Jiaheng Zhang wins 2021 Facebook Fellowship for Security & Privacy

Third-year EECS graduate student Jiaheng Zhang (advisor: Dawn Song) has won a 2021 Facebook Fellowship for Security & Privacy.   He is the only student from Berkeley this year to win one of these coveted fellowships, which are designed to support emerging scholars who are engaged in innovative research.  Zhang's focus is on computer security and cryptography, especially zero-knowledge proofs and their applications on blockchain and machine learning models.  He is a member of the RISE Lab, the Initiative for Cryptocurrencies & Contracts Lab (IC3), and the Berkeley AI Research (BAIR). 

Charles Dalziel's Ground-Fault Circuit Interrupters still make plugging in safer

EE alumnus and Prof. Charles Dalziel (1904-1986, B.S./M.S./E.E. 1935 ), the inventor of the Ground-fault circuit interrupter (GFCI),  is the subject of an article in the California Department of Consumer Affairs (DCA) Spring 2021 Consumer Connection.  Patented in 1965 by Dalziel, a professor in the department for 35 years (1932-1967), GFCIs are built into electrical systems and power cords to monitor the current flowing through them.  If the incoming current differs from the returning current, the GFCI interrupts the power "to prevent a lethal dose of electricity, specifically before the electricity can affect your heartbeat."  Besides protecting users against severe electrical shock, a particular hazard in wet environments, GFCIs prevent surges that can cause electrical fires.  The U.S. National Economic Council (NEC) now mandates GFCI protection in many areas of the home as part of their standards for modern building construction.  The article explores GFCI's importance, their California connection, and how to ensure one's home is up to current safety standards.

Anthony Joseph named Director of Fung Institute

EECS Prof. Anthony Joseph has been name the next Director of the Coleman Fung Institute for Engineering Leadership.  After earning his degrees at MIT, Joseph was hired as a professor of Computer Science at Berkeley in 1998. His primary research interests are in Genomics, Secure Machine Learning, Datacenters, mobile/distributed computing, and wireless communications (networking and telephony). His research also includes adaptive techniques for cloud computing, distributed network monitoring and triggering, cybersecurity, and datacenter architectures. He is the former Director of Berkeley Intel Lab, the co-founder of two startup companies, and a committed teacher who has experience developing and teaching five successful massive, open online courses (MOOC) on Big Data and Machine Learning offered through the BerkeleyX platform. Joseph is noted for his commitment to access and inclusion, and has worked to recruit and mentor a diversity of students at the undergraduate and graduate levels.  He will begin his directorship on July 1st.

Tiny wireless implant detects oxygen deep within the body

CS Prof. and Chan Zuckerberg Biohub investigator Michel Maharbiz is the senior author of a paper in Nature Biotechnology titled "Monitoring deep-tissue oxygenation with a millimeter-scale ultrasonic implant," which describes a tiny wireless implant that can provide real-time measurements of tissue oxygen levels deep underneath the skin. The device, which is smaller than the average ladybug and powered by ultrasound waves, could help doctors monitor the health of transplanted organs or tissue and provide an early warning of potential transplant failure.  “It’s very difficult to measure things deep inside the body,” said Maharbiz. “The device demonstrates how, using ultrasound technology coupled with very clever integrated circuit design, you can create sophisticated implants that go very deep into tissue to take data from organs.”

Yang You makes Forbes 30 Under 30 2021 Asia for Healthcare and Science

EECS alumnus Yang You (Ph.D. '20, advisor: James Demmel) has been named in the Forbes 30 Under 30 2021 Asia list for Healthcare and Science.  Yang, who is now a Presidential Young Professor of Computer Science at the National University of Singapore, studies Machine Learning, Parallel/Distributed Algorithms, and High-Performance Computing. The focus of his research is scaling up deep neural networks training on distributed systems or supercomputers.  He has broken two world records for AI training speed: one in 2017 for ImageNet and the other in 2019 for Boundless Electrical Resistivity Tomography (BERT).  Yang has won numerous best paper awards as well as the inaugural Berkeley EECS Lotfi A. Zadeh Prize for outstanding contributions to soft computing and its applications by a graduate student.

Leslie Field to participate in "Reflections on Arctic Ice" webinar

EE alumna Leslie Field (M.S. '88/Ph.D. '91, advisor: Richard White), who is the founder and CTO of the Arctic Ice Project and an adjunct lecturer at Sanford, will be a co-panelist in a webinar titled "Reflections on Arctic Ice: A special webinar with Dr. Peter Wadhams."  Wadhams, a professor emeritus of Ocean Physics at Cambridge and the author of “A Farewell to Ice,”  has made more than 50 polar expeditions and recently appeared in the documentary “Ice on Fire” with Leonardo DiCaprio.   Field was the first woman to earn a Ph.D. from the Berkeley Sensor and Actuator Center (BSAC).  The event will be on April 20th at 12 pm PST and is free, but registration is required.

Rediet Abebe co-chairing ACM Conference on Equity and Access in Algorithms, Mechanisms, & Optimization

CS Assistant Prof. Rediet Abebe is co-chairing the inaugural ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO ’21) in October 2021.  This conference will highlight work where techniques from algorithms, optimization, and mechanism design, along with insights from other disciplines, can help improve equity and access to opportunity for historically disadvantaged and underserved communities.  Launched by the Mechanism Design for Social Good (MD4SG) initiative, it will feature keynote talks and panels, and contributed presentations of research papers, surveys, problem pitches, datasets, and software demonstrations.   The submission deadline is June 3, 2021.

Andreea Bobu named 2021 Apple Scholar in AI/ML

EECS graduate student Andreea Bobu (advisor: Anca Dragan) has been named a 2021 Apple Scholar in AI and Machine Learning (AI/ML).  The scholarship was created by Apple to "celebrate the contributions of students pursuing cutting-edge fundamental and applied machine learning research worldwide."  Bobu's research interests lie at the intersection of machine learning, robotics, and human-robot interaction, with a focus in robot learning with uncertainty. She is particularly interested in the ways in which autonomous systems’ models of the world and of other agents (e.g. humans) can go wrong, and is devising ways to enhance interaction between people and robots.  She earned her BS in Computer Science and Engineering at MIT in 2017, where she worked on probabilistic models for medical image analysis.  She is currently associated with the Berkeley Artificial Intelligence Research (BAIR) lab.

Wenshuo Guo wins 2021 Google PhD Fellowship

EECS graduate student Wenshuo Guo (advisor: Michael I. Jordan) has won a 2021 Google PhD Fellowship in Algorithms, Optimization and Markets.  This award acknowledges and supports exemplary PhD students in computer science and related fields who are making contributions to their areas of specialty.   Guo studies robustness guarantees in algorithms and machine learning foundations, as well as their impact on society.  She is also interested in the intersection of CS and economics, and is currently focused on mechanism design, causal inference, and statistical questions in reinforcement learning. The award, which will cover full tuition, fees, and a stipend for the 2021-22 school year, will be presented at the Global Fellowship Summit over the summer.

Michael Jordan explains why today’s AI systems aren’t actually intelligent

CS Prof. Michael I. Jordan is the subject of an IEEE Spectrum article which describes his life, research, and philosophy.  A computer science pioneer, Jordan blended CS, statistics, and applied mathematics, to help transform unsupervised machine learning into a powerful algorithmic tool for solving problems in fields like natural language processing, computational biology, and signal processing.  He explains that machine learning is, in essence, a new field of engineering focused on the interface between people and technology.  The optimal goal of machine learning should not be artificial imitation of human thinking since that is something human beings can already do for themselves.  Instead, AI should be focused on helping humanity solve the problems that it has created.  “While the science-fiction discussions about AI and super intelligence are fun, they are a distraction,” Jordan says. “There’s not been enough focus on the real problem, which is building planetary-scale machine learning–based systems that actually work, deliver value to humans, and do not amplify inequities.