Allen School News

Rules are vital for building a safe and healthy functioning online community, and Reddit is no exception. For community moderators, however, it can be difficult to make data-driven decisions on what rules are best for their community. A team of Allen School researchers conducted the largest-to-date analysis of rules on Reddit looking at over 67,000 rules and their evolution across more than 5,000 communities over a period of five years. The team presented their work titled “Reddit Rules and Rulers: Quantifying the Link Between Rules and Perceptions of Governance Across Thousands of Communities” at the 2025 International AAAI Conference on Web and Social Media (ICWSM 2025) and received the Best Paper Award. Read more →

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There has been a lot of chatter lately, online and in various media outlets, about the supposed dwindling prospects for new computer science graduates in the artificial intelligence era. Recent layoffs in the technology sector have students, parents and educators worried that a degree in computing, once seen as a sure path to a fulfilling career, is no longer a reliable bet. In this Q&A, Allen School professors Magdalena Balazinska and Dan Grossman examine the myths and realities surrounding AI and the prospects for current and future Allen School majors. Read more →

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Back in 2011, a team of University of Washington and University of California San Diego researchers published a paper detailing how they could remotely hack into and take control of a pair of 2009 Chevy Impalas through a range of attack vectors including CD players, Bluetooth and cellular radio. Since its publication, the team’s research has helped lead to new standards for motor vehicle security and put the brakes on automobile cyberattacks. For their lasting contributions, their paper titled “Comprehensive Experimental Analyses of Automotive Attack Surfaces” received the Test of Time Award at the 34th USENIX Security Symposium in Seattle earlier this month. Read more →

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The Allen School has teamed up with the Allen Institute for AI (Ai2) on a new project aimed at developing the first fully open set of artificial intelligence tools to accelerate scientific discovery and enhance the United States' leadership in AI innovation. The U.S. National Science Foundation (NSF) and NVIDIA announced a combined investment of $152 million n the Open Multimodal AI Infrastructure to Accelerate Science (OMAI) project, including $75 million awarded through the NSF’s Mid-Scale Research Infrastructure program. Read more →

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Understanding how different parts of the brain communicate is like trying to follow conversations at a crowded party. Neuroscientists face a similar challenge: even when they can record signals from multiple brain regions, it is difficult to figure out who is “talking” to whom and what is being said. In a recent paper published at the 2025 International Conference on Machine Learning (ICML), a team of researchers led by Allen School professor Matt Golub developed a new machine learning technique called Multi-Region Latent Factor Analysis via Dynamical Systems (MR-LFADS) to decode how different parts of the brain talk to each other even when some parts can’t be directly observed. Read more →

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Flying insect robots (FIRs) have the potential for use in search and rescue operations, environmental monitoring and even space missions due to their small size and low material cost. The problem, however, is finding the minimum sensor suite and computation resources, or avionics, needed for the robot to maintain flight and control. Allen School undergraduates Joshua Tran and Claire Li didn’t let that challenge bug them — the two were part of a team in the UW’s Autonomous Insect Robotics Lab that earned the ICRA Best Student Paper for TinySense, the current lightest avionics system with the potential for FIR sensor autonomy. Read more →

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The cardinality estimation problem, or the challenge of accurately predicting the size of the output to a query without actually evaluating the query, is one of the oldest and most important problems in databases and data management. Cardinality estimation helps guide decisions on every aspect of query execution, however, current methods can often have large errors, leading to poor decisions downstream. To address this, a team of researchers led by Allen School professor Dan Suciu of the UW Database Group introduced a new pessimistic cardinality estimator called LpBound which provides a guaranteed upper bound on the query output size, and received a SIGMOD Best Paper Award for their work. Read more →

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Magdalena Balazinska, professor and director of the Allen School, has been elected a member of the Washington State Academy of Sciences (WSAS) in recognition of her “contributions in data management for data science, big data systems, cloud computing, and image/video analytics and leadership in data science education.” The WSAS was established in 2015 as a source of independent, evidence-based scientific and technical advice for state policy makers, modeled after the National Academies of Science, Engineering and Medicine. Balazinska, who was directly elected by her WSAS peers, is one of 36 members in the 2025 class. Read more →

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Allen School professor Abhishek Gupta is interested in developing ways to help robots learn new skills with minimal human help and engineering. Gupta joined the Allen School faculty in 2022, and already he has introduced research that has shaped the future of robotics. His contributions to the field earned him the IEEE Robotics & Automation Society (RAS) Early Academic Career Award in Robotics and Automation where the organization recognized him “for pioneering contributions to real world robotic reinforcement learning.” Read more →

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Allen School Ph.D. student Cheng-Yu Hsieh is interested in tackling one of the biggest challenges in today’s large-scale machine learning environment — how to make artificial intelligence development more accessible. Large foundation models trained on massive datasets have revolutionized AI, however, but these scaling efforts are often out of reach for many except for well-resourced companies. With support from a Google Ph.D. Fellowship, Hsieh is working to make data and model scaling more efficient and affordable to help democratize AI development. Read more →

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