CS Associate Prof. Laura Waller, and Assistant Profs. Sarah Chasins and Nilah Ioannidis, are among the newest cohort of scientists to be named Chan Zuckerberg Biohub Investigators.  The Chan Zuckerberg Biohub Investigator Program, open to faculty from Stanford, UCSF, and UC Berkeley, funds "innovative, visionary research with the goal of building and sustaining an engaged, interactive, and collaborative community of researchers that spans across disciplines and across the three campuses to help solve critical challenges in biomedicine."  Waller leads the Computational Imaging Lab, which develops new methods for optical imaging designed jointly with optics and computational algorithms; Chasins' research focuses on programming languages and program synthesis, with an emphasis on work that brings together programming systems, HCI, and data science; and Ioannidis works on computational methods for personal genome interpretation, including machine learning tools to predict the clinical significance of rare genetic variants of unknown significance and statistical methods to link genetic variation with personal complex disease risks.  Recipients  receive $1M in unrestricted gift funds over a nonrenewable 5-year term ($200,000 per year).

CS Prof. Emeritus David Patterson has won the 2022 Charles Stark Draper Prize for Engineering from the National Academy of Engineering (NAE).  Recognized as one of the world's preeminent awards for engineering achievement, the prize "honors an engineer whose accomplishment has significantly impacted society by improving the quality of life, providing the ability to live freely and comfortably, and/or permitting the access to information."  Patterson, and his co-recipients, John Hennessy, Stephen Furber, and Sophie Wilson, were cited "for contributions to the invention, development, and implementation of reduced instruction set computer (RISC) chips."  Patterson began the seminal Berkeley RISC project in 1980 to design a basic, neutral, freely-available set of microprocessor instructions that could be used in different types of machines and which could be optimized for different characteristics, like efficiency, physical size, and monetary cost.  When different devices are capable of running the same machine code, a better quality, higher-performance machine can replace a less expensive, lower-performance machine without having to replace software.  The open-source Berkeley RISC design was later commercialized by Sun Microsystems as the SPARC architecture, and inspired the ARM architecture used in virtually all new computer chips in the world today.  The biennial Draper prize is open to both NAE members and non-members worldwide, and comes with a $500K cash award.

CS Profs. Alistair Sinclair and Shafi Goldwasser have won inaugural Test of Time awards at the 2021 Symposium on Theory of Computing (STOC), sponsored by the ACM Special Interest Group on Algorithms and Computation Theory (SIGACT).  Sinclair won the 20 Year award for his paper, “A polynomial-time approximation algorithm for the permanent of a matrix with non-negative entries," which solved a problem that had been open for decades. Goldwasser won the 30 Year award for "Completeness theorems for non-cryptographic fault-tolerant distributed computation," which showed how to compute a distributed function even if up to one-third of the participants may be failing, misbehaving, or malicious.  The awards were presented at the 2021 STOC conference in June.

CS alumna and Prof. Shafi Goldwasser (Ph.D. '84, advisor: Manuel Blum) has won the 2021 Foundations of Computer Science (FOCS) Test of Time Award.  This award "recognizes papers published in past Annual IEEE Symposia on Foundations of Computer Science (FOCS) for their substantial, lasting, broad, and currently relevant impact. Papers may be awarded for their impact on Theory of Computing, or on Computer Science in general, or on other disciplines of knowledge, or on practice."  Goldwasser is among five co-authors who won the award in the 30 year category for their groundbreaking complexity theory paper "Approximating Clique is Almost NP-Complete," which used the classification of approximation problems to show that some problems in NP remain hard even when only an approximate solution is needed. 

EECS Prof. Stuart Russell is slated to direct the future Kavli Center for Ethics, Science, and the Public, which aims to make ethics and social equity more central to scientific decision-making, and which will try to ensure that the public has a greater say in future scientific advances.  The new center will combine forces with a sister center at the University of Cambridge, UK, to connect scientists, ethicists, and the public, in necessary and intentional discussions about the potential impacts of scientific discoveries.  “In addition to answering fundamental questions about the ethics of science, the Kavli Center is going to create a generation of scientific leaders who have seen how other scientific disciplines grapple with ethical problems and who have real training in the philosophical analysis of these questions,” said Russell. “It’s not just about changing public policy, it’s about changing what it means to be a good scientist in every discipline that can have an impact on the public.”

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.”

Berkeley EECS is happy to announce the launch of the Center for Energy Efficient Deep Learning (CEEDL), a new Intel oneAPI Center of Excellence (CoE) with Prof. Kurt Keutzer as Principal Investigator and Prof. Joey Gonzalez as co-PI. This center will focus on producing energy-efficient algorithms and implementations for deep learning’s most computationally-intensive workloads. As computing grows to become an increasingly significant portion of an organization’s energy budget, deep-learning workload compute demands are also becoming insatiable. The CEEDL’s charter includes developing energy-efficient algorithms for challenging workloads such as training recommendation systems and natural language understanding systems. The center will use the oneAPI Deep Neural Network Library (oneDNN) and the oneAPI Collective Communications Library (oneCCL) to optimize this work. While high-level algorithms are useful, these algorithms must be implemented on an ever-increasing variety of computational platforms to be impactful. oneAPI’s open, unified heterogeneous programming will significantly ease the development of portable implementations across multiple types of architectures: CPUs, GPUs, FPGAs, and other accelerators.