EE Prof. Chunlei Liu has been named a Fellow of the International Society for Magnetic Resonance in Medicine (ISMRM)  The ISMRM is an international, nonprofit, scientific association "whose purpose is to promote communication, research, development, and applications in the field of magnetic resonance (MR) in medicine and biology and other related topics and to develop and provide channels and facilities for continuing education in the field." Fellowships are bestowed to recognize "a significant and substantial contribution to research in a field within the Society’s purposes, who have contributed in a significant manner to the development of the Society...and/or who have made a significant contribution to education in MR."  Liu is known for pioneering higher-order tensor diffusion MRI,  which utilizes higher-order tensor statistics (variance, sknewness, kurtosis etc.) to measure the diffusion processes in biological tissues. He is also credited with developing susceptibility tensor imaging for mapping bio-magnetism.

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 Assistant Prof. Sophia Shao has won the inaugural Workshop on Modeling & Simulation of Systems and Applications (ModSim) 2021 Dr. Sudhakar Yalamanchili (Sudha) Award.  This award recognizes "researchers who showcase the most outstanding contribution to the field of computer modeling and simulation" during the annual ModSim Workshop, which primarily features younger talent in the scientific community.  Shao presented "Enabling Holistic Machine-Learning Hardware Evaluation via Full-System Simulation," which described the Gemmini project's "systolic-array based matrix multiplication accelerator generator" that enables users to explore and evaluate different deep neural network accelerators.  The research was presented during a multi-part Rapid Fire flash talk/digital poster session.

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.

EECS Prof. Murat Arcak and his graduate student He Yin have won the second Systems & Control Letters (SCL) Brockett-Willems Outstanding Paper Award. Their paper, "Reachability analysis using dissipation inequalities for uncertain nonlinear systems," published in SCL Volume 142, on August 2020, was deemed the best of 295 papers submitted to the journal in the two-year period between January 2019 through December 2020.  Co-authors include former ME Prof. Andrew Packard, who died in 2019, and Packard's former graduate student, Peter Seiler.  SCL hopes to present the award at the 25th International Symposium on Mathematical Theory of Networks and Systems (MTNS) which will be held in Bayereuth, Germany, in September 2022.

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.”