Allen School News

While the Allen School’s annual Research Showcase and Open House highlights both the breadth and depth of computing innovation at the state’s flagship university, the 2024 event at the University of Washington last week had a decidedly AI flavor. From a presentation on advances in AI for medicine, to technical sessions devoted to topics such as safety and sustainability, to the over 100 student research projects featured at the evening poster session, the school’s work to advance the foundations of AI and its ever-expanding range of applications took center stage. Read more →

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Pets can do more than just provide us with companionship and cuddles. Our love for our pets can improve science education and lead to innovative ways to use augmented reality (AR) to see the world through a canine or feline friend’s eyes. In a paper titled “Reconfiguring science education through caring human inquiry and design with pets”, a team of researchers led by Allen School professor Ben Shapiro introduced AR tools to help teenage study participants in a virtual summer camp design investigations to understand their pets’ sensory experiences of the world around them and find ways to improve their quality of life. The paper won the 2023 Outstanding Paper of the Year Award from the Journal of the Learning Sciences. Read more →

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Would you call your favorite fizzy drink a soda or a pop? Just because you speak the same language, does not mean you speak the same dialect based on variations in vocabulary, pronunciation and grammar. And whatever the language, most models used in artificial intelligence research are far from an open book, making them difficult to study. At the 62nd Annual Meeting of the Association for Computational Linguistics in August, Allen School researchers took home multiple awards for their work to address these challenges. Their research ranged from introducing more dialects into language technology benchmarks to evaluating the reliability and fairness of language models and increasing the transparency and replicability of large language model training as well as evaluations across languages. Read more →

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Trying to work or record interviews in busy and loud cafes may soon be easier thanks to new artificial intelligence models. A team of University of Washington, Microsoft and AssemblyAI researchers led by Allen School professor Shyam Gollakota, who heads the Mobile Intelligence Lab, built two AI-powered models that can help reduce the noise. By analyzing turn-taking dynamics while people are talking, the team developed the target conversation extraction approach that can single out the main speakers from background audio in a recording. Similar kinds of technology may be difficult to run in real time on smaller devices like headphones, but the researchers also introduced knowledge boosting, a technique whereby a larger model remotely helps with inference for a smaller on-device model. Read more →

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Determining protein sequences, or the order that amino acids are arranged within a protein molecule, is key to understanding their role in different biological processes and diseases. In a recent paper published in the journal Nature, a team of University of Washington researchers introduced a new approach to long-range, single-molecule protein sequencing using commercially available devices from Oxford Nanopore Technologies. The team, led by senior author and Allen School research professor Jeff Nivala, demonstrated how to read each protein molecule by pulling it through a nanopore sensor multiple times to increase sequencing accuracy. Read more →

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In a recent paper published in the journal Nature Medicine, a team of researchers at the University of Washington and Stanford University co-led by Allen School professor Su-In Lee introduced a medical concept retriever, or MONET, that can connect images of skin diseases to semantically meaningful medical concept terms. Beyond annotating dermatology images, MONET has the potential to improve transparency and trustworthiness throughout the entire AI development pipeline, from data curation to model development. Read more →

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Urban communities are vibrant centers of economic, cultural and civic activity. Such vibrancy yields a lot of data that could provide a window onto how our urban environments function, but it’s difficult to extract usable insights when the data is stored in different formats, spread across different systems and maintained by different agencies. Allen School professor Jon Froehlich is pursuing a more unified approach that will democratize data analysis and exploration at scale and empower urban communities as part of a new, five-year project dubbed OSCUR — short for Open-Source Cyberinfrastructure for Urban Computing — that recently earned a $5 million grant from the National Science Foundation. Read more →

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When Miranda Wei attended her first Symposium on Usable Privacy and Security (SOUPS) conference in 2017, she had little experience in the field; she had only recently graduated with a degree in political science from the University of Chicago. But the community of researchers at the conference welcomed her in. That experience paved the way for her to continue doing research on privacy and security and, eventually, to pursue a Ph.D. at the Allen School. Seven years after her first foray into the SOUPS community, Wei received the 2024 John Karat Usable Privacy and Security Student Research Award at the conference for her interdisciplinary contributions to the field, efforts to mentor others and community service. Read more →

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In the Marvel Universe, mutants known as the X-Men wield superhuman abilities ranging from shape-shifting to storm-summoning. In the software universe, mutants may not bring the thunder, but they are no less marvelous. In 2014, Allen School professors René Just and Michael Ernst, along with their collaborators, demonstrated that mutants function as an effective substitute for real defects in software testing. Their work earned them the Most Influential Paper Award at the recent ACM International Conference on the Foundations of Software Engineering (FSE 2024). Read more →

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When you reach out to pet a dog, you expect it to feel soft. If it doesn’t feel like how you expect, your brain uses that feedback to inform what you do next. For Allen School professor Rajesh Rao, perception and action are closely intertwined, and their relationship can be mapped using a computational algorithm. In a paper published in the journal Nature Neuroscience, Rao suggested that the brain uses active predictive coding (APC) to understand the world and break down complicated problems into simpler tasks using a hierarchy. This artificial intelligence-inspired architecture could be used to help train AI algorithms on increasingly complex problems with less data. Read more →

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