News

Umesh Vazirani to help lead $25 million quantum computing center

The National Science Foundation (NSF) has awarded UC Berkeley $25 million over five years to help lead the establishment of a multi-university institute focused on advancing quantum science and engineering.  EECS Prof. Umesh Vazirani, who is co-director of the Berkeley Quantum Computation Center (BQIC) and leads the quantum computing effort at the Simons Institute for the Theory of Computing (SITC), will serve as co-director of the new institute.  Other participants from EECS will include Prof. Ming Wu, Prof. Shafi Goldwasser, Prof. John Kubiatowicz, and Associate Prof. Boubacar Kanté. The center will be one of three Quantum Leap Challenge Institutes (QLCI) designed as part of the federal government's effort to accelerate the development of quantum computers, train a future workforce to build and use them, and position them to be as ubiquitous as smart phones.  The new institute for Present and Future Quantum Computation will connect Berkeley, UCLA, UCSB, USC, Caltech, UT Austin, MIT, and UW, to combine the talents of top experimental and theoretical scientists in the fields of computer science, chemistry, physics, materials science, engineering and mathematics, to solve problems and devise strategies around this currently rudimentary technology.   Attaining a better understanding of its computational capabilities will require a major increase in the number of computer scientists involved in asking and answering questions.  “Realizing the full power of quantum computation requires development of efficient schemes for correction of errors during operation of quantum machines, as well as protocols for testing and benchmarking," said Vazirani. “Translating this remarkable ability of quantum computers into actually solving a computational problem is very challenging and requires a completely new way of thinking about algorithms.”

prof. david wagner

David Wagner testifies about remote voting security before Congress

Prof. David Wagner, whose area of expertise includes computer security and the security of electronic voting, testified before Congress at a hearing of the House Administration Committee on Friday, July 15, 2020. The hearing was called to investigate options for lawmakers in Congress to vote remotely during Covid-19. Wagner explained that while it is technologically feasible for the House to conduct roll-call votes remotely, it will come with some manageable risk.  He recommended securing the vote using "a combination of people, process, and technology," including making all votes public immediately, having the House establish policies to govern the process--including contingencies for technology failures, and specifically selecting technology to support cybersecurity. 

Stuart Russell answers 3+ questions in wake of Turing Lecture

In May 2020, CS Prof. Stuart Russell delivered the most highly attended Turing Lecture yet,  to a virtual audience of over 700 people from around the world, on the subject of provably beneficial AI.  In a follow-up article, "Three (plus) questions with Turing Lecturer Stuart Russell," he answers some of the many questions not covered during the live Q&A.  In his talk, Russell argues that "it is useful to imbue systems with explicit uncertainty concerning the true objectives of the humans they are designed to help. This uncertainty causes machine and human behaviour to be inextricably (and game-theoretically) linked, while opening up many new avenues for research."  The top three questions address how AI should make immediate choices, how to address changing preferences as society evolves, and how AI can be controlled to minimize bias.  The ideas discussed are explored in his most recent book, "Human Compatible: AI and the Problem of Control" (Viking/Penguin, 2019).  The Turing Lectures are hosted by The Alan Turing Institute, the UK’s national institute for data science and artificial intelligence, and should not be confused with the Turing Talks sponsored by BCS and IET.

Two EECS projects awarded Berkeley Changemaker Technology Innovation Grants

CS Prof. Eric Paulos and Associate Prof. Bjoern Hartmann have both won 2020 Berkeley Changemaker Technology Innovation Grants to support projects involving "transformative ideas with real applications that benefit the Berkeley campus."  Paulos's project is Lucid Learning, a suite of tools to help students in disciplines like architecture, art practice, theater, dance and performance studies, to incorporate augmented reality (AR) and virtual reality (VR) into their iterative processes of collaboration, design and feedback.  There are currently online tools that can help assess work in quantitative courses but few available for more open-ended, studio-based teamwork courses.  Hartmann's project, VRTutor, aims to both allow students to interact with an instructional 3D video pre-recorded by their professor in VR, and also allow instructors to view a live feed of students working in VR to give them guidance.  Tutorial feedback can be offered by drawing on the student's video feed on a tablet, then re-projecting the drawings into the student’s VR scene in 3D.

Two projects led by EECS faculty win funding to combat COVID-19

Projects led by CS Prof. Jennifer Listgarten and EE Prof. Alberto Sangiovanni-Vincentelli have been awarded funding from the C3.ai Digital Transformation Institute to harness the power of AI to combat the spread of COVID-19 and other emerging diseases.  Listgarten's project will draw upon techniques such as reinforcement learning, robust uncertainty estimation and probabilistic modeling to develop new and trustworthy methods for therapeutic drug discovery for COVID-19.  Sangiovanni-Vincentelli's project will develop algorithms for AI that will help health care institutions better detect and contain emerging diseases.  These projects are two of six awarded to UC Berkeley, and among 26 projects world-wide, which will share $5.4M to accelerate AI research for COVID-19 mitigation through advances in medicine, urban planning and public policy.

Aditya Parameswaran Awarded Best Paper at SIGMOD/PODS 2020

CS Assistant Prof. Aditya Parameswaran has been awarded the Best Paper Award at the 2020 ACM Special Interest Group on Management of Data (SIGMOD)/Symposium on Principles of Database Systems (PODS) for his joint paper: “ShapeSearch: A Flexible and Efficient System for Shape-based Exploration of Trendlines.”  The paper proposes the implementation of ShapeSearch, a tool that mitigates issues with existing visual analytics tools, such as limited flexibility, expressiveness, and scalability.  The paper was one of two that received the top award out of over 144 accepted research papers and 450 submissions to ACM SIGMOD/PODS, the premiere international conference on the theoretical aspects of database systems.

11 EECS faculty among the top 100 most cited CS scholars in 2020

The EECS department has eleven faculty members who rank among the top 100 most cited computer science & electronics scholars in the world. UC Berkeley ranked #4  in the global list of universities with the highest number of influential scholars in 2020 (35, up from 24 in 2018).  Profs. Michael Jordan, Scott Shenker, Ion Stoica, Jitendra Malik, Trevor Darrell, David Culler, Shankar Sastry, Randy Katz, Alberto Sangiovanni-Vincentelli, Lotfi Zadeh and Dawn Song all ranked in the top 100 with an H-index score of 110 or higher, a measure that reflects the number of influential documents they have authored.   Jordan ranks fourth in the world, with an H-index of 166 and 177,961 citations.  The H-index is computed as the number h of papers receiving at least h citations among the top 6000 scientist profiles in the Google Scholars database. 

Dawn Song discusses adversarial machine learning and computer security on AI podcast

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.

Michael Jordan awarded Honorary Doctorate from Yale

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 authored by EECS faculty win Test-of-Time Awards at 2020 IEEE-SP

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.