Two proposals led by CS Assistant Teaching Professor Narges Nourozi have won $4M in funding from the National Science Foundation Directorate for Computer and Information Science and Engineering (CISE) and Directorate for STEM Education (EDU). The proposals, “Transforming Introductory Computer Science Instruction with an AI-Driven Classroom Assistant” and “CUE-P: Establishing Servingness in Computing through Baskin Engineering Excellence Scholars Program” have been awarded approximately $2M over four years, and $1.93M over five years, respectively. The first proposal, INSIGHT, is a collaboration between North Carolina State University and UC Berkeley focusing on an AI-driven classroom assistant that holds significant transformative potential for yielding a deeper understanding of how students learn computer science with AI-driven classroom assistants and producing a set of practical instructional support principles for coding-enriched classroom interactions. The second proposal is a CUE Pathways project, wherein researchers from the Universities of California collaborate with eight California community colleges to study the effects of operationalizing servingness and transfer pathways between two- and four-year institutions to increase persistence, knowledge attainment, belongingness, graduation, and post-graduation outcomes.

Graduate students Nathan Brooks, Samantha Coday, Maggie Blackwell, Rose Abramson, and post-doc Nathan Ellis have won the IEEE COMPEL Best Paper Award for their paper, "Operation of Flying Capacitor Multilevel Converters At and Above Resonance." The paper was presented at the 23rd IEEE Workshop on Control and Modeling for Power Electronics (COMPEL), which took place in Tel Aviv, Israel. COMPEL is the premier conference on the latest advances in modeling, simulation, analysis, and control of power electronics devices, circuits and systems. The criteria for the award are based on the quality of the technical results, write-up, and presentation. The paper describes a new method for operating flying capacitor multilevel converters at and above resonance, which has proven to be more efficient and with better performance than existing methods. In addition to the best paper award, the group, advised by Professor Robert Pilawa-Podgurski, organized and presented a tutorial at the IEEE 7th Southern Power Electronics Conference (SPEC) in December 2022.

Alishba Imran, a 1st-year undergraduate student studying computer science, was named in Teen Vogue’s 21 under 21. The list recognizes those “who have made a substantial impact in both their communities and the world.” Imran, an undergraduate researcher in CS Prof. Ken Goldberg’s AUTOLab, focuses her work on using machine learning to solve real-world problems, like tracking counterfeit medication in the supply chain or using machine learning and physics to develop renewable energy storage devices. “I think the best things to work on are at the intersection of what you're good at, what you enjoy, and are a way for you to create value for the world,” said Imran.

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."