CS Profs. Dawn Song and David Wagner have won the Association for Computing Machinery (ACM) Special Interest Group on Security, Audit and Control (SIGSAC) Test-of-Time Award. The 2011 paper titled, “Android Permissions Demystified,” by Felt, Chin, Hanna, Song and Wagner, was the first paper to examine real-world security issues in Android applications' use of permissions. The paper has been cited 1985 times and is still taught in graduate courses today. The award was presented at the ACM Conference on Computer and Communications Security (CCS), the flagship conference of the ACM SIGSAC, which took place in Los Angeles this year.

Theory Ph.D. student Fred Zhang (advisor: Jelani Nelson) has won the Best Student Paper Award at ACM-SIAM Symposium on Discrete Algorithms (SODA) 2023. The paper titled, “Online Prediction in Sub-linear Space'' was co-authored by Binghui Peng of Columbia University. The ACM-SIAM Symposium on Discrete Algorithms, or “SODA,” conference showcases “research topics related to design and analysis of efficient algorithms and data structures for discrete problems.” 

CS and IEOR Prof. Ken Goldberg and his lab at BAIR have won multiple best paper awards this year. “Autonomously Untangling Long Cables” won the Best Systems Paper at the Robotics: Science and Systems (RSS) conference in June 2022. “Automated Pruning of Polyculture Plants” won Best Paper at the IEEE Conference on Automation Science and Engineering (CASE) in August 2022. At this year’s IEEE International Conference on Intelligent Robots and Systems (IROS), held in October, the paper titled, “Speedfolding: Learning Efficient Bimanual Folding of Garments” took the top spot out of 3500 submissions to win the IROS Best Paper Award. The common thread among these results is the application of advances in deep learning to solve robot manipulation problems. “I feel lucky every day that I get to work in this uniquely stimulating environment with the world's most brilliant, creative, and dedicated students, staff, and faculty," said Goldberg.

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.”

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 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.

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."

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.”

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.

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.