News

Nine papers make four Top 10 lists in TOPBOTS AI research rankings

9 papers co-authored by 6 EECS faculty, 13 students,  3 post docs, and 3 alumni have made it into the Top 10 research papers ranked by TOPBOTS in four categories of AI Research. TOPBOTS is the largest publication, community, and educational resource for business leaders applying AI to their enterprises.  3 papers co-authored by Sergey Levine made the #1, #3, and #9 spots in "What Are Major Reinforcement Learning Achievements & Papers From 2018?"  A paper co-authored by Moritz Hardt ranked #5 in "Top 2018 AI research papers" and #3 in  "Recent Breakthrough Research Papers In AI Ethics." A paper co-authored by Jitendra Malik ranked #7 in the Top 2018 papers and #5 in "10 Cutting Edge Research Papers In Computer Vision & Image Generation."  The #2 Top 2018 paper was co-authored by David Wagner, and a paper co-authored by Alexei Efros ranked #9 in the Computer Vision category.

Diane Greene wins 2019 Campanile Excellence in Achievement Award

CS alumna Diane Greene (M.S. '88) has won a 2019 U. C. Berkeley Campanile Excellence in Achievement Award.  This award "recognizes an alumnus/a whose remarkable professional achievements reflect the excellence of a UC Berkeley education" and is co-presented every year by the UC Berkeley Foundation and the Cal Alumni Association.  Greene recently served as the CEO of Google's cloud business and was a founder and CEO of VMware.  She will be formally presented with her award at the Berkeley Charter Gala on May 16, 2019.

Yannis Tsividis elected to NAE

EECS alumnus Yannis Tsividis (M.S. '73/Ph.D. '76, advisor: Paul Gray) has been elected to the U.S. National Academy of Engineering (NAE).  Tsividis is a professor at Columbia University who has made contributions to Analog and Mixed Signal Integrated Circuit Technology, as well as to engineering training.  He has worked at Motorola Semiconductor and AT&T Bell Labs, and has taught at UC Berkeley, MIT, and  the National Technical University of Athens.

Introducing the 2019 EECS Distinguished Alumni

The EECS Distinguished Alumni Awards recognize the valuable contributions of its most distinguished alumni. The 2019 EE distinguished alumni are Sharad Malik (M.S. '87/EE Ph.D '90, advisor: Robert k. Brayton), Chair of Electrical Engineering at Princeton; and Dr. Ahmad Bahai (EE Ph.D '94, advisor: Pravin Varaiya), CTO of Texas Instruments. The 2019 CS distinguished alumni are Andrew Ng (CS Ph.D. '03, adviser: Michael Jordan), Stanford Professor; and Dr. Amin Vahdat (B.S. '92/ CS Ph.D.'98, advisor: Thomas Anderson), Technical Lead for networking at Google, and Google Fellow. The award presentation will be at BEARS on February 14, 2019.

Claire Tomlin elected to the NAE

EE alumna and Prof. Claire Tomlin (Ph.D. '98, adviser: Shankar Sastry) has been elected to the National Academy of Engineering (NAE).  NAE is among the highest professional distinctions accorded to an engineer.  Academy membership honors those who have made outstanding contributions to "engineering research, practice, or education, including, where appropriate, significant contributions to the engineering literature" and to "the pioneering of new and developing fields of technology, making major advancements in traditional fields of engineering, or developing/implementing innovative approaches to engineering education."  Tomlin was cited “For contributions to design tools for safety-focused control of cyberphysical systems.”

Rebecca Sorla Portnoff: Coding against sex trafficking

In an effort to catch sex traffickers, CS alumna Rebecca Sorla Portnoff (Ph.D. '17, adviser: David Wagner) creates computer codes that help identify similarities in traffickers’ online ads and find the Bitcoin accounts they use to buy the ads.  She works for THORN: Digital Defenders of Children, an organization that builds technology to fight the sexual abuse of children.  UC Berkeley News has created a video about her work.

Thomas Philip joins Graduate School of Education faculty

EECS alumnus Thomas Philip (B.S. '98) has joined the faculty of the U.C. Berkeley Graduate School of Education as an associate professor. He is interested in how teachers make sense of power and hierarchy in classrooms, schools and society, and how they navigate and ultimately transform classrooms and institutions toward more equitable, just, and democratic practices and outcomes. In particular, he is studying the possibilities and tensions that emerge with the use of digital learning technologies in classrooms.

Michael Orshansky named to eSilicon Technical Advisory Board

EECS alumnus Michael Orshansky (B.S./M.S./Ph.D. '01, adviser: Chenming Hu) has been named to the technical advisory board of eSilicon, a leading provider of FinFET ASICs, market-specific IP platforms and 2.5D packaging solutions.  Orshansky, who is currently a faculty fellow at the Department of  Electrical and Computer Engineering at the University of Texas, researches approximate computing for on-chip machine learning acceleration.  The board will focus on guiding the company’s development work associated with artificial intelligence ASICS.

HP Names Yoky Matsuoka to Board of Directors

2014 CS Distinguished Alumna, Yoky Matsuoka (B.S. '93), has been appointed to the Board of Directors of HP Inc.  Matsuoka was the founder of Google[x], the company's innovative research and development lab, before serving as CTO of Google Alphabet's Nest business.  She was also a senior executive at Apple and an endowed professor at Carnegie Mellon University and the University of Washington.  The HP Board of Directors is said to be one of the most diverse of any technology company in the U.S.

'Ambidextrous' robots could dramatically speed e-commerce

CS Prof. Ken Goldberg and members of the AUTOLAB including postdoc Jeffrey Mahler (Ph.D. '18), grad students Matthew Matl and Michael Danielczuk, and undergraduate researcher Vishal Satish, have published a paper in Science Robotics which presents new algorithms to compute robust robot pick points, enabling robot grasping of a diverse range of products without training.  They trained reward functions for a parallel-jaw gripper and a suction cup gripper on a two-armed robot, and found that their system cleared bins with up to 25 previously unseen objects at a rate of over 300 picks per hour with 95 percent reliability.