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Seeing the world through your pet’s eyes: Allen School professor Ben Shapiro receives JLS 2023 Outstanding Paper of the Year Award

A black dog lies in front of a row of toys and picks out a frog toy to play with. 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 →
October 22, 2024

A way with words and trillions of tokens: Allen School researchers recognized at ACL 2024 for expanding the capabilities of language models

An artist’s illustration of artificial intelligence (AI). This illustration depicts language models which generate text. 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 →
October 8, 2024

Allen School researchers showcase speech and audio as the new frontier for human-AI interaction at Interspeech 2024

An overhead view of people sitting around laptops and phones working on a project. 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 →
September 19, 2024

Singular sensation: Allen School researchers develop new method for sequencing proteins using nanopores

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 →
September 12, 2024

MONET helps paint a clearer picture of medical AI systems

A glowing brain emerges from a stack of books flinging pages with dermatology images around. 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 →
September 11, 2024

Pushing beyond the silos: Allen School’s Jon Froehlich aims to build a unified approach to urban science as part of new NSF-funded project

Urban street scene depicting hot pink streetcar entering an intersection, with a car on the opposite side of the road and painted pedestrian crossing visible against a backdrop of street trees and low-rise buildings, with high-rise buildings in distant background 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 →
September 3, 2024

Looking into places others are not: Allen School’s Miranda Wei receives 2024 Karat Award for contributions to usable security and privacy

Headshot of Miranda Wei wearing a black sweater against a brick background 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 →
August 26, 2024

Marvelous mutants: Allen School’s René Just and Michael Ernst receive FSE Most Influential Paper Award for showing the validity of mutants in software testing

A large bolt of lightning illuminates clouds in a bluish-purple sky 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 →
August 21, 2024

Mind over model: Allen School’s Rajesh Rao proposes brain-inspired AI architecture to make complex problems simpler to solve

A glowing hologram of a brain emerges from a circuit board. 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 →
August 19, 2024

NSF CAREER Award recipients Vikram Iyer and Adriana Schulz seek to expand capabilities in robotic sensing and computer-aided design with AI

Side-by-side portraits of Vikram Iyer and Adriana Schulz When it comes to miniature robots, Allen School professor Vikram Iyer has big ideas about autonomous navigation and the applications that will enable — applications for which he seeks to ditch the batteries and add onboard perception and computation with the help of artificial intelligence. Meanwhile, his faculty colleague Adriana Schulz has designs on a different kind of power problem: how to use AI to supercharge a new era of creativity and eco-consciousness in computer-aided manufacturing. Schulz and Iyer recently earned National Science Foundation (NSF) CAREER Awards for research that promises to fundamentally alter the way we create and interact with objects and our environment. Read more →
July 30, 2024

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