If you shop on Google Flights, you get a quick comparison for different itineraries: One flight’s carbon emissions may be average, while another’s are 14% higher. But if you go shopping for a new laptop, you likely won’t find quick, comprehensible information on different models’ sustainability bonafides, despite the of producing and discarding electronics. In part, that’s because understanding a device’s emissions is difficult and time-consuming, even for experts.��

����ԭ�� researchers developed an artificial intelligence system that automatically estimates the environmental impacts of making different electronic devices. The system uses AI agents — programs that perform tasks autonomously — to comb through publicly available data and conduct life cycle assessments, or LCAs. The system achieves an average error rate of 5%-19%, similar to the accuracy of LCAs conducted by experts.

The team June 12 in Nature Electronics.��

AI agents have recently grown increasingly capable of performing complex tasks. Today’s agents can search the web and pull information about electronic parts from product descriptions, images and documents.��

“Some of our previous research made me curious about how LCA experts perform environmental assessments — and whether that process could be automated,” said lead author , a UW doctoral student in the Allen School. “So to understand the bottlenecks firsthand, and then built a system that emulates these interactions with two AI agents. Each of them mimics different roles in the LCA process.”

One agent acts as a sort of analyst, defining what information needs to be gathered and how it will fit together. It also reviews results for accuracy. The second agent is more like an engineer. It scrapes publicly available data for information on an electronic device’s components. That might entail sifting through spreadsheets, or looking up images of the insides of devices and taking chip information from them — including from sources not typically used for LCAs, such as and posts on.��

The two agents work in a loop. The first sets the scope, the second gathers information. The first then looks that information over and might send the second agent searching again, and so on. The agents then reference to convert the complete list of parts to carbon estimates.

The team also developed a new method to bypass this detailed data collection and directly estimate carbon footprints. For common devices like laptops and smartphones with publicly available carbon footprint reports, they found that products with similar specs like screen size and processors clustered around similar carbon values, because only a handful of companies make specialized parts for all these devices. So an unknown device’s footprint can be represented as a weighted average of similar products.��

They also use this to estimate the carbon for materials not in LCA databases. For example, a new type of sustainable plastic could be estimated based on plastics with similar properties and chemistry.

“We tried this ‘nearest-neighbors’ approach and found that for materials, it’s actually better than the standard approach of a human picking the single closest entry,” said Zhang. “When estimating missing emissions factors in a test, the average error for our method was 23%. Human experts had an average error of 143%.” 

The authors note that while the aim of the system is to help reduce carbon emissions overall, running AI models requires energy, so they’ve taken several steps to mitigate its impact. They use small AI models that aren’t as energy-intensive as general-purpose models. They also start the process by running a search to see if the device’s estimated emissions have already been calculated. If so, it can stop there. If the system does need to call its AI models repeatedly, estimating a device’s carbon footprint is currently on par with the emissions generated by brewing a cup of tea.

The team plans to collaborate with companies in the future to help automate their workflows.��

“A lot of big companies have sustainability teams that perform these LCAs,” Iyer said. “Our hope is that automating this will actually free up their time, so they can spend their time reducing the carbon footprint of the products themselves, instead of hunting down elusive stats.” 

Co-authors include , a UW student in the Allen School;, , a UW postdoctoral researcher in the Allen School; , a UW doctoral student in the Allen School; , a UW professor in the Allen School; of Wesleyan University, who completed this research as a UW doctoral student in the Allen School; of the University of Notre Dame; of Northeastern University; and of Brown University, who completed this research as a UW assistant professor in the Allen School.��

This research was funded by Amazon Research Awards and the National Science Foundation. Zhang was supported by the .

For more information, contact Iyer at vsiyer@uw.edu and Zhang at zzhihan@cs.washington.edu.

Quantum materials are a class of exotic materials with special properties that are governed by rather than . Those properties — like , and unusual forms of magnetism — often originate in the tiny repeating patterns of atoms inside crystals, but through clever engineering they can be observed and controlled at a more human scale. Quantum materials are helping to power the quickly growing field of , and could find their way into future generations of energy-efficient electronics.��

Designing new materials from the atomic scale up, however, requires intense modeling and simulation. Some materials may appear ordinary when viewed as small clusters of atoms, yet reveal new and useful properties when their atomic building blocks repeat and interact over larger distances. Researchers must be able to accurately predict behaviors at large scales in order to find materials with practical applications — otherwise designing new materials is a slow and costly trial-and-error process.

In the past 50 years, supercomputers have helped materials scientists solve some of those thorny prediction problems, but two recent studies from the ����ԭ�� demonstrate how newer computing techniques can help researchers sniff out promising quantum materials to pursue. , published June 2 in the Proceedings of the National Academy of Sciences, shows how researchers can use artificial intelligence to simulate dozens of sheets of atoms stacked in intricate patterns, a process that produces complex and potentially useful quantum behaviors. , published June 8 in Nature Communications, shows how quantum computers can create a self-improving design loop by discovering new materials that could themselves be components of future quantum computers.��

“What is exciting is that AI and quantum computing are beginning to change not just what problems we can solve, but how we do research,” said , a UW associate professor of materials science and engineering and the senior author of both studies.

These two new tools — AI and quantum computing — are complementary in that they each excel at a different kind of simulation problem. With the right training, an AI model can act as a fast and relatively inexpensive surrogate of a supercomputer, extrapolating the behavior of huge material systems from a relatively small dataset. Cao and collaborators used this approach to stack virtual sheets of atoms on top of one another over and over — a process that created completely new phenomena that were absent on a smaller scale, but would have been impractical to model by traditional supercomputing. From there, researchers can try to make the most promising materials in the lab to prove out the simulations.

Quantum computers, on the other hand, are essentially powered by the same quantum phenomena — like entanglement — that Cao and other materials researchers want to study. Such phenomena can be difficult to simulate using traditional computers or AI systems, but quantum computers are naturally suited to the task. In the study, Cao and his team used a quantum computer to study an exotic phase of matter known as a .��

Moving forward, Cao and his team plan to further build out their datasets and eventually develop models that can simulate a much wider range of materials. They also hope to combine their AI and quantum computing systems into a more powerful and flexible hybrid tool.

“The next step is to bring these tools together,” Cao said. “We can use AI to guide quantum simulations, and quantum computers to generate new data and insights that improve AI models.”

“We are at the start of a new era,” said , UW professor and chair of materials science and engineering and co-author of both studies. “Our field is fundamentally changing. Things that were literally impossible a couple of years ago are now becoming routine. And we are only beginning to see what AI and quantum computing will make possible for quantum materials.”

was led by , a UW doctoral student of materials science and engineering. was led by , a UW doctoral student of physics. A complete list of authors is included with the studies.

The authors acknowledge the support of Amazon and the Department of Energy.

For more information, contact Cao at tingcao@uw.edu.

Micropantries — commonly called “little free pantries”  — and community fridges are a frequent sight throughout Seattle and the greater Puget Sound region. One estimate suggests that they supply around 4 million pounds of food per year to neighbors in need in the Seattle area, more than the state’s largest food bank. The curbside cupboards are a decentralized, community-driven effort to fight food insecurity and reduce food waste at the neighborhood level, but their ad hoc nature limits their dependability — users don’t know when food is available without repeatedly checking, and donors don’t know what foods are needed most.

Now, anyone who interacts with micropantries or community fridges in the Seattle area can try out an experimental app, made by ����ԭ�� researchers, that brings a suite of new features to the micropantry network. , maps many local pantries across the region. The app also gives each pantry an activity feed where users can share food they’ve donated, report on stock levels, add requests to a wish list, post photos and leave other notes. The research team also retrofitted some pantries with sensors that anonymously auto-report their usage and stock levels to the app in real time.

“This is an effort to document and quantify the phenomenon of micropantries,” said , a senior research scientist at the UW . “Lots of micropantries and community fridges popped up around the time of the COVID-19 pandemic, and I was curious about who uses them and how they are used.”

The team was cognizant of privacy concerns and designed the smart pantry tech accordingly.

“Putting cameras in the pantries could give us a lot of information about what specific foods are moving through the system, but that may also deter users who are concerned about privacy,” said , a UW doctoral student in the Paul G. Allen School of Computer Science & Engineering who designed and built the sensor suite. “Instead, we settled on simpler sensors that measure weight and interactions like opening the door to measure stock levels while preserving everyone’s anonymity.”

The researchers hope that neighbors will find new ways to connect and help one another through these tools. A user might see that stock levels are low in a nearby pantry, for example, and decide to add some food. Another user might request certain foods to accommodate their dietary restrictions.��

The sensor-equipped pantries are a small subset of the dozens of pantries throughout Seattle, but in addition to providing some neighborhoods with enhanced food tracking, they will generate aggregate data that will help Dalla Chiara’s team study donor and usage behavior. Dalla Chiara also plans to survey donors to learn more about what motivates people to provide food to pantries.

“We know that there is a lot of food insecurity in Seattle and in the United States in general,” Dalla Chiara said. “But we know that there is also a lot of food waste — lots of people have a surplus of food. And we want to see how grassroots efforts like micropantries can address both food insecurity and waste at the same time.”

Dalla Chiara and his team recently completed a refit on a cold, sleeting March day at a pantry owned by Saint Paul’s Episcopal Church near Seattle Center. The church keeps the pantry regularly stocked, and rector Stephen Crippen is curious about the data the new system will produce.

“It puts numbers on what we’re actually accomplishing,” Crippen said. “It helps us get in touch with what’s going on on this street.”

The research team is also working with local businesses and nonprofits to encourage and track food distribution throughout the pantry network. In April, Seattle-based recycling startup ran a nonperishable food drive across Seattle and delivered 25,000 pounds of food to the ; from there, volunteers from the Cascade Bicycle Club’s distributed the food to micropantries around the city by bike, giving the network an infusion of both food and usage data. The and the nonprofit helped support the project’s community fridges effort.

Dalla Chiara recognizes that there are other grassroots online, and he doesn’t want his app to replace those services. Nor does he expect the smart pantry network to remain in service indefinitely — it costs about $150 to retrofit each pantry with sensors, and all that tech will be difficult to maintain after the study concludes in October of this year. At its core, the project is an effort to learn about micropantry usage and explore how technology might encourage sharing of resources and mutual aid systems.

“We’re trying to measure and quantify goodwill,” Dalla Chiara said. “Behind each little free pantry there is a whole system of behaviors — people trying to help one another. If we can understand that system better, we can support it better.”

Other UW collaborators include , professor of civil and environmental engineering and director of the Urban Freight Lab; , assistant teaching professor of environmental and occupational health sciences; , assistant professor of food systems, nutrition and health; and , assistant professor in the Allen School.

For more information, contact Dalla Chiara at giacomod@uw.edu.

Artificial intelligence has swept into American schools, and more is sure to come. This year, both Google and Microsoft — the two biggest companies at the forefront of the AI boom — in AI training for teachers.��

But what do teachers think of this transformation of their work?

, a ����ԭ�� professor in the Information School and co-director of the Center for Digital Youth, studies how technology affects young people’s learning and development. Davis has also been teaching for over two decades — first as an elementary school teacher and now as a professor — so she’s acutely aware of how earlier technological revolutions in teaching have not always played out as hoped.

Davis and a UW-led team of researchers interviewed 22 teachers in in Colorado — a district that’s investing heavily in AI through systems like Google’s Gemini and , an AI tool that helps teachers plan. Overall, teachers were ambivalent about the technology. They liked that it could reduce workload, especially for rote tasks, but worried that it could erode the social aspects of teaching.

The team April 15 at the Association for Computing Machinery Conference on Human Factors in Computing Systems in Barcelona.

UW News talked with Davis about the study and how ostensibly democratizing technologies can widen disparities in schools.��

Why did you want to study AI adoption by schools?

Katie Davis: At least since the introduction of the radio, every new technological invention has been hyped for how it will change teaching and learning. Computers are the prototypical example. They were pushed into schools only to start collecting dust, because they didn’t really change anything. We saw it with , too. Ten or 15 years ago, these courses were supposed to transform education and put colleges and universities out of business. But that hasn’t happened.

Often the hype centers on closing educational inequities. But these new technologies actually tend to aggravate existing inequities. The schools serving the most affluent students have the resources to think carefully about how to incorporate technologies into their curriculum so that they’re supporting student learning goals and outcomes, whereas more under-resourced schools don’t have the resources or the time to do that kind of work. So they end up incorporating technologies in ways that don’t necessarily help students learn; instead, they make things more efficient or keep track of students.

When AI started being intensely pushed into schools, I thought here we go again. AI is here and it’s not going anywhere, so I would love for us to understand how it’s being taken up in schools and, ideally, to prevent this recurring pattern.

What did you hear from teachers about AI?

KD: Teachers expressed a deep ambivalence toward AI. It wasn’t as if any one teacher said it’s all great or it’s all terrible. I think the single strongest driver for teachers to use AI was to prevent burnout. Teachers are being asked to do more and more — not just teach, but care for students’ entire emotional, cognitive and academic lives. It really weighs on them. So a lot of them talked about turning to AI to be a thought partner, to help them brainstorm lesson ideas, create assessments and differentiate lessons for different learners.

Another really big benefit for this particular school district was multilingual support. The district serves students who speak more than 160 languages. One teacher we spoke with said she had four main languages represented in her classroom but she only spoke English, so she was turning to AI to help her translate materials for her students and for their families so that she could communicate with them.��

I think it’s really important to note that this district is going all in on AI. They’re encouraging teachers to use it and providing professional development, and teachers are talking among themselves and sharing ideas. This kind of institutional support and more informal teacher conversations are also encouraging teachers to use AI and explore how they might incorporate it into their teaching practice.

AI is often presented as a democratizing technology, but a recently showed that higher wage earners are using AI more than lower wage earners in the same industry — possibly increasing disparities. Are you seeing anything like that playing out in education?

KD: The way that manifests in education is in the kinds of support that students have access to. It’s more likely that better-resourced schools are also going to provide some form of AI literacy instruction — to really engage students in thoughtful reflection about what AI is, how it may or may not be useful for their learning, and to actually get them to think about these issues in a deep way. Whereas in under-resourced schools, the easiest thing to do is to just block AI. That’s not going to prevent students from using it, but they will end up using it in a communication vacuum, without any adult guidance. You can see how that would create disparities in how well students can use it.

I was really interested in the finding that teachers are concerned that students will know they’re using AI.

KD: That is one of the most interesting findings for me. Teachers are definitely aware that if their students think they’ve used AI, students and their parents will feel that their teachers are cheating them out of a proper education. Teachers are very worried about both students and their more AI-resistant colleagues seeing them that way. I don’t think this is unique to teachers — I feel it in university jobs, too. Many people have this perception that using AI is cheating or taking the easy way out.��

But there’s another layer: Teachers are personally worried about their own authentic voice and professional identity. They’re asking, “If I am using AI, at what point am I no longer a teacher? Where’s that line between using AI as a thought partner to augment my professional practice versus it now replacing my professional practice?” 

What are ways schools might amplify the positive parts of using AI while mitigating some of these negative effects?

KD: One of the first things is to bring AI out of the shadows and talk about it. Since we published this piece, I’ve been engaging with groups of teachers around the country in professional development experiences around AI, and they really enjoy having a community of practice. They feel that those spaces don’t necessarily exist in their schools. It’s like there’s this vacuum of communication — students don’t talk about it because they’re implicitly getting the message that it’s not OK to use it, and it’s the same with teachers.

Professional development is also very important. But a lot of professional development for teachers is just one-off PowerPoint presentations. It doesn’t really connect to whatever is going on in the classroom. Professional development needs to be done in a sustained way that meaningfully connects AI to teachers’ immediate classroom experiences.

School leaders need to be able to communicate AI policies, so that teachers are aware of them and understand how they apply in their specific schools. If you take Washington state as an example, the Office of Superintendent of Public Instruction has a really great blueprint and guidance for using AI. But my sense is that not many teachers are aware of it, or even if they are, there hasn’t been any concerted effort to say, “OK, this is what that means in our school.” We need to be working at many levels to make sure that AI is integrated into education well.��

Is there anything you want to add?

KD: Something I hold very dear as a teacher is that teaching is relational. Kids don’t learn in isolation. The gave saying the ideal vision is for every kid on the planet to have their own personal AI tutor and for every teacher to have their own personal AI teaching assistant. Maybe that would be great, but I worry that this push toward AI will erode the relationships between teachers and students. Teaching and learning are social processes. It’s not just about putting information into a student’s brain. Students learn through dialog, through participation in cultural practices. To remove that element of learning really concerns me.

Co-authors include, a UW doctoral student in human centered design and engineering; of Artech and of Rutgers University, both of whom contributed to this research as UW graduate students in the Information School; of Columbia University; of Aurora Public Schools;, a UW associate professor in the Information School;, a UW professor and chair of human centered design and engineering; of Lahore University of Management Sciences; of the University of Colorado Boulder; and of Boston College. This research was supported by a Spencer Foundation Vision Grant and the AI Research Institutes program by the National Science Foundation and the Institute of Education Sciences.

For more information, contact Davis at kdavis78@uw.edu.

Even though he wanted to bike commute from his Capitol Hill home to the ����ԭ��, Jared Hwang often took transit because he struggled to find a good bike route. Apps like Google Maps and Strava might suggest hilly, busy streets simply because they have bike lanes. He even headed to Reddit to crowdsource ideas.��

“I was like, surely, this cannot be the best way to do things,” said , a UW doctoral student in the Paul G. Allen School of Computer Science & Engineering. “This data is out there. We know where bike lanes are, what the roads are like, what the speed limits are. We should be able to easily access all this information at once.”

So Hwang and a team of UW researchers built , a demo web app that lets users find personalized bike routes in Seattle. Cyclists plug in their origin and destination — just like in other mapping apps — and can then create personalized routes by adjusting eight sliders.�� 

Researchers initially worked with four participants to understand how cyclists tend to plan their routes. Based on that, they built a prototype of BikeButler. For the basic street layout and other info, they pulled data from OpenStreetMap and government data sets. But those didn’t have information on more subjective qualities.��

For those, researchers turned to Google Street View. They used a visual language model, or VLM — a type of artificial intelligence — to analyze street images and rate subjective attributes like greenery and pavement quality. The team had the VLM rate the level of greenery on streets and then compared this with two researchers’ ratings. The humans agreed with each other about as much as they agreed with the VLM — about 60% of the time. Future research might try to gather individual users’ greenery preferences to offset this discrepancy.��

Once they’d mapped most of Seattle, the team tested the prototype with 16 participants.��

“Overall the response was really positive,” Hwang said. “We found that people do, in fact, have contextual preferences. A cyclist riding for fun on a Saturday might want a safer, greener route compared with their fast work commute. People intuitively know this, but it hadn’t been established through research.” 

Researchers say future work might integrate feedback from the user study, such as the ability to drag routes to change them slightly and an option to take fewer turns. The team is currently studying how to quantify cyclists’ preferences around intersections and turns.

The researchers note that the quality of BikeButler’s recommendations is constrained by the recency and accuracy of the data it uses. For instance, a new bike lane might not yet appear on a map, or it could appear in OpenStreetMap but not Google Street View. Also, since the team planned this as a proof of concept, BikeButler is limited to Seattle, though it could be expanded to other areas.��

“I’m a lifelong biker and bike commuter,” said senior author , a UW professor in the Allen School. “What excites me most about Jared’s work is how it points to a future where we receive route choices individualized to our preferences. So whether I’m biking with my two young children, or riding for groceries, I can find a route for that context.”

Co-authors include , a student at Issaquah High School and intern in the Allen School; , a UW doctoral student in urban design and planning; and , a UW student in the Allen School. This study was supported by the National Science Foundation.

For more information, contact Hwang at jaredhwa@cs.washington.edu.

����ԭ�� researchers developed the first system that incorporates tiny cameras in off-the-shelf wireless earbuds to allow users to talk with an AI model about the scene in front of them. For instance, a user might turn to a Korean food package and say, “Hey Vue, translate this for me.” They’d then hear an AI voice say, “The visible text translates to ‘Cold Noodles’ in English.”

The prototype system called VueBuds takes low-resolution, black-and-white images, which it transmits over Bluetooth to a phone or other nearby device. A small artificial intelligence model on the device then answers questions about the images within around a second. For privacy, all of the processing happens on the device, a small light turns on when the system is recording, and users can immediately delete images.��

The team will April 14 at the Association for Computing Machinery Conference on Human Factors in Computing Systems in Barcelona.��

“We haven’t seen most people adopt smart glasses or VR headsets, in part because a lot of people don’t like wearing glasses, and they often come with , such as recording high-resolution video and processing it in the cloud,” said senior author , a UW professor in the Paul G. Allen School of Computer Science & Engineering. “But almost everyone wears earbuds already, so we wanted to see if we could put visual intelligence into tiny, low-power earbuds, and also address privacy concerns in the process.”

Cameras use far more power than the microphones already in earbuds, so using the same sort of high-res cameras as those in smart glasses wouldn’t work. Also, large amounts of information can’t stream continuously over Bluetooth, so the system can’t run continuous video.��

The team found that using a low-power camera — roughly the size of a grain of rice — to shoot low-resolution, black-and-white still images limited battery drain and allowed for Bluetooth transmission while preserving performance.

There was also the matter of placement.��

“One big question we had was: Will your face obscure the view too much? Can earbud cameras capture the user’s view of the world reliably?” said lead author , who completed this work as a UW doctoral student in the Allen School.��

The team found that angling each camera 5-10 degrees outward provides a 98-108 degree field of view. While this creates a small blind spot when objects are held closer than 20 centimeters from the user, people rarely hold things that close to examine them — making it a non-issue for typical interactions.

Sixteen participants also wore VueBuds and tested the system’s ability to translate and answer basic questions about objects. VueBuds achieved 83-84% accuracy when translating or identifying objects and 93% when identifying the author and title of a book.

This study was designed to gauge the feasibility of integrating cameras in wireless earbuds. Since the system only takes grayscale images, it can’t answer questions that involve color in the scene.��

The team wants to add color to the system — color cameras require more power — and to train specialized AI models for specific use cases, such as translation.�� 

“This study lets us glimpse what’s possible just using a general purpose language model and our wireless earbuds with cameras,” Kim said. “But we’d like to study the system more rigorously for applications like reading a book — for people who have low vision or are blind, for instance — or translating text for travelers.” 

Co-authors include , a UW master’s student in the Allen School, and , , , and , all UW students in electrical and computer engineering.��

For more information, contact vuebuds@cs.washington.edu.

Even on a campus like the ����ԭ��’s — home to particle accelerators, wave tanks and countless other bespoke pieces of equipment — the machinery in the stands out. Take the dilution fridge, a large, white, cylindrical device that can cool a small chamber to one hundredth of a kelvin above absolute zero — the coldest possible temperature in the universe.��

“This is the coldest fridge money can buy,” said , a UW assistant professor of electrical and computer engineering and the former director of the lab, which goes by the nickname QT3. “When it’s running, the chamber inside this device is about 100 times colder than outer space. At that temperature, it’s much easier to study and manipulate a material’s quantum properties.”

The lab also houses a photon qubit tabletop lab: a nondescript set of boxes, lasers and lenses that can demonstrate the “spooky” — a term scientists actually use — phenomenon known as quantum entanglement, where two particles appear to communicate instantaneously with each other despite being physically apart.

Or there’s the lab’s latest acquisition, the scanning tunneling microscope, which can image individual atoms within a solid material, allowing researchers to study the structure of materials at the smallest scales.

An interdisciplinary group of researchers has been marshalling resources and expertise to create QT3 for three years, and now, the lab is opening its doors as a unique one-stop shop resource for quantum researchers and educators at the UW.

“The idea of this lab is to improve access to quantum hardware,” Parsons said. “It’s rather hard to acquire equipment like this. And there are a lot of researchers that may have good ideas that they want to test, but don’t have the resources yet for their own equipment. So we’re inviting researchers, initially from across campus, but also from other universities and from industry, to come in and test their ideas. This can be a hub for quantum experts to share their ideas and collaborate.”

The lab also boasts hardware that can demonstrate known quantum principles and techniques, making it useful for students in quantum fields. In addition to the entanglement device, Parsons’ students developed a machine that can suspend charged particles — in this case, tiny grains of pollen — in midair using electric fields. Researchers use the same technique to trap single atoms and manipulate their quantum properties, making the lab’s ion-trapping machine good practice for more complex work.

Some students even work at the lab through an undergraduate staffing program, and have helped install instrumentation, write code to power equipment and build parts for custom microscopes. The program provides yet another avenue for students to get hands-on experience with unusual machinery and techniques.��

“Quantum mechanics is inherently counterintuitive, and that makes it a powerful teaching tool,” Parsons said. “In the QT3 lab, students will encounter systems where their everyday intuition breaks down, and they must rely on careful reasoning and experimentation instead. They learn how to debug when results don’t match expectations, how to test simple cases and how to build understanding about hardware step by step.”

The cosmically cold dilution fridge remains something of a centerpiece, even as the lab fills up with specialized equipment. The extreme environment within the device strips heat, light and other stray energy away from materials, allowing researchers to observe the peculiar quantum properties that remain. One such property is superposition, or the ability of a particle like an electron to maintain multiple mutually exclusive properties at the same time. Scientists use superposition to create a powerful, tiny piece of technology: a quantum bit, or qubit.��

“Traditional computers use bits, which can only be one or zero. A qubit, on the other hand, we can make one plus zero,” Parsons said. “It’s both at the same time, and only when we measure it do we find out which one it is. We can use this unusual property to build a new class of computers that excel at tasks like communications and encryption.”

QT3 is part of a collaborative effort to solidify UW as a leader in quantum research and applications. Most of the lab hardware was funded by a congressional earmark championed by Senator Maria Cantwell’s office. Departmental funding from across the College of Engineering and the College of Arts and Sciences helped rehab the lab space. The National Science Foundation provided seed funding for the instructional lab equipment.

The UW has also spent the past decade investing heavily in faculty with quantum expertise.

“Very few places have expertise across the full quantum stack, from materials up to algorithms,” said , a UW professor of physics and founder of QT3. “The UW has quantum faculty in electrical and mechanical engineering, physics, computer science, materials science and chemistry. Our faculty work on superconducting qubits, spin defects, photons, trapped ions, neutral atoms and topological qubits. Our advantage is the breadth of our investment.”

The lab is now available to researchers and students across the UW, and private companies are encouraged to reach out about partnering. Parsons has already used the lab to teach a graduate-level class in electrical and computer engineering for students who included employees from Boeing, Microsoft and quantum computing company IonQ. The lab is hiring for a full-time manager to maintain the equipment and help users make the most of the facility.��

“Here in academia, we can improve the building blocks for applied technologies like quantum computing, and then transfer those learnings to industry for further scaling,” Parsons said.

For more information, contact Parsons at mfpars@uw.edu.

At the ����ԭ�� Harris Hydraulics Lab, an odd scene plays out. Over and over again, researchers from the UW and the (PNNL) pass a small rubber model of a marine animal through a large tank filled with flowing water and fitted with a spinning turbine. On some runs, the model bonks against the turbine blades; on others, it receives a glancing blow or sails past undisturbed. When bonks or knicks occur, a small collision sensor on one of the turbine’s blades detects the impacts and plots the interactions in a computer program.

The researchers are repeatedly simulating something that they hope will rarely happen in the wild: a collision between marine wildlife like a seabird, seal, fish or whale — or submerged debris like logs — and an underwater turbine.��

“We want to make sure we’re minimizing the chances of a collision in the first place,” said Aidan Hunt, a senior research engineer in mechanical engineering at the UW and member of the (PMEC). “But if a collision were to occur, we want to be able to detect it, and potentially avoid it, in real time. The available evidence suggests that collisions are rare, but we’re taking a ‘trust-but-verify’ approach.”

Marine energy — power harvested from tides, waves and currents — has enormous potential as a clean, renewable resource. But more information is needed about how large, commercial installations of underwater turbines or power-generating buoys could affect marine wildlife, whether through increased noise in the environment, habitat change or direct interactions with equipment.��

The marine collision experiments are part of the , a collection of projects led by PNNL to study the environmental impact of marine energy.��

The work at Harris Hydraulics follows a by PNNL and the UW Applied Physics Lab using a four-foot-tall prototype turbine installed at the entrance to Sequim Bay. In that study, researchers trained an underwater camera on the turbine for 109 days and then catalogued every instance of an animal approaching or interacting with the turbine. The camera captured more than 1,000 instances of fish, birds and seals approaching the turbine blades. There were only four collisions, and all were small fish.��

“This study was a first step, but a promising one,” said co-author , a research scientist at the UW Applied Physics Lab. “We didn’t see any endangered species in our study, and the risk of collision for seals and sea birds seemed to be quite low. We’re excited to get back out there with the camera and learn even more.”

The Sequim Bay experiment generated hours of valuable data, but that degree of intense monitoring may not be practical in large commercial installations in the future. Cheaper impact sensors, like the ones logging bath toy impacts at Harris Hydraulics, could be a solution, researchers say.�� 

The project is funded by the U.S. Department of Energy’s Hydropower & Hydrokinetics Office, through the Pacific Northwest National Laboratory’s Triton Initiative and the TEAMER program.

For more information, contact Hunt at ahunt94@uw.edu or Emma Cotter at emma.cotter@pnnl.gov.

Heart rate is an important sign of fetal health, yet few technologies exist to easily and inexpensively track fetal heart rates outside of doctors’ offices. This can create risks for pregnancies in low-resource regions where doctors are far away or inaccessible.��

A team led by ����ԭ�� researchers has created DopFone, a system that uses an off-the-shelf smartphone’s existing speaker and microphone to accurately estimate fetal heart rate. The phone mimics a Doppler ultrasound, emitting a tone and listening for the subtle variations in its echo caused by fetal heart beats. A machine learning model then estimates the heart rate. In a clinical test with 23 pregnant women, DopFone estimated heart rate with an average error of 2 beats per minute, or bpm. The accepted clinical range is within 8 bpm.��

The team Dec. 2 in the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies.��

“Eventually DopFone could let people test fetal heart rate regularly, rather than relying on the intermittent tests at a doctor’s office, or not getting tested at all,” said lead author , a UW doctoral student in the Paul G. Allen School of Computer Science & Engineering. “Patients might then send this data to doctors so that they can better judge patients’ health when they’re not in a clinic.”

Traditional Doppler ultrasounds, the clinical standard for fetal heart rate monitoring, work by sending high-frequency sound into a person’s body and tracking how the echo changes in frequency. They’re very accurate at measuring fetal heart rate but require costly equipment and a skilled technician to operate it.

To use DopFone, a user places the phone’s microphone against their abdomen for one minute. The phone emits a subaudible 18 kilohertz tone. The team chose this low frequency because — unlike a Doppler’s high frequencies, above 2,000 kilohertz —  it sits within the range smartphone microphones can record while still traveling well through tissue. As the tone is reflected through the user’s abdomen, the fetus’s heartbeat creates small shifts in the sound.��

A machine learning model then estimates the heart rate using the audio and the patient’s demographic information

The team tested DopFone in UW Medicine’s maternal-fetal medicine division on 23 pregnant patients between 19 and 39 weeks of pregnancy. On average its readings were within 2.1 bpm of the medical Doppler ultrasound. Its accuracy was slightly diminished for patients with high body mass indexes, though those readings were still within normal limits. Because an irregular fetal heartbeat is often an emergency, DopFone was not tested on patients with irregularities.��

Next, the team plans to gather more data outside a lab to better train the model. Eventually they want to deploy it as a publicly available app.

“This women’s health space is often overlooked,” Garg said. “So I want to focus on accessible alternatives that can be available to people in low resource areas, whether that’s here in the U.S. or in other countries. Because health belongs to everyone.”

Co-authors include , a UW graduate student in electrical and computer engineering; and , both OB/GYNs in UW Medicine’s  maternal-fetal medicine division; and , a UW assistant professor in the Allen School. , a UW professor in the Allen School and in electrical and computer engineering, and of the Georgia Institute of Technology, were senior authors. This research was funded by the UW Gift Fund.��

For more information, contact Garg at pgarg70@uw.edu.

On Feb. 24, astronomers’ computers around the world lit up with a deluge of cosmic notifications — 800,000 alerts about new asteroids in our solar system, exploding stars across the galaxy and other noteworthy changes in the night sky. The discoveries were made by the Simonyi Survey Telescope at the in Chile and distributed globally within about two minutes.

That flurry of notifications marked the commencement of the observatory’s Alert Production Pipeline, a sophisticated software system developed at the ����ԭ�� that is eventually expected to produce up to seven million alerts per night.

“Rubin’s alert system was designed to allow anyone to identify interesting astronomical events with enough notice to rapidly obtain time-critical follow-up observations,” said , a research associate professor of astronomy at the UW who leads the Alert Production Pipeline Group for the Rubin Observatory. “Rubin will survey the sky at an unprecedented scale and allow us to find the most rare and unusual objects in the universe. We can’t wait to see the exciting science that comes from these data.”

The beginning of scientific alerts is one of the last major milestones before Rubin Observatory launches its (LSST) later this year. During the LSST, Rubin will scan the Southern Hemisphere sky nightly for 10 years to precisely capture every visible change using . These alerts will chronicle the treasure trove of scientific discoveries that Rubin will make through its time-lapse record of the universe. In the first year of the LSST, Rubin is expected to capture images of more objects than all other optical observatories combined in human history.

The UW played a central role in the software that enabled this month’s milestone. The alert pipeline was developed by a team of about two dozen researchers and software developers in the astronomy department’s . The team has spent the past decade working with other data management teams around the country to figure out how to process the staggering 10 terabytes of images that Rubin produces every night, and will continue to develop and operate the alert system throughout the 10-year LSST survey.

“Enabling real-time discovery on such a massive data stream has required years of technical innovation in image processing algorithms, databases and data orchestration. We’re thrilled to continue the UW’s legacy of excellence in data-driven science.” Bellm said.

While the night sky seems calm and unchanging to the casual viewer, it’s actually alive with motion and transformation. Each alert signals something that has changed in the sky since Rubin last looked — a new source of light, a star that brightened or dimmed, or an object that moved. With Rubin’s alerts, scientists will have a greater ability to catch supernovae in their earliest moments, discover and track asteroids to assess potential threats to Earth and spot rare interstellar objects as they race through the solar system.

Scientists can use these data to better understand the nature of dark matter, dark energy and other unknown aspects of the universe.

“The discoveries reported in these alerts reflect the power of NSF-DOE Rubin Observatory as a tool for astrophysics and the importance of sustained federal support,” said Kathy Turner, program manager in the High Energy Physics program in the U.S. Department of Energy’s . “Rubin Observatory’s groundbreaking capabilities are revealing untold astrophysical treasures and expanding scientists’ access to the ever-changing cosmos.”

Every 40 seconds during nighttime observations, Rubin captures a new region of the sky. It then sends the data on a seconds-long journey from Chile to the U.S. Data Facility (USDF) at the in California for initial processing. Rubin’s data management system automatically compares it to a template made from previous images of the same region. This comparison allows it to detect the slightest variations. With every change, such as the appearance of a new point of light, an object’s movement or a change in brightness, the system generates a public alert within two minutes.

“The scale and speed of the alerts are unprecedented,” says Hsin-Fang Chiang, a SLAC software developer leading operations for data processing at the USDF. “After generating hundreds of thousands of test alerts in the last few months, we are now able to say, within minutes, with each image, ‘Here is everything. Go.’”

Rubin’s alerts are public, meaning anyone — from professional researchers to students and citizen scientists — can access and explore them. The speed of the alerts allows scientists using other ground- and space-based telescopes around the world to coordinate follow-up observations. This collaboration will enable fast and detailed studies of unfolding phenomena.��

Additionally, through collaborations with platforms like , Rubin will empower the global community to help classify cosmic events and contribute directly to discovery.

Rubin Observatory is jointly operated by NSF and SLAC.

For more information, contact Bellm at ecbellm@uw.edu.

This story was adapted from a press release by and .

Operations of the Vera C. Rubin Observatory are funded by the U.S. National Science Foundation and the U.S. Department of Energy’s Office of Science.