia and earned her B.S. in EE at the University of South Carolina before coming to Berkeley for her graduate studies.  She has merged social justice and technology into a post-doc research position at Berkeley, split between EECS and the Human Contexts and Ethics (HCE) program in CDSS.  Gomez helped develop the course CS 194-100 EECS for All: Social Justice in EECS last spring, was one of three presenters in a June HCE workshop titled "Towards Social Justice in the Data Science Classroom," and serves on the EECS Anti-Racism Committee.  She says the preoccupation with perfectionism at Berkeley "doesn’t leave room [for you] to learn from your mistakes...You need to give yourself room to learn or unlearn, to grow and relearn.”

EECS alumnus Yang You (Ph.D. '20, advisor: James Demmel)  was named as one of two honorable mentions for the 2020 ACM Special Interest Group in High Performance Computing (SIGHPC) Dissertation Award.  You was selected for developing LARS (Layer-wise Adaptive Rate Scaling) and LAMB (Layer-wise Adaptive Moments for Batch training) to accelerate machine learning on HPC platforms. His thesis, “Fast and Accurate Machine Learning on Distributed Systems and Supercomputers,” focuses on improving the speed and accuracy of Machine Learning training to optimize the use of parallel programming on supercomputers.  You made the Forbes 30 Under 30 2021 Asia list for Healthcare and Science in April and is now a Presidential Young Professor of Computer Science at the National University of Singapore.

"PlushPal: Storytelling with Interactive Plush Toys and Machine Learning," co-authored by CS Masters student Deanna Gelosi (advisor: Dan Garcia), has won the Best Full Paper Award at the Association for Computing Machinery (ACM) Interaction Design for Children (IDC) conference 2021.  IDC is "the premier international conference for researchers, educators and practitioners to share the latest research findings, innovative methodologies and new technologies in the areas of inclusive child-centered design, learning and interaction."  The paper, which was presented in the "Physical Computing for Learning" conference session, describes PlushPal, "a web-based design tool for children to make plush toys interactive with machine learning (ML). With PlushPal, children attach micro:bit hardware to stuffed animals, design custom gestures for their toy, and build gesture-recognition ML models to trigger their own sounds."  It creates "a novel design space for children to express their ideas using gesture, as well as a description of observed debugging practices, building on efforts to support children using ML to enhance creative play."  Gelosi's degree will be in the field of Human-Computer Interaction and New Media, and her research interests include creativity support tools, traditional craft and computing technologies, digital fabrication, and equity in STEAM.  She is a member of the Berkeley Center for New Media (BCNM), the Berkeley Institute of Design (BID), and the Tinkering Studio--an R&D lab in the San Francisco Exploratorium.

EECS Instructional Support Group (ISG) systems administrator Yang "Linda" Huang, has just published her third book, My Good Son (University of New Orleans Press, May 2021).  The novel, described as "layered, evocative and engaging" by Ms Magazine, had been selected for the University of New Orleans (UNO) Publishing Lab Prize "for the best unpublished novel or short story collection" by authors from around the world.  Like Huang's previous work, "My Good Son" focuses on the generational and cultural complexities of post-Tiananmen Chinese family life.  The story centers on a traditional Chinese father striving for the success of his son, and explores "the parallels and differences of American and Chinese cultures―father-son relationships, familial expectations, sexuality, social mobility, and privilege."  "My Good Son" was reviewed by both the  New York Times and the San Francisco Chronicle.  Huang, who was featured in the Chinese Literature Podcast on June 4th, will be participating in a Virtual Launch at Booksmith on June 9th, where she will engage in a conversation with author Kaitlin Solimine.

CS Prof. and Chan Zuckerberg Biohub investigator Michel Maharbiz is the senior author of a paper in Nature Biotechnology titled "Monitoring deep-tissue oxygenation with a millimeter-scale ultrasonic implant," which describes a tiny wireless implant that can provide real-time measurements of tissue oxygen levels deep underneath the skin. The device, which is smaller than the average ladybug and powered by ultrasound waves, could help doctors monitor the health of transplanted organs or tissue and provide an early warning of potential transplant failure.  “It’s very difficult to measure things deep inside the body,” said Maharbiz. “The device demonstrates how, using ultrasound technology coupled with very clever integrated circuit design, you can create sophisticated implants that go very deep into tissue to take data from organs.”