MuSHR is a low-cost, open-source robotic racecar platform for education and research, developed by the Personal Robotics Lab in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. MuSHR aspires to contribute towards democratizing the field of robotics as a low-cost platform that can be built and deployed by following detailed, open documentation and do-it-yourself tutorials. A set of demos and lab assignments developed for the Mobile Robots course at the University of Washington provide guided, hands-on experience with the platform, and milestones for further development. MuSHR is a valuable asset for academic research labs, robotics instructors, and robotics enthusiasts.

Yejin Choi, a University of Washington computer science professor and senior research manager at Seattle's Allen Institute for Artificial Intelligence (AI2), won a $800,000 "genius grant" given annually by the John D. and Catherine T. MacArthur Foundation. Choi, one of 25 MacArthur Fellows for 2022 revealed Wednesday, is an expert in natural language processing. Her work aims to improve the ability of computers and artificial intelligence systems to perform commonsense reasoning and understand implied meaning in human language. "This is such a great honor because there have been only two other researchers in the natural language processing field who have received this award," Choi told UW News. Choi spoke to GeekWire earlier this year about the debate over a robot's ability to have human-like feelings.

As meetings shifted online during the COVID-19 lockdown, many people found that chattering roommates, garbage trucks and other loud sounds disrupted important conversations. This experience inspired three University of Washington researchers, who were roommates during the pandemic, to develop better earbuds. To enhance the speaker's voice and reduce background noise, "ClearBuds" use a novel microphone system and one of the first machine-learning systems to operate in real time and run on a smartphone. The researchers presented this project June 30 at the ACM International Conference on Mobile Systems, Applications, and Services. "ClearBuds differentiate themselves from other wireless earbuds in two key ways," said co-lead author Maruchi Kim, a doctoral student in the Paul G. Allen School of Computer Science & Engineering.

We are a research group at the Paul G. Allen School of Computer Science of Engineering. Our aim is to explore and understand behavior through the lens of data science. The Behavioral Data Science Group develops computational methods that leverage large-scale behavioral data to extract actionable insights about our lives, health and happiness through combining techniques from data science, social network analysis, and natural language processing. We currently work on research related to mental health, misinformation online, scientific reproducibility, and informing the COVID-19 response. We have a postdoc position available.

Relying on shortcuts for work isn't always a great idea – and that holds true for artificial intelligence (AI) with chest X-rays and COVID-19, as well. New research from the University of Washington (UW), shows that AI, like humans, has a tendency to lean on shortcuts for disease detection with these scans. If the tools are deployed clinically, investigators said, the result could be diagnostic errors that impact real patients. Rather than learning from actual medical pathology and clinically significant indicators, the team, led by Paul G. Allen School of Computer Science & Engineering doctoral students, Alex DeGrave, who is also a medical student in the UW Medical Scientist Training Program, and Joseph Janizek, also a UW medical student, showed that algorithms used during the pandemic relied on text markers and patient positioning specific to each dataset to predict whether someone was COVID-19-positive. The team published their results May 31 in Nature Machine Intelligence.

Artificial intelligence promises to be a powerful tool for improving the speed and accuracy of medical decision-making to improve patient outcomes. From diagnosing disease, to personalizing treatment, to predicting complications from surgery, AI could become as integral to patient care in the future as imaging and laboratory tests are today. But as University of Washington researchers discovered, AI models -- like humans -- have a tendency to look for shortcuts. In the case of AI-assisted disease detection, these shortcuts could lead to diagnostic errors if deployed in clinical settings. In a new paper published May 31 in Nature Machine Intelligence, UW researchers examined multiple models recently put forward as potential tools for accurately detecting COVID-19 from chest radiography, otherwise known as chest X-rays.

Researchers at the University of Washington have figured out a way to use machine-learning algorithms to turn smart speakers into sensitive medical devices that can detect irregular heartbeats. The scientists use smart speakers like Amazon Echo or Google Home to send out an inaudible sound that bounces off a person's chest and returns to the device, reshaped in a way that reveals the heartbeat. An uneven cardiac rhythm can be associated with ailments including strokes or sleep apnea. The researchers employed a machine-learning algorithm to tease out the heartbeats from other sounds and signals such as breathing, which is easier to detect because it involves a much larger motion. The algorithm was also needed to zero in on erratic heart rhythms -- which from a health perspective are generally more important to identify than a steady "lub-dub."

Allen School professor Adriana Schulz and adjunct professor Nadya Peek are among the 35 "Innovators Under 35" recognized by MIT Technology Review as part of its 2020 TR35 Awards. Each year, the TR35 Awards highlight early-career innovators who are already transforming the future of science and technology through their work. Schulz, a member of the Allen School's Graphics & Imaging Laboratory (GRAIL) and Fabrication research group, was honored for her visionary work on computer-based design tools that enable engineers and average users alike to create functional, complex objects. Peek, a professor in the Department of Human-Centered Design & Engineering, was honored in the "Inventors" category for her work on modular machines for supporting individual creativity. Schulz and Peek are also among the leaders of the new cross-campus Center for Digital Fabrication (DFab), a collaboration among researchers, educators, industry partners, and the maker community focused on advancing the field of digital fabrication.

Benjamin Lee, a second-year Ph.D. student in the Allen School's Artificial Intelligence group working with professor Daniel Weld, has been named a 2020 Innovator in Residence by the Library of Congress. Now in its second year, the Innovator in Residence program aims to enlist artists, researchers, journalists, and others in developing new and creative ways of using the library's digital collections. During his residency, Lee will apply deep learning to enable the automatic extraction and tagging of photographs and illustrations contained in the more than 15 million newspaper scans comprising the library's Chronicling America collection. His goal is to produce interactive visualizations, searchable by topic, that will make the content more accessible to users and support cultural heritage research. "A primary motivation behind my project is to excite the American public by demonstrating the possibilities of applying machine learning to library collections," Lee explained in an interview posted on the library's blog.

The College of Engineering Awards acknowledge the extraordinary efforts of the college's teaching and research assistants, staff, and faculty members. The College of Engineering Awards ceremony scheduled for April 20 has been canceled. Since joining UW in 2014, Cole DeForest has established himself as an innovative researcher, an effective teacher and a collaborative colleague, holding appointments in Chemical Engineering, Bioengineering, and the Institute for Stem Cell & Regenerative Medicine. His research focuses on the development of (de)polymerization reactions that can be triggered using light in the presence of cells, and "represents a major advancement in cell culture niches that allow unprecedented control of the cellular microenvironment, and is enabling him to conduct newfound experiments that were previously impossible." Cole has received numerous honors, including an NSF Career Award, a Young Investigator Award through the American Chemical Society, and a UW Presidential Distinguished Teaching Award.