Professors Alberto Sangiovanni-Vincentelli and Scott Shenker have been named Distinguished Fellows of the International Engineering and Technology Institute (IETI) for 2019.  They are among 15 professors elected this year, joining well-known experts from all over the world, including Nobel Prize and Turing Award Laureates. Founded in 2015, IETI is a non-profit organization that promotes the innovations of Science, Engineering and Technology across the world. 

Prof. Aditya Parameswaran wins the Very Large Data Bases (VLDB) Early Career Award, which recognizes a researcher who has demonstrated research impact through a specific technical contribution of high significance since completing the Ph.D. The VLDB Endowment is a non-profit organization incorporated in the United States for the sole purpose of promoting and exchanging scholarly work in databases and related fields throughout the world. Prof. Parameswaran is cited "for developing tools for large-scale data exploration, targeting non-programmers.” 

Profs. Anca Dragan and Alvin Cheung have been awarded the Presidential Early Career Award for Scientists and Engineers (PECASE), which is the highest honor bestowed by the U.S. government to scientists and engineers in the early stages of their careers. Established in 1996, the PECASE acknowledges the contributions scientists and engineers have made to the advancement of science, technology, education, and mathematics (STEM) education and to community service as demonstrated through scientific leadership, public education, and community outreach. Prof. Dragan was nominated by the National Science Foundation and Prof. Cheung was nominated by the U.S. Department of Energy.

CS Prof. Michael Jordan has written a commentary in the Harvard Data Science Review (HDSR) titled "Dr. AI or: How I Learned to Stop Worrying and Love Economics" (a play on the title of the film Dr. Strangelove).  In it, he  argues that instead of trying to put "‘thought’ into the computer, and expecting that ‘thinking computers’ will be able to solve our problems and make our lives better," he explores the prospect of bringing microeconomics "into the blend of computer science and statistics that is currently being called ‘AI.'"

CS Prof. Emeritus David Patterson, his former graduate student Zhangxi Tan (PhD '13), and Lin Zhang of the Tsinghua-UC Berkeley Shenzhen Institute (TBSI), have been chosen to co-direct the new RISC-V International Open Source (RIOS) Laboratory, an non-profit research lab launched by the TBSI.  RIOS aims to expand and elevate the capabilities of Reduced Instruction Set Computer (RISC) microprocessors.  Patterson, who is currently a distinguished engineer at Google, coined the term RISC in the early 1980s to describe a computer architecture that allowed microprocessors to operate far more efficiently with simple, general instructions.  Nearly all of the 16 billion microprocessors produced annually are RISC processors.

CS graduate student Shruti Agarwal and her thesis advisor Prof. Hany Farid have created a new weapon in the war against "deepfakes," the hyper-realistic AI-generated videos of people appearing to say and do things they never actually said or did.  The new forensic technique, which uses the subtle characteristics of how a person speaks to recognize whether a new video of that individual is real, was presented this week at the Computer Vision and Pattern Recognition conference in Long Beach.  “The basic idea is we can build these soft biometric models of various world leaders, such as 2020 presidential candidates," said Farid, "and then as the videos start to break, for example, we can analyze them and try to determine if we think they are real or not.”

CS Prof. Alexei "Alyosha" Efros has teamed up with student researcher Sheng-Yu Wang and postdoc Andrew Owens, as well as Adobe researchers Richard Zhang and Oliver Wang, to develop a method for detecting and reversing edits to images made using Adobe Photoshop’s Face Aware Liquify feature--a popular tool for adjusting facial features, including making adjustments to facial expressions.  While still in its early stages, this collaboration to train a convolutional neural network (CNN) is part of a broader effort across Adobe to better detect image, video, audio and document manipulations, as well as a step toward democratizing image forensics.

EE Prof. Michael Lieberman has won the 2020 IEEE Marie Sklodowska-Curie Award for outstanding contributions to the field of nuclear and plasma sciences and engineering.  This IEEE-level Technical Field Award is the highest honor administered by the Nuclear and Plasma Sciences Society.  Lieberman was cited “For groundbreaking research and sustained intellectual leadership in the physics of low-temperature plasmas and their application.”  Prof. Ned Birdsall (1925-2012) won the inaugural award in 2011, making Berkeley EECS the only institution to have won two of these awards.

Today, the MIT Technology Review announced Raluca Ada Popa has been named to MIT Technology Review’s prestigious annual list of Innovators Under 35 as a Visionary. Every year, the world-renowned media company has recognized a list of exceptionally talented technologists whose work has great potential to transform the world.

Prof. Popa is a co-founder of the RISELab where she is developing a learning and analytics framework that can run on encrypted data.

Gideon Lichfield, editor-in-chief of MIT Technology Review, said: “MIT Technology Review’s annual Innovators Under 35 list is a chance for us to honor the outstanding people behind the breakthrough technologies of the year that have the potential to disrupt our lives. These profiles offer a glimpse into what the face of technology looks like today as well as in the future.”

EECS Assistant Prof. Joseph Gonzalez is the focus of a profile for the Association for Computing Machinery's "People of ACM" series.  Gonzalez, who works at the intersection of machine learning and data systems, desribes how and why his field has grown over time, where it might be heading, and what challenges might need to be addressed in the future.  "Today progress is largely limited by creativity and our budget for compute resources and data," he says. "Machine learning frameworks...provide the necessary abstractions to hide the complexity of differentiation, optimization, and parallel computation, freeing the modern data scientist to focus on the learning problem. These frameworks build on advances in data systems and scientific computing to unlock new parallel hardware."