EECS alumnus Warren “Woody” Hoburg (M.S. '11/Ph.D. '13, advisor: Pieter Abbeel) will be among the first candidates to graduate under NASA's Artemis astronaut basic training program on Friday, Jan. 10, at the Johnson Space Center in Houston. Starting next week, Hoburg will be eligible for spaceflight assignments to the International Space Station, missions to the Moon, and ultimately, missions to Mars.  He earned a B.S. in Aeronautics and Astronautics (AeroAstro) from MIT before attending Berkeley, and returned to MIT as an assistant professor in AeroAstro after graduation.  Hoburg is also a commercial pilot who served on the Bay Area Mountain Rescue Unit and Yosemite Search and Rescue.

Alumnus Kirk Tramble (B.S. EECS 1993) is both the son and father of proud UC Berkeley alumni.  His father, Thomas, earned a B.A. in Sociology/African American Studies in 1971, and his son, Gabriel, graduated with a B.A. in Media Studies in 2018.  The family is profiled in a Cal Alumni article which compares their experiences as Black students at Cal during three different eras.  Kirk arrived on campus at a time when academic support for students of color was at an all-time high, and watched as everything changed after the passage of Proposition 209 in 1996, when the percentage of black students dropped from 8% to 2%.  “The response [to Prop 209] was to ultimately shut down many of the programs that were the highest serving programs for African Americans on campus,” he said.  He and his father now administer a scholarship offered by the African American Initiative to encourage and support Black students at Cal.

RISC-V, a royalty-free microprocessor architecture first developed at Berkeley, is emerging as a rival to Arm, the most successful microchip architecture in the world.   The first RISC-V chip was built in 2011 as part of the open source Peer Lab Project by CS Prof. and alumnus Krste Asanović (Ph.D. '98, advisor: John Wawrzynek), CS Prof. Emeritus David Patterson, and CS alumni Andrew Waterman (M.S. 11/Ph.D. '16, advisors: David Patterson/Krste Asanović) and Yunsup Lee (M.S. '11/Ph.D. '16, advisor: Krste Asanović).  Asanović, Waterman and Lee went on to found SiFive, "the first fabless semiconductor company to build customized silicon on RISC-V."   Asanović explains that the architecture has gained momentum "not because it's 10% faster. It's because it's a new business model."  Chip designers traditionally have to find a seller to make their microprocessors, but now designers can select RISC-V and "all suppliers compete for your business.  You can add your own extensions without obtaining permission" or paying license fees.

EECS Prof. and alumna Dawn Song (Ph.D. '02, advisor: Doug Tygar) has been  selected as a 2019 Fellow of the Association for Computing Machinery (ACM).    Song was cited "For contributions to security and privacy" and is now part of an elite group that represents less than 1% of the Association’s global membership.  As one of the world’s foremost experts in computer security and trustworthy artificial intelligence, Song founded a startup to build a new platform based on a paradigm in which people control their data and are compensated for its use by corporations. She was named to both the 2019 WIRED25 list of innovators and Inc.com's list of the 100 most innovative businesswomen in 2019.   Fellows will be honored at an awards banquet in June.

EECS  alumnus Mark Lee McKelvin Jr (M.S.  '05, Ph.D. '11, advisor: Alberto L. Sangiovanni-Vincentelli), has won the 2020 BEYA (Black Engineer of the Year Award) STEM Most Promising Engineer in Industry Award.  The award, which is sponsored by Career Communications Group (publisher of "US Black Engineer and Information Technology magazine") is considered among the most competitive awards in U.S. science, engineering and technology managament.  After graduating, McKelvin worked at Jet Propulsion Laboratory as a software systems engineer for five years before becoming an engineering specialist at the Aerospace Corporation. The award will be presented at the 34th BEYA STEM Conference in February 2020.

Two papers co-authored by Berkeley EECS authors won ACM SIGPLAN Distinguished Paper Awards at the Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA) 2019.  "Duet: An Expressive Higher-Order Language and Linear Type System for Statically Enforcing Differential Privacy" co-authored by Prof. Dawn Song (Ph.D. '02, advisor: Doug Tygar), graduate student Lun Wang, undergraduate researcher Pranav Gaddamadugu, and alumni Neel Somani (CS B.A.  '19), Nikhil Sharma (EECS B.S. '18/M.S. '19),  and Alex Shan (CS B.A. '18), along with researchers in Vermont and Utah, and "Aroma: Code Recommendation via Structural Code Search" co-authored by Prof. Koushik Sen (along with authors at Facebook and UC Irvine), won two of the five honors awarded at the top programming language conference, part of the ACG SIGPLAN conference on Systems, Programming, Languages, and Applications: Software for Humanity (SPLASH) in October.  

CS alumna and Prof. Down Song (Ph.D. '02, advisor: Doug Tygar) is the subject of a New York Times article titled "Building a World Where Data Privacy Exists Online."  One of the world’s foremost experts in computer security and trustworthy artificial intelligence, Song founded a startup called Oasis Labs to build a new platform based a paradigm in which people control their data and are compensated for its use by corporations.  She and her colleagues believe that by marrying specialized computer chips and blockchain technology, they can build a system that provides greater scalability and privacy protection.  This year, Song made the Wired25  list top 25 innovators as well as Inc.'s second annual Female Founders 100 list as a "Money Mover."

A computer algorithm co-developed by Vision Group alumnus Weicheng Kuo (Ph.D. '19), post doc Christian Hӓne, their advisor Prof. Jitendra Malik, and researchers at UCSF, bested two out of four expert radiologists at finding tiny brain hemorrhages in head scans, an advance that one day may help doctors treat patients with traumatic brain injuries, strokes and aneurysms.  The algorithm found some small abnormalities that the experts missed, noted their location within the brain, and classified them according to subtype.  The researchers used of a type of deep learning known as a fully convolutional neural network (FCN) to train the algorithm on a relatively small number of images that were packed with data.  Each small abnormality was manually delineated at the pixel level. The richness of this data — along with other steps that prevented the model from misinterpreting random variations, or “noise,” as meaningful — created an extremely accurate algorithm.