2003 Distinguished CS Alumnus Madhu Sudan (Ph.D. '92, advisor: Umesh Vazirani) has won the Institute of Electrical and Electronics Engineers (IEEE) Hamming Medal.  This award recognizes "exceptional contributions to information sciences, systems, and technology."   Sudan was cited “for fundamental contributions to probabilistically checkable proofs and list decoding of Reed-Solomon codes.”  He won the Berkeley EECS Sakrison Memorial Award for his graduate thesis, worked as a researcher at both the IBM Watson Research Center and Microsoft Research, was a professor of EECS and the Associate Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT, and is now a professor at the Harvard School of Engineering and Applied Sciences (SEAS).  Sudan is known for his contributions to theoretical computer science, particularly for advancing the theory of probabilistically checkable proofs, which is a way to recast a mathematical proof in computer language for additional checks on its validity, and for developing error-correcting codes.

2008 Distinguished CS Alumna Deborah Estrin (B.S. EECS '80) has won the prestigious Institute of Electrical and Electronics Engineers (IEEE) John von Neumann Medal.  The award recognizes “outstanding achievements in computer-related science and technology.”  Estrin, whose research interests include technologies for caregiving, immersive health, small data, participatory sensing and public interest technology, was cited for “her leadership in mobile and wireless sensing systems technologies and applications, including personal health management.”  Now a professor at Cornell, Estrin was the founding director of the National Science Foundation Center for Embedded Networked Sensing (CENS) at UCLA, where she pioneered the development of mobile and wireless systems to collect and analyze real-time data about the physical world. She also co-founded the nonprofit startup Open mHealth, which creates open data sharing standards and tools that allow developers of health applications to store, process, and visualize data.

A postdoc working in Prof. Laura Waller's Computational Imaging Lab, Loza Tadesse, has been included in the Forbes 2022 list of 30 Under 30 in Healthcare.  Tadesse was cited for "developing diagnostics for extreme environments. To that end, she has developed a rapid system using machine learning and a light scattering approach that can identify infectious bacteria much faster which will lead both to better treatments and less reliance on the use of broad spectrum antibiotics."   She co-founded Frontiers in Science for Africa (SCIFRO) which "works with institutions in Africa to enable African high school and college students to innovate affordable technologies that solve local problems in areas of health and energy." Tadesse received her PhD in BioE from Stanford last year and has accepted a faculty position in MIT's engineering department.

The EECS Director of Undergraduate Student Instruction, Christopher Hunn, and the EECS Director of Undergraduate Affairs, Antoine Davis, have won 2020 and 2021 Advising and Student Services Awards.   These awards are presented by the UC Berkeley Council of Advising and Student Support to "recognize the positive and innovative impact our recipients have on student learning, engagement, and belonging on the Berkeley campus."   Hunn won an Equity Champion Award for coaching TAs, undergraduates (especially CS Scholars and CS Mentors), graduates, staff (including student services personnel), and faculty "with evidence-based practices that have increased student engagement, success, self-efficacy, and belonging."   Davis won an Outstanding Advising or Student Services Administrator, Director, or Manager Award for supporting  and strengthening his staff team and the EECS and CS undergraduate populations, particularly during the pandemic, with his unique blend of humor and calm positivity.  "His relaxed approach fosters an environment where we are able to engage in self-reflection and open-mindedness toward each other and our students."  The winners will be celebrated at a virtual ceremony on December 15th.

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.

CS alumna Medha Kothari (B.A. '20) is featured in an episode of California magazine's The Edge podcast titled "Blockchain for the People."  While still a student, Kothari, who is currently a Research Partner at Variant, founded she256, a non-profit that "aims to increase diversity and break down barriers to entry in the blockchain space."  She discusses what blockchain is and why it has the potential to be a fairer technology "that can change the world."  Produced by the Cal Alumni Association, The Edge podcast series explores "cutting-edge ideas in science, tech, and society coming out of UC Berkeley."

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.