EECS alumna and Prof. Dawn Song (Ph.D. '02) appears in episode #95 of the Artificial Intelligence Podcast with Lex Fridman to discuss adversarial machine learning and computer security.   They cover topics ranging from attacks on self-driving cars to data ownership, program synthesis, and the meaning of life.

CS Prof. Michael I. Jordan, one of the world’s foremost researchers of machine learning, has been awarded an Honorary Doctorate in Engineering and Technology from Yale University.  Since 1702, honorary degrees have been the most significant recognition conferred by Yale, and signal "pioneering achievement in a field or conspicuous and exemplary contribution to the common good." Jordan's citation reads: "Facing an uncertain and complex world, you harness the power of human and machine learning to solve daunting problems. By bridging disciplines and following your curiosity, you have made possible what was once only imagined. Explorer of new domains, champion of big ideas: in recognition of the doors you have opened and the networks you have built, we proudly bestow on you this Doctor of Engineering and Technology degree."  Jordan is known for his foundational work at the interface of computer science and statistics, and for his applied work in computational biology, natural language processing, and signal processing.

Four papers co-authored by EECS faculty (3 of which were co-authored by Prof. Dawn Song) have won Test-of-Time awards at the IEEE Symposium on Security and Privacy today: "Efficient Authentication and Signing of Multicast Streams Over Lossy Channels," co-authored by Song (Ph.D. '02) and the late Prof. Doug Tygar (with Perrig and Canetti) in 2000, "Practical Techniques for Searches on Encrypted Data," co-authored by Song and Prof. David Wagner (with Perrig) in 2000, "Random Key Predistribution Schemes for Sensor Networks," co-authored by Song (with Chan and Perrig) in 2003, and "Outside the Closed World: On Using Machine Learning For Network Intrusion Detection" co-authored by Prof. Vern Paxson (with Sommer) in 2010.    IEEE-SP is considered the premier computer security conference and this four-fold achievement demonstrates Berkeley's preeminence in the field.

A team of researchers led by EE Prof. Sayeef Salahuddin and his graduate student, Suraj Cheema, have managed to grow an ultra-thin material on silicon that can power tiny electronic devices at the atomic scale.  Prior to this fundamental breakthrough, the thinnest conventional material that could demonstrate stable ferroelectricity was 3 nanometers thick.  The new ultrathin material, made of doped hafnium oxide just 1 nanometer thick (equivalent to the size of two atomic building blocks), can demonstrate even stronger ferroelectricity than material several times thicker.  This means it can efficiently power increasingly smaller devices, including memory and logic chips, batteries and sensors, with lower amounts of energy.  The findings were published in the April 22 issue of Nature.

CS Prof. Emeritus David Patterson is featured in the inaugural episode of the Association for Computing Machinery (ACM) ByteCast podcast series, released today.  The episode also features John Hennessy who, along with Patterson, won the ACM A.M. Turing Award in 2017 for their breakthrough work in RISC microprocessor architecture.  During the interview, they share their experiences, the lessons they’ve learned, and their visions for the future of computing.  The new podcast focuses on "researchers, practitioners and innovators who are at the intersection of computing research and practice."

EECS Prof. and alumnus Chenming Hu (M.S. '70/Ph.D. '73) is the subject of an IEEE Spectrum article titled "The father of FinFets: Chenming Hu took transistors into the third dimension to save Moore's Law" (Volume: 57 , Issue: 5 , May 2020).    Hu devised a way around a limitation in semiconductor design that threatened to keep transistors from getting smaller by innovating a technology called FinFET (Fin Field-Effect Transistors) in the IEEE Spectrum.  His idea was to raise  the channel through which current flows so that it sticks out above the surface of the chip like the fin of a shark.   Hu, who extended manufacturers' ability to miniaturize chips beyond what was expected by decades, won the National Medal of Technology and Innovation in 2016 and the IEEE Medal of Honor in 2020.   The article charts Hu's winding career path, the nature of his research and the impact of his contributions.

Freshly-graduate CS Ph.D. student Daniel J. Fremont (advisor: Sanjit Seshia) has won the Association for Computing Machinery (ACM) Special Interest Group on Embedded Systems (SIGBED) Paul Caspi Memorial Dissertation Award for his thesis on "Algorithmic Improvisation."  The award, which was established in 2013, recognizes outstanding doctoral dissertations that significantly advance the state of the art in the science of embedded systems.  Fremont's thesis proposes a theory of algorithmic improvisation to enable the correct-by-construction synthesis of randomized systems, and explores its applications to safe autonomy.

EECS Prof. Ruzena Bajcsy has won the 2020 NCWIT Pioneer in Tech Award which "recognizes technical women whose lifetime contributions have significantly impacted the landscape of technological innovation, amplifying the importance of capitalizing on the diverse perspectives that girls and women can bring to the table. "   Bajcsy pioneered a new area of study within the field of robotics called Active Perception and was the first to argue that robots should be able to autonomously control the movements of their own sensors and other apparatus for interacting with their environment. She  is known for creating the  first 3D computer atlas of the human brain, which revolutionized brain surgery by allowing doctors to more accurately locate tumors.  Bajcsy also pioneered the process of elastic matching "in which computers match defined points in the human body with standardized medical images, enabling non-invasive diagnostics of the brain and other organs."  Like other winners of the award, Bajcsy serves as a role model whose legacy continues "to inspire generations of young women to pursue computing and make history in their own right."

CS Prof. Hany Farid is launching a major survey of people in the United States and Western Europe to determine how far COVID-19 misinformation has penetrated the population. Using Amazon’s Mechanical Turk survey software, he and his research team hope to interview thousands of people in an effort to better understand how misinformation is being distributed, consumed, and spread.  Farid will work with other researchers and social media platforms to develop strategies on how to stop misinformation before it can take hold.

EECS Associate Profs. Prabal Dutta and Robert Pilawa-Podgurski have embarked on a project with doctors at UCSF to develop cost-effective powered air-purified respirators (PAPR) that will offer greater protection for healthcare workers from the coronavirus during higher risk medical procedures.  They are using an approach originally proposed to them by Oakland resident Lakin Moser, to explore a do-it-yourself PAPR concept that would be medically acceptable, inexpensive to build and rapidly scalable for regional, national and global needs.  Their prototype, which is made with a combination of off-the-shelf components and custom electronic circuits and mechanical parts sourced from Bay Area manufacturers and major electronics distributors, will cost $200 per unit--ten times less than standard devices--and can be manufactured at scale in weeks. “A key aspect of the design was to source components that are widely available, and to provide modularity to enable swap-in of alternatives if supply chain issues arise,” said Pilawa-Podgurski.  The latest version of their prototype, which was built in the team's garages and basements,  is currently undergoing usability testing at UCSF Medical Center to ensure that it meets clinical standards.  The team plans to post their design on the web as soon as it is finalized for production.