Allen School News

Each year, the InfoSys Science Foundation (ISF) recognizes the achievements and contributions of researchers and scientists of Indian origin who are making waves in their field and beyond. Allen School professor Shyam Gollakota, who leads the Mobile Intelligence Lab, received this year’s InfoSys Prize in Engineering and Computer Science for his research that uses artificial intelligence to change the way we think about speech and audio. He is among one of six award winners who will be honored at a ceremony in Bangalore, India, next month and receive a prize of $100,000. Read more →

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A team of researchers in the Allen School’s UbiComp Lab and UW School of Medicine developed the app FeverPhone that turns smartphones into thermometers without the need for additional hardware. The research, which was originally published in the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, received an IMWUT Distinguished Paper Award at the ACM International Joint Conference on Pervasive and Ubiquitous Computing/International Symposium on Wearable Computing (UbiComp/ISWC) in October. Read more →

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In honor of the late Allen School professor Gaetano Borriello, whose work focused on applying mobile technologies to tackle issues of global equity and social and environmental justice, the ubiquitous computing community each year recognizes Ph.D. students who are following in his footsteps. This year, those footsteps led back to the Allen School when Ph.D. student Anandghan Waghmare won the 2024 Gaetano Borriello Outstanding Student Award at the ACM International Joint Conference on Pervasive and Ubiquitous Computing/International Symposium on Wearable Computing. Read more →

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“Having traveled to and experienced different countries, I bring a more practical and global outlook to computer science. This definitely gives me a different understanding and appreciation for the subject compared to some of my peers.” That sentiment, offered by Fei Huang, veteran and current undergraduate, is one of the themes we found among Allen School students with military backgrounds. To mark Veterans Appreciation Week at the University of Washington, we spoke with three students on how being a veteran gives them a unique perspective on their studies and future career path. Read more →

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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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