News

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Raluca Ada Popa featured in People of ACM

CS Prof. Raluca Ada Popa was interviewed as a Featured ACM Member as part of the "People of ACM" bulletin. As the Co-Director of RISELab and SkyLab, two labs aiming to build secure intelligent systems for the cloud and for the sky of cloud, she spoke about her research interests, which include security, systems, and applied cryptography. “I love both to build systems that can solve a real-world problem and to reason about deep mathematical concepts,” she said. Aiming to predict the direction of her research, she outlined her renewed focus on confidential computing, a major shift in the cloud computing landscape, which she said “will revolutionize data systems in industry in the coming years…[through] the combination of hardware security via hardware enclaves and cryptographic techniques. Many organizations have a lot of confidential data that they cannot share between different teams in their organization or different organizations. Sharing it would enable better medical studies, better fraud detection, increased business effectiveness, and other benefits.”

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‘Bro’ wins USENIX Security Test of Time Award

CS Prof. Vern Paxson has won the USENIX Security Test of Time Award. Originally published in 1998, Prof. Paxson’s paper, “Bro: A System for Detecting Network Intruders in Real-Time,” was selected for its lasting impact on the research community and by traditional publication metrics; as of this writing, “Bro” has been cited 3852 times according to Google Scholar. “The paper belongs in the compendium of ‘must read’ classic papers for any graduate security course,” according to the award committee. The award will be presented at the 31st USENIX Security Symposium, which takes place in Boston, MA this year.

CS Grad Xin Lyu

Xin Lyu wins CCC 2022 Best Student Paper Award

CS graduate student Xin Lyu (advisors: Jelani Nelson and Avishay Tal) has won the Best Student Paper Award at the Computational Complexity Conference (CCC) 2022. The solo-authored paper titled “Improve Pseudorandom Generators for AC^0 Circuits” was one of two co-winners of the Best Student Paper Award at CCC, which is an annual conference on the inherent difficulty of computational problems in terms of the resources they require. Organized by the Computational Complexity Foundation, CCC is the premier specialized publication venue for research in complexity theory.

Pratul Srinivasan and Benjamin Mildenhall jointly awarded honorable mention for 2021 ACM Doctoral Dissertation Award

Two of EECS Prof. Ren Ng's former graduate students, Pratul Srinivasan and Benjamin Mildenhall, jointly received an honorable mention for the 2021 Association for Computing Machinery (ACM) Doctoral Dissertation Award.  This award is presented annually to the "author(s) of the best doctoral dissertation(s) in computer science and engineering."  Srinivasan and Mildenhall, who both currently work at Google Research,  were recognized "for their co-invention of the Neural Radiance Field (NeRF) representation, associated algorithms and theory, and their successful application to the view synthesis problem."  Srinivasan’s dissertation, "Scene Representations for View Synthesis with Deep Learning," and Mildenhall’s dissertation, “Neural Scene Representations for View Synthesis,” addressed a long-standing open problem in computer vision and computer graphics called the "view synthesis" problem:  If you provide a computer with just a few of photographs of a scene, how can you get it to predict new images from any intermediate viewpoint?  "NeRF has already inspired a remarkable volume of follow-on research, and the associated publications have received some of the fastest rates of citation in computer graphics literature—hundreds in the first year of post-publication."

‘Off label’ use of imaging databases could lead to bias in AI algorithms, study finds

A paper with lead author EECS postdoc Efrat Shimron and co-authors EECS graduate student Ke Wang, UT Austin professor Jonathan Tamir (EECS PhD ’18), and EECS Prof. Michael Lustig shows that algorithms trained using "off-label" or misapplied massive, open-source datasets are subject to integrity-compromising biases.  The study, which was published in the Proceedings of the National Academy of Sciences (PNAS), highlight some of the problems that can arise when data published for one task are used to train algorithms for a different one.  For example, medical imaging studies which use preprocessed images may result in skewed findings that cannot be replicated by others working with the raw data.  The researchers coined the term “implicit data crimes” to describe research results that are biased because algorithms are developed using faulty methodology. “It’s an easy mistake to make because data processing pipelines are applied by the data curators before the data is stored online, and these pipelines are not always described. So, it’s not always clear which images are processed, and which are raw,” said Shimron. “That leads to a problematic mix-and-match approach when developing AI algorithms.”

Tiny switches give solid-state LiDAR record resolution

A new type of high-resolution LiDAR chip developed by EECS Prof. Ming Wu could lead to a new generation of powerful, low-cost 3D sensors for autonomous cars, drones, robots, and smartphones. The paper, which appeared in the journal Nature, was co-authored by his former graduate students Xiaosheng Zhang (Ph.D. '21) and Johannes Henriksson (Ph.D. '21), current graduate student Jianheng Luo, and postdoc Kyungmok Kwon, in the Berkeley Sensor and Actuator Center (BSAC).  Their new, smaller, more efficient, and less expensive LiDAR design is based on a focal plane switch array (FPSA) with a resolution of 16,384 pixels per 1-centimeter square chip, which dwarfs the 512 pixels or less currently found on FPSA.  The design is scalable to megapixel sizes using the same complementary metal-oxide-semiconductor (CMOS) technology used to produce computer processors.   Additionally, large, slow and inefficient thermo-optic switches are replaced by microelectromechanical system (MEMS) switches, which are traditionally used to route light in communications networks.  If the resolution and range of the new system can be improved, conventional CMOS production technology can be used to produce the new, inexpensive chip-sized LiDAR.

Alistair Sinclair and Shafi Goldwasser win inaugural STOC Test of Time awards

CS Profs. Alistair Sinclair and Shafi Goldwasser have won inaugural Test of Time awards at the 2021 Symposium on Theory of Computing (STOC), sponsored by the ACM Special Interest Group on Algorithms and Computation Theory (SIGACT).  Sinclair won the 20 Year award for his paper, “A polynomial-time approximation algorithm for the permanent of a matrix with non-negative entries," which solved a problem that had been open for decades. Goldwasser won the 30 Year award for "Completeness theorems for non-cryptographic fault-tolerant distributed computation," which showed how to compute a distributed function even if up to one-third of the participants may be failing, misbehaving, or malicious.  The awards were presented at the 2021 STOC conference in June.

Shafi Goldwasser wins 2021 FOCS Test of Time Award

CS alumna and Prof. Shafi Goldwasser (Ph.D. '84, advisor: Manuel Blum) has won the 2021 Foundations of Computer Science (FOCS) Test of Time Award.  This award "recognizes papers published in past Annual IEEE Symposia on Foundations of Computer Science (FOCS) for their substantial, lasting, broad, and currently relevant impact. Papers may be awarded for their impact on Theory of Computing, or on Computer Science in general, or on other disciplines of knowledge, or on practice."  Goldwasser is among five co-authors who won the award in the 30 year category for their groundbreaking complexity theory paper "Approximating Clique is Almost NP-Complete," which used the classification of approximation problems to show that some problems in NP remain hard even when only an approximate solution is needed. 

He Yin and Murat Arcak win 2019-20 Brockett-Willems Outstanding Paper Award

EECS Prof. Murat Arcak and his graduate student He Yin have won the second Systems & Control Letters (SCL) Brockett-Willems Outstanding Paper Award. Their paper, "Reachability analysis using dissipation inequalities for uncertain nonlinear systems," published in SCL Volume 142, on August 2020, was deemed the best of 295 papers submitted to the journal in the two-year period between January 2019 through December 2020.  Co-authors include former ME Prof. Andrew Packard, who died in 2019, and Packard's former graduate student, Peter Seiler.  SCL hopes to present the award at the 25th International Symposium on Mathematical Theory of Networks and Systems (MTNS) which will be held in Bayereuth, Germany, in September 2022.

Tsu-Jae King Liu

Tsu-Jae King Liu says the U.S. must revitalize semiconductor education and training

EECS Prof. and dean of Engineering Tsu-Jae King Liu has written an opinion piece for the Mercury News in which she explains why "the country urgently needs to reinvest in semiconductor design and manufacturing, including the development of a highly trained workforce."  She argues that America's lack of a skilled semiconductor manufacturing workforce, in the face of a global semiconductor chip shortage, is a matter of national security because it leaves the country vulnerable to geopolitical instability. "Systems that we rely upon for communications, commerce, defense and more are in jeopardy because the United States has lost its leadership in semiconductor manufacturing over the past three decades."  She appeals to Congress to address the issue and says "we need to double the number of students trained in microelectronics graduating today from all U.S. colleges and universities."  This will require "universities across the nation to collaborate with each other and to partner with industry" to create a geographically-distributed American Semiconductor Academy "with participating schools sharing curricula, facilitating access to industry-leading software tools and coordinating hands-on training for students."