Eight 91爆料 researchers have been selected as Fulbright Scholars for 2026-2027 and will pursue studies around the world.听

Fulbright Scholars are college and university faculty, administrators and researchers, as well as artists and professionals, who build their skills and connections, gain valuable international insights and return home to share their experiences with their students and colleagues.

This year鈥檚 91爆料 cohort represents a variety of disciplines, including sciences, engineering, business, environmental sciences, electrical and computer engineering, and computer science. The scholars will conduct research across the globe, including in Australia, India, Indonesia, Western Europe, Scandinavia and East Asia.

Two-thirds of this year鈥檚 91爆料 applicants were selected as Fulbright Scholars 鈥� an astonishing acceptance rate in the prestigious and highly selective program.

鈥淲e are incredibly proud of these outstanding 91爆料 faculty and staff whose selection as Fulbright Scholars reflects the excellence, innovation and global impact of their work,鈥� said 91爆料 Vice Provost for Global Affairs Ahmad M. Ezzeddine. 鈥淭he knowledge, partnerships and cultural understanding they gain through these experiences will enrich the 91爆料 and strengthen our shared commitment to addressing global challenges through collaboration and discovery. As the Fulbright Program celebrates its 80th anniversary, we are grateful for the U.S. Department of State鈥檚 continued investment in this transformative program.鈥澨�

The Fulbright Scholar Program for academics and professionals supports more than 800 people to teach and conduct research abroad.听

This year鈥檚 91爆料 Fulbright Scholars are:

is a professor in the Department of Biology in the College of Arts & Sciences. His research focuses on how mutually beneficial interactions between species 鈥� such as how insects pollinating plants is beneficial to both 鈥� scale into networks involving multiple species, and how the structure of those networks affects ecosystems. For example, some ecological network structures, or how connections between species are arranged, make these networks more resilient to perturbations, such as droughts or climate change.

Brosi’s Fulbright Scholar award will be through Spain’s flagship public research institution, Consejo Superior de Investigaciones Cient铆ficas, at the Do帽ana Biological Station in Seville. His work there will involve synthesizing and analyzing two comprehensive long-term datasets 鈥� one from his lab and one from his Spanish host lab 鈥� to better understand global patterns in pollination networks. In particular, scientists have recorded species that appear to be 鈥渟pecialists鈥� 鈥� such as a bee species that has only been recorded visiting one plant species听 鈥� in many ecological networks, but, without long-term data, it鈥檚 difficult to disentangle whether they are really specialists or just rare. Brosi will tackle this problem in collaboration with his Fulbright host, Ignasi Bartomeus, at Do帽ana.听听

is the director of academic services in the Department of Bioengineering in the College of Engineering and 91爆料 Medicine. She supports the department鈥檚 doctoral students with a focus on improving the research experience, expanding opportunities and advancing access and collaboration.听

While a Fulbright Scholar, she will develop a framework for a new doctoral biomedical research exchange between the 91爆料 and Tampere University in Finland. Combs will work with faculty, students and staff at both universities to lead the development of a preliminary structure of a doctoral research exchange, including eligibility criteria, mentorship plans and evaluation metrics. She will also explore funding sources for the program鈥檚 ongoing sustainability and draft a memorandum of understanding for the institutions to consider.

, an assistant professor of management and organization and the Helen Moore Gerhardt Faculty Fellow in Entrepreneurship in the Foster School of Business. Her areas of expertise include entrepreneurship, organizational growth and scaling, technology and innovation strategy, and venture capital. DeSantola teaches entrepreneurship and entrepreneurial strategy to undergraduates, master鈥檚 and doctoral students. She was named a Poets & Quants top 50 undergraduate business professor in 2021.听

DeSantola will use her Fulbright award, during which she will be a visiting U.S. Scholar to University College Cork in Ireland, to study factors influencing innovation and entrepreneurship in novel food technologies. The project connects to a broader stream of DeSantola鈥檚 research exploring the emergence and evolution of new technology-based industries.

is听 an interdisciplinary scientist in the School of Marine and Environmental Affairs in the College of the Environment. Her work focuses on how coastal communities adapt to climate change and other environmental and socioeconomic stressors, particularly within fisheries and aquaculture systems. During the past 15 years, she has worked with coastal populations, including Indigenous harvesters, to support food sovereignty and long-term approaches to adaptation and resilience.

Greene鈥檚 Fulbright award is to advance the inclusion of听 fish and other aquatic foods 鈥斕� 鈥淏lue Foods鈥� 鈥� into Indonesia鈥檚 National School Lunch Program. The goal of this project is to improve nutritional outcomes for school-aged children while strengthening local food systems. Through working directly with fishers and fish suppliers, Green will work with the project team to identify the conditions necessary to provide Blue Foods that promote positive nutritional outcomes for children, support local fishers and sustain local ecosystems. This project is a pilot program for the initiative that will hopefully be expanded nationally.

is an associate professor in the Information School and also is part of the Responsibility in AI Systems and Experiences (RAISE) Center. An interdisciplinary scholar with expertise in human-centered AI, Mitra’s work draws on human-computer interaction, machine learning, natural language processing and social science to understand how people and AI interact in large-scale online systems. Her research examines the societal impacts of generative AI and develops methods to make AI systems more trustworthy, culturally aware and beneficial for diverse communities.

She will use her Fulbright award in India, where she will collaborate with researchers at the Centre for Machine Intelligence and Data Science (C-MInDS) at the Indian Institute of Technology (IIT Bombay) 鈥� the nation鈥檚 topmost and most selective public research institution. She will investigate the risks and capabilities of generative AI systems across socio-cultural contexts most relevant to the Global South. The work aims to advance more culturally aware and responsible AI while strengthening research partnerships between the United States and India.

is an associate professor in the School of Oceanography in the College of the Environment. Morris鈥� research uses cultivation and whole-genome DNA sequencing to identify the roles of bacteria in global nutrient cycles. With a focus on carbon, nitrogen and sulfur, he has conducted studies that show the effects of low dissolved oxygen on the nutrient cycling activities of the ocean’s most abundant organisms.听

During his time at in South Korea, Morris will pursue a project entitled, 鈥淗igh-throughput cultivation-based genomics of freshwater Chloroflexota.鈥� A key goal is to advance understanding of the evolution of this important group of bacteria and its potential to mediate key nutrient transformations.

is a professor in the Department of Electrical and Computer Engineering in the College of Engineering. She leads a neural engineering lab focused on motor brain-computer interfaces, or BCIs. Her work combines experiments with computational methods to develop new ways to build BCIs that interact with plasticity in the brain.

During her stay at the Champalimaud Institute Centre for Restorative Neurotechnology in Portugal, she will collaborate with two researchers, Dr. Juan 脕lvaro Gallego and Dr. John Krakauer. The new projects aim to improve our understanding of how plasticity shapes brain dynamics and apply new BCI algorithms for stroke rehabilitation.听听

is a professor in the Information School and an adjunct professor in the Paul G. Allen School of Computer Science & Engineering in the College of Engineering. He is the founding director of the InfoSeeking Lab and founding co-director of RAISE, the Center for Responsibility in AI Systems & Experiences. His research focuses on agentic AI, human-centered information seeking and responsible AI, examining how intelligent systems can act on people’s behalf while remaining transparent, trustworthy and accountable. He is also the founder and CEO of VersarAI, a startup translating his research on AI agents into enterprise applications. His book, 鈥淎gent Nation,鈥� was published this year.听

Shah will use his Fulbright Distinguished Chair in Entrepreneurship and Innovation at RMIT University in Melbourne, Australia, to study how agentic AI can responsibly power entrepreneurship and innovation ecosystems. Working with RMIT researchers and Australia’s startup community, he will investigate what he calls the Delegation Paradox: the tension between the efficiency gained by delegating tasks to AI agents and the oversight, trust and accountability that delegation demands. The work aims to produce frameworks that help founders, enterprises and policymakers adopt AI agents in ways that drive innovation without sacrificing human agency.

At the end of every presidential term, the preserves that administration鈥檚 web presence as a vast trove of documents and webpages. The archive began in 2008, with George W. Bush鈥檚 second term, and runs up to 2024, collecting images, text, graphs, redacted pages and other media. So while it contains important public information, finding that information in the glut can prove difficult.

A 91爆料-led research team created , an efficient search system for PDFs from the End of Term Web Archive. Users can look up exact keywords, like 鈥淔AFSA,鈥� or use a semantic search, which finds documents on a topic even if the exact search terms don鈥檛 appear on the page. A visual search option lets them query for qualities like 鈥渞edacted documents,鈥� “aerial photographs鈥� or 鈥減ie charts.鈥� The system can currently search the 10 million PDFs hosted online during Donald Trump鈥檚 first term; the team plans to expand it to the whole archive.听

Because researchers used highly efficient artificial intelligence models to read the documents, processing all the PDFs costs less than $1,500, or about $1 per 47,000 pages. By comparison, Google might charge consumers .听

The team will July 5 at the Annual Meeting of the Association for Computational Linguistics in San Diego.听

鈥淭he End of Term Web Archive is immensely important to historians, journalists and the American public,鈥� said senior author , a 91爆料 assistant professor in the Information School. 鈥淏ut many of these digital archives are getting so big 鈥� just announced its trillionth page archived 鈥� that finding information is the real challenge.鈥�

The team worked with PDFs because they are a ubiquitous file format and can contain text, charts and images 鈥� a mix that is challenging for existing search systems but makes the documents ideal candidates for GovScape鈥檚 multimodal search.听

鈥淛ust as library classification systems group books on similar topics on the same shelf, these embeddings group similar pages with one another based on their visual and textual content,鈥� Lee said.

Researchers then built different indexing systems for the three kinds of search. The keyword search uses a basic index 鈥� similar to a book index 鈥� for all the text. If a user types in 鈥淔AFSA,鈥� the system finds all the pages the word appears on.听

For semantic and image searches, the system takes the user鈥檚 search term and creates an embedding. It then compares this embedding with the indices created from the embeddings of PDF pages and identifies the closest matches, which are returned as search results.听

鈥淥ur next goal is to cover all of the 70 million PDFs in the entire End of Term Web Archive 鈥� everything from 2008 to 2024,鈥� Lee said. 鈥淥ne of the challenges moving forward is how to efficiently search at that scale.鈥澨�

Because government archives contain 鈥渆very file type under the sun,鈥� Lee said, future work might expand to documents such as spreadsheets, images and HTML pages.听

鈥淚’m really excited about the prospects for better access to government information with projects like GovScape,鈥� Lee said. 鈥淏eing able to actually find relevant information is vital to the health of democracy and to the functioning of society.鈥�

Co-authors include of Boston University, who completed this research as a doctoral student in the Paul G. Allen School of Computer Science & Engineering; and , who completed this research as 91爆料 master鈥檚 students in the Information School;,,, , and , all students in the Allen School; of Harvard University; of the Massachusetts Institute of Technology; of the University of North Texas; and of the American Institute of Physics.听

For more information, contact Lee at bcgl@uw.edu.

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

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

, a 91爆料 professor in the Information School and co-director of the Center for Digital Youth, studies how technology affects young people鈥檚 learning and development. Davis has also been teaching for over two decades 鈥� first as an elementary school teacher and now as a professor 鈥� so she鈥檚 acutely aware of how earlier technological revolutions in teaching have not always played out as hoped.

Davis and a 91爆料-led team of researchers interviewed 22 teachers in in Colorado 鈥� a district that鈥檚 investing heavily in AI through systems like Google鈥檚 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.

91爆料 News talked with Davis about the study and how ostensibly democratizing technologies can widen disparities in schools.听

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鈥檛 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 鈥� 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.听

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 鈥� 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 鈥� 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鈥檙e 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 鈥� I feel it in university jobs, too. Many people have this perception that using AI is cheating or taking the easy way out.听

But there’s another layer: Teachers are personally worried about their own authentic voice and professional identity. They鈥檙e asking, 鈥淚f 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?鈥澨�

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 鈥� students don’t talk about it because they’re implicitly getting the message that it’s not OK to use it, and it鈥檚 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.听

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鈥檚 brain. Students learn through dialog, through participation in cultural practices. To remove that element of learning really concerns me.

Co-authors include, a 91爆料 doctoral student in human centered design and engineering; of Artech and of Rutgers University, both of whom contributed to this research as 91爆料 graduate students in the Information School; of Columbia University; of Aurora Public Schools;, a 91爆料 associate professor in the Information School;, a 91爆料 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.

UPDATE April 7, 2026:听The original version of this story omitted two 91爆料 programs that were included in the rankings: Occupational Therapy (Tied for 20th) and Physical Therapy (Tied for 31st).听

The 91爆料鈥檚 graduate and professional degree programs again were recognized as among the best in the nation, according to .

Topping this year鈥檚 list include programs at the Evans School of Public Policy & Governance, the School of Public Health, the School of Nursing, the Paul G. Allen School of Computer Science & Engineering in the College of Engineering and the College of Education. The College of Arts & Sciences and the College of the Environment also had top-rated programs.

In total, 81 graduate and professional degree programs across the 91爆料 placed in the top 35 in this year鈥檚 U.S. News rankings.

“These rankings highlight the strength and impact of the 91爆料鈥檚 graduate and professional programs,鈥� said 91爆料 President Robert J. Jones. 鈥淭hese programs equip students with the skills and knowledge to meet critical workforce needs and serve society, while demonstrating the power of higher education to advance the public good. We are proud to foster an environment where students and faculty can thrive and have a real impact on the world around them.鈥�

While the 91爆料 celebrates the success and impact of the programs recognized by U.S. News 鈥� and notes that many applicants use these rankings to help them select schools and discover potential areas of study 鈥� the University also recognizes shortcomings inherent in the ranking systems.

The 91爆料 School of Law and the 91爆料 School of Medicine withdrew from the U.S. News rankings in 2022 and 2023, respectively, citing concerns that some of the methodology in the rankings for those specific disciplines incentivize actions and policies that run counter to the schools鈥� public service missions.

91爆料 leaders continue to work with U.S. News and other ranking organizations to improve their methodologies, to the extent that the organizations are open to it. Schools, colleges and departments continually reevaluate the benefits and potential shortfalls of participating in specific rankings.

Excluding the School of Law and the School of Medicine, 29 91爆料 programs placed in the top 10, and 81 are in the top 35.

听The 91爆料 this year placed in the top 10 nationwide in public affairs, biostatistics,听 nursing, computer science, education, psychology, speech and language pathology, statistics and Earth sciences.

The 91爆料鈥檚 Evans School of Public Policy & Governance has maintained its top-10 ranking for more than a decade and tied for fifth in the nation this year. The Evans School鈥檚 environmental policy program was ranked second, while public finance and budgeting as well as leadership both ranked No. 10.

The 91爆料 School of Nursing鈥檚 doctor of nursing practice program tied for No. 1 among public institutions. The School of Public Health has maintained its top-10 ranking for more than a decade, coming in this year at No. 9. The school also had three programs in the top 10: biostatistics, environmental health sciences and epidemiology.听

The 91爆料鈥檚 programs in speech and language pathology tied for No. 6.听 Two programs from the College of Education placed in the top 10. And the Paul G. Allen School of Computer Science & Engineering this year tied for seventh place overall with three programs ranked in the top 10, including artificial intelligence, programming language and systems.

U.S. News ranks biostatistics in two ways. 91爆料 ranked No. 3 as a science discipline that applies statistical theory and mathematical principles to research in medicine, biology, environmental science, public health and related fields. 91爆料鈥檚 School of Public Health ranked No. 7 in biostatistics as an area of study that trains students to apply statistical principles and methods to problems in health sciences, medicine and biology. At the 91爆料, biostatistics is a division of the School of Public Health.

In some cases, such as the College of Arts & Science and the Foster School of Business, U.S. News ranks several professional disciplines housed within academic units. Programs in dentistry are not ranked.听

The rankings below are based on preliminary data and may be updated. relies on both expert opinions and statistical indicators.

TOP 10:

Library and Information Studies (overall): Two-way tie for 1st (ranked in 2025)

Public Affairs (environmental policy): 2nd

Library and information studies (digital librarianship): Two-way for 2nd (ranked in 2022)

Library and Information Studies (information systems): 2nd (ranked in 2022)

Biostatistics: 3rd

Physics (nuclear): Two-way tie for 3rd (ranked in 2024)

Nurse practitioner (doctor of nursing practice): Four-way tie for 4th

Evans School of Public Policy & Governance (overall): Four-way tie for 5th

Library and Information Studies (library services for children and youth): Two-way for 5th (ranked in 2022)

Computer science (systems): Tied for 6th

Education (elementary education): 6th

Psychology (clinical): Three-way tie for 6th

Speech-language pathology: Five-way tie for 6th

Statistics: Four-way tie for 6th

Public Health (biostatistics): 7th

Computer science (overall): Three-way tie for 7th

Computer science (programming language): Tied for 7th

Education (secondary education): 7th

Nursing (midwifery): Five-way tie for 7th

Public Health (environmental health sciences): 7th

School of Social Work (overall): 7th (ranked in 2025)

Public Health (epidemiology): 8th

Computer science (artificial intelligence): 9th

Earth sciences: Tied for 9th听

Geophysics: Three-way tie for 9th (ranked in 2024)

Public Affairs (nonprofit management): 9th

School of Public Health (overall): Tied for 9th

Public Affairs (public finance and budgeting): 10th

Public Affairs (public management and leadership): 10th

TOP 25:

Biological sciences: Five-way tie for 16th

Business (accounting): 10-way tie for 16th

Business (entrepreneurship): Five-way tie for 17th

Business (information systems): Three-way tie for 15th

Business (part-time MBA): Three-way tie for 11th

Business (full-time MBA): 20th

Business (management): Five-way tie for 25th

Business (marketing): Eight-way tie for 25th

Chemistry (analytical): Four-way tie for 16th (ranked in 2024)

Chemistry: Seven-way tie for 22nd

Chemistry (inorganic): Three-way tie for 22nd (ranked in 2024)

Computer science (theory): Tied for 11th

College of Education (overall): Tied for 24th

Education (administration): Tied for 11th

Education (curriculum/instruction): Tied for 12th

Education (policy): Tied for 14th

Education (special education): Tied for 12th

College of Engineering (overall): Three-way tie for 22nd

Engineering (aerospace/aeronautical/astronautical): Tied for 17th

Engineering (biomedical/bioengineering): Five-way tie for 12th

Engineering (civil): Four-way tie for 13th

Engineering (computer): 12th

Engineering (electrical): Three-way tie for 22nd

Engineering (industrial/manufacturing/systems): Seven-way tie for 24th

Engineering (materials engineering): Five-way tie for 25th

Library and Information Studies (school library media): Two-way tie for 11th (ranked in 2022)

Mathematics (applied math): 21st (ranked in 2024)

Nursing master鈥檚 (overall): Tied for 12th

Nurse practitioner (adult gerontology acute care): Tied for 11th

Nurse practitioner (family): Tied for 15th

School of Pharmacy (overall): Tied for 14th

Physics (overall): Tied for 20th听

Public Affairs (public policy analysis): 14th

Public Affairs (social policy): Tied for 13th

Public Affairs (urban policy): Three-way tie for 21st

Public Health (health care management): Three-way tie for 16th听

Public Health (health policy and management): 11th

Public Health (social behavior): 13th

Sociology (overall): Two-way tie for 22nd (ranked in 2025)

Sociology (population): Two-way tie for 15th (ranked in 2022)

TOP 35:

Business (analytics): Seven-way tie for 32nd

Business (executive MBA): Three-way tie for 29th

Business (finance): Nine-way tie for 31st

Business (international MBA): Tie for 32nd

Business (production & operations): Five-way tie for 27th

Engineering (chemical): Tied for 28th

Engineering (mechanical): 34th

English: Two-way tie for 34th (ranked in 2025)

Fine arts: 15-way tie for 34th

History: Three-way tie for 31st (ranked in 2025)

Mathematics: Four-way tie for 26th

Occupational Therapy: Tied for 20th

Physical Therapy: Tied for 31st

Political science: Five-way tie for 33rd (ranked in 2025)

Since Ryan Calo joined 91爆料 School of Law in 2012, he has become a leading expert on the law and emerging technology.听听

Calo believes that few interesting questions 鈥� especially around technology 鈥� can be resolved by reference to a single discipline.听

Calo is a co-founder of the , and the . He is also a professor in the and an adjunct in the .听

Calo鈥檚 newest book, 鈥�,鈥� published late last year, is a guide to a legal analysis of regulation and technology. Nearly a decade ago, Calo realized that the most recent book on the topic was published in the 1970s. He decided it was time for an updated resource reflecting current, rapidly evolving technology and the present regulatory environment.听

91爆料 News spoke with Calo about the book and the current legal and policy climate in the United States.

Who is the intended audience for 鈥淟aw and Technology鈥�?

Ryan Calo: I wrote it primarily for new entrants to the field, be they junior scholars or students. I also hoped that the themes would resonate with more senior scholars and that it would be useful outside of academia for either analysis or instruction. Because ultimately, what the book does is proposes a methodology for analyzing technology from a legal perspective.听

I spent a lot of time interacting with policymakers, staffers on Capitol Hill, people who work for senators and members of Congress. A legislator might come to a staffer and say,听 鈥淗ey, my constituents are really worried about augmented reality or AI. They’re really worried about deep fakes.鈥� That staff member doesn’t really have a place to start, and they end up just calling up experts, reading New York Times articles, talking to industry, but not in any kind of methodical way. This book is designed to help them figure out what’s going on.听

I also hope that this book would be of use to people who are in practice and want to be more methodical about analyzing a given technology.听

Technology evolves fast. How should the legal system and policymakers prepare to navigate the relationship between law and emerging technologies?

RC: Many of us have an expectation that technology is just going to change. It’s just going to evolve, and our job as lawyers or judges or policymakers, is to kind of scramble and accommodate the resulting disruption, and perhaps try to restore the status quo. Part of what I hope to see is legal scholars and policymakers acknowledging that the disruption isn鈥檛 inevitable.

We need to empower independent researchers to figure out what’s going on with new technology. Right now researchers are disempowered because they don’t have access to the relevant data and platforms. And many times when they try to get that data, they get served with a cease and desist letter.听

We need to protect whistleblowers and make sure there’s adequate, truly top-notch expertise within government. If you have those things, then you’re much more likely to be able to figure out what could go wrong with these technologies without having to observe the harm unfold over a long period of time, as we have with the internet and now with AI.

You mentioned the School of Law鈥檚 leadership in tech policy. How is the 91爆料 positioned nationally in this space?

RC: We are really among the leaders in this area.听

The School of Law has a lot of tech policy offerings, including a . Many faculty have contributed to scholarship over the years. We have lots of faculty writing about law and technology.听

We also have been really a model for impactful interdisciplinary collaboration. Law students can work in the clinic or the Tech Policy Lab. I’m one of the founders of the Center for an Informed Public, which bridges human centered and design engineering as well as the Information School and dozens of other departments including psychology, education and even geography.听

A third important example is the . We did a whole year of work mapping out who was doing work in the space 鈥� all the centers, all the labs, all the initiatives 鈥� all the people on the three campuses identified as working at this intersection.听

We’re leaders across the country at the law school in terms of our student offerings in our research, but we are also part of that interstitial glue. People think of the iSchool, which they should. They think of computer science, which they should. But they also should think about who else is in the center of this, who else is at the heart of it, and the School of Law is a big part of that.

There鈥檚 been a lot of news lately about states trying to regulate AI and the federal government pushing back. What鈥檚 your perspective?

RC: If I were trying to sabotage the innovation edge of the United States, I would do at least two things, maybe three.听

First, I would divest in basic research. The United States has had an innovation edge over the rest of the world in large part because of decisions made in the 1950s and beyond to invest in basic research. I would dismantle that, and I would try to make it really hard for universities to do research, either by spending less, disrupting the relationships, or messing with overhead in ways that makes research impossible.听

The second thing I would do is make it really hostile for outside innovators to come in and participate in knowledge production here. I would, whether xenophobically or not, try to make it really hard for people with ideas and talent and knowledge to come here to the United States to work on teams with other Americans, to stay here and teach in our schools, to found companies. The second enormous advantage the United States has had is that the country has become attractive because of its commitment to the rule of law and its robust higher ed system, and that鈥檚 built on its innovation and investment in research. People from all over the world come here to try and make the next Google and Amazon, or are teaching in our schools and contributing to our ecosystem.听

The third thing I would do in this hypothetical situation is remove non-existent hurdles to transformative technologies like AI. What do I mean? Federal leaders are currently talking about getting out of the way of AI, but there aren’t any regulations about AI, really. There are some state laws that have a kind of European flavor of risk management, like and . There are specific things that states are worried about, including deep fakes and labeling online social media accounts that are automated. There’s almost nothing standing in the way of AI innovation in terms of regulation.听

The way that our system is structured is that the individual states, under our concept of federalism, are supposed to be laboratories of ideas, experimenting with legislation, and showing that it works or it doesn’t. Pretending that you’re pro-innovation because you’re trying to stamp out the very few regulatory hurdles that companies have to have to abide by all in the name of competing with China, which has AI laws, is just senseless. We’re much better off following the wisdom of the founders, who said, 鈥淗ey, if you have something new in society, let the states serve as laboratories for different laws, and we can all learn from each other about how that’s going.鈥� That’s classic federalism and it used to be a pillar of conservative thinking.听

The President doesn’t have the power to boss the states around in terms of their legislative capacities. And Congress has taken up the question of whether to try to preempt AI laws, and they resignedly declined. I just want to comment that the overall strategy of the administration has been deeply anti-innovation in its impact, even though it is vociferously proinnovation in its rhetoric.

Any final thoughts?

RC: We have an environment in the U.S. that promotes innovation, sometimes through laws, such as laws that protect intellectual property, and laws that make people feel safe enough to use products and services that companies can sell them to us. There鈥檚 not, and never has been, a one-to-one correlation between regulation and promoting innovation. It’s really important that we acknowledge, as a society and community, that sometimes laws are written in the service of innovation. What you want is a favorable regulatory environment, not a complete absence of the rule of law.

For more information, contact Calo at rcalo@uw.edu.听

The 91爆料 is the best in the U.S. and No. 2 in the world for library and information management, according to the 2026 released Wednesday. Three other 91爆料 subject areas placed in the top 10 in the world: geology, geophysics and Earth and marine sciences.

This ranking tracks an analysis of reputation and research output, conducted by . The consultancy looks at more than 18,300 individual university programs at more than 1,700 universities in 100 locations around the world. The ranking spans 55 academic disciplines across five broad faculty areas including arts and humanities; engineering and technology; life sciences and medicine; natural sciences; and social sciences and management.

The 91爆料 has 29 programs in the top 100, 14 in the top 50, and four in the top 10, including:

• Library and information management 鈥� No. 2
• Geology 鈥� No. 8
• Geophysics 鈥� No. 9
• Earth and marine sciences 鈥� No. 10

Visit the rankings site for .

Seattle Public Library, or SPL, is the only U.S. library system that makes its anonymized, granular checkout data public. Want to find out how many times people borrowed the e-book version of Toni Morrison鈥檚 鈥淏eloved鈥� in May 2018? That data is available.听

The hitch is that the library鈥檚 data set contains nearly 50 million rows, and a single title can appear variously. Morrison鈥檚 鈥淏eloved,鈥� for instance, is listed as 鈥淏eloved,鈥� 鈥淏eloved (unabridged),鈥� 鈥淏eloved : a novel / by Toni Morrison鈥� and so on.听

To track trends in the catalogue over the last 20 years, 91爆料 researchers analyzed the checkout data of the 93 authors included in the post-1945 volume of 鈥淭he Norton Anthology of American Literature.鈥� It鈥檚 assigned in U.S. English classes more than virtually any other anthology, so what鈥檚 thought of as the contemporary American 鈥� the books and writers we鈥檝e deemed culturally important.听

The team found that among these vaunted writers 鈥� including Morrison, Viet Thanh Nguyen, David Foster Wallace and Joan Didion 鈥� science fiction was particularly popular. Ursula K. Le Guin and Octavia E. Butler topped the list.听

The team Nov. 21 in Computational Humanities Research 2025, and created .听

鈥淚t鈥檚 kind of mind-boggling and ironic that in this age of abundant data, we have so little data about what people are reading,鈥� said senior author , a 91爆料 assistant professor in the Information School. 鈥�, particularly for researchers, so I鈥檝e been obsessed with SPL鈥檚 data for years now. But extracting insights from it is actually a really hard computational and bibliographic modeling problem.鈥�

To organize the data, the team used computational methods, such as stripping away subtitles and standardizing punctuation. They also manually identified things like translations of a work.听

鈥淲e worked with the Norton anthology in part because it’s a small enough scale for us to handle,鈥� said lead author , a 91爆料 doctoral student in the Information School. 鈥淚t allows us to have a ground truth to work off of. We can still put a human eye on things.鈥澨�

In all the team looked at 1,603 works by the 93 authors, which were checked out a total of 980,620 times since 2005.

The 10 top authors were:

1. Ursula K. Le Guin
2. Octavia E. Butler
3. Louise Erdrich
4. N.K. Jemisin
5. Toni Morrison
6. Kurt Vonnegut
7. George Saunders
8. Philip K. Dick
9. Sherman Alexie
10. James Baldwin

The 10 top books were:听

1. 鈥淧arable of the Sower鈥� by Octavia E. Butler
2. 鈥淟incoln in the Bardo鈥� by George Saunders
3. 鈥淭he Fifth Season鈥� by N.K. Jemisin
4. 鈥淭he Sympathizer鈥� by Viet Thanh Nguyen
5. 鈥淜indred鈥� by Octavia E. Butler
6. 鈥淏eloved鈥� by Toni Morrison
7. 鈥淭he Left Hand of Darkness鈥� by Ursula K. Le Guin
8. 鈥淭he Absolutely True Diary of a Part-Time Indian鈥� by Sherman Alexie
9. 鈥淭he Year of Magical Thinking鈥� by Joan Didion
10. 鈥淭he Sentence鈥� by Louise Erdrich

Researchers noted several trends that may have driven checkouts. In general, books with genre and sci-fi elements were some of the most popular.听

鈥淚 found the prevalence of sci-fi books and writers really interesting,鈥� Gupta said. 鈥淭hese are recent additions to the anthology, since sci-fi and genre fiction haven鈥檛 always been seen as important literature. So while it鈥檚 a bit unsurprising, it鈥檚 also striking to see that despite comprising a small portion of the anthology, these are the authors people are actually reading the most.鈥�

News events also drove spikes in readership, such as film adaptations of James Baldwin鈥檚 鈥淚f Beale Street Could Talk鈥� and Don DeLillo鈥檚 鈥淲hite Noise,鈥� or the deaths of authors such as Didion, Wallace, Morrison and Philip Roth.听

The top book, 鈥淧arable of the Sower,鈥� saw a huge spike in readership in 2024 鈥� the year the futuristic novel is set, and the year SPL selected the novel for its program.听

鈥淲e鈥檝e deemed these canonical authors important enough to continue reading, to continue teaching, to continue studying and talking about, so it鈥檚 fascinating to see who we鈥檙e actually reading and when,鈥� Walsh said. 鈥淚 find it very beautiful that after years of these big debates about diversifying the canon, the works that people are turning to the most are by women and Black and Native writers, who previously were not even included in these anthologies.鈥�

Co-authors include Daniella Maor, Karalee Harris, Emily Backstrom and Hongyuan Dong, all students at the 91爆料. This research was supported in part by the .

For more information, contact Walsh at melwalsh@uw.edu and Gupta at ngupta1@uw.edu.

Once the domain of buttons and knobs, car dashboards are increasingly home to large touch screens. While that makes following a mapping app easier, it also means drivers can鈥檛 feel their way to a control; they have to look. But how does that visual component affect driving?

New research from the 91爆料 and Toyota Research Institute, or TRI, explores how drivers balance driving and using touch screens while distracted. In the study, participants drove in a vehicle simulator, interacted with a touch screen and completed memory tests that mimic the mental effort demanded by traffic conditions and other distractions. The team found that when people multitasked, their driving and touch screen use both suffered. The car drifted more in the lane while people used touch screens, and their speed and accuracy with the screen declined when driving. The effects increased further when they added the memory task.听

These results could help auto manufacturers design safer, more responsive touch screens and in-car interfaces.

The team Sept. 30 at the ACM Symposium on User Interface Software and Technology in Busan, Korea.听

鈥淲e all know ,鈥� said co-senior author , a 91爆料 professor in the Paul G. Allen School of Computer Science & Engineering. 鈥淏ut what about the car鈥檚 touch screen? We wanted to understand that interaction so we can design interfaces specifically for drivers.鈥�

As the study鈥檚 16 participants drove the simulator, sensors tracked their gaze, finger movements, pupil diameter and electrodermal activity. The last two are common ways to measure mental effort, or 鈥渃ognitive load.鈥� For instance, pupils tend to grow when people are concentrating.听

While driving, participants had to touch specific targets on a 12-inch touch screen, similar to how they would interact with apps and widgets. They did this while completing three levels of an 鈥淣-back task,鈥� a memory test in which the participants hear a series of numbers, 2.5 seconds apart, and have to repeat specific digits.听

The participants鈥� performance changed significantly under different conditions:

• When interacting with the touch screen, participants drifted side to side in their lane 42% more often. Increasing cognitive load had no effect on the results.
• Touch screen accuracy and speed decreased 58% when driving, then another 17% under high cognitive load.
• Each glance at the touchscreen was 26.3% shorter under high cognitive load.
• A 鈥渉and-before-eye鈥� phenomenon, in which drivers鈥� reached for a control before looking at it, increased from 63% to 71% as memory tasks were introduced.

The team also found that increasing the size of the target areas participants were trying to touch did not improve their performance.听

鈥淚f people struggle with accuracy on a screen, usually you want to make bigger buttons,鈥� said , a 91爆料 doctoral student in the Allen School. 鈥淏ut in this case, since people move their hand to the screen before touching, the thing that takes time is the visual search.鈥�

Based on these findings, the researchers suggest future in-car touch screen systems might use simple sensors in the car 鈥� eye tracking, or touch sensors on the steering wheel 鈥� to monitor drivers鈥� attention and cognitive load. Based on these readings, the car鈥檚 system might adjust the touch screen鈥檚 interface to make important controls more prominent and safer to access.

鈥淭ouch screens are widespread today in automobile dashboards, so it is vital to understand how interacting with touch screens affects drivers and driving,鈥� said co-senior author , a 91爆料 professor in the Information School. 鈥淥ur research is some of the first that scientifically examines this issue, suggesting ways for making these interfaces safer and more effective.鈥�

, a 91爆料 doctoral student in the Information School, is co-lead author. Other co-authors include , , and of TRI. This research was funded in part by TRI.

For more information, contact Wobbrock at wobbrock@uw.edu and Fogarty at jfogarty@cs.washington.edu.

A new tool shows it is possible to turn down the partisan rancor in an X feed 鈥� without removing political posts and without the direct cooperation of the platform.听

The study, from researchers at the 91爆料, Stanford University and Northeastern University, also indicates that it may one day be possible to let users take control of their social media algorithms.听

The researchers created a seamless, web-based tool that reorders content to move posts lower in a user鈥檚 feed when they contain antidemocratic attitudes and partisan animosity, such as advocating for violence or jailing supporters of the opposing party.听

Researchers Nov. 27 in Science.

鈥淪ocial media algorithms direct our attention and influence our moods and attitudes, but until now, only platforms had the power to change their algorithms鈥� design and study their effects,鈥� said co-lead author , a 91爆料 assistant professor in the Information School. 鈥淥ur tool gives that ability to external researchers.鈥�

In an experiment, about 1,200 volunteer participants used the tool over 10 days during the 2024 election. Participants who had anti-democratic content downranked showed more positive views of the opposing party. The effect was also bipartisan, holding true for people who identified as liberals or conservatives.

鈥淧revious studies intervened at the level of the users or platform features 鈥� demoting content from users with similar political views, or switching to a chronological feed, for example. But we built on recent advances in AI to develop a more nuanced intervention that reranks content that is likely to polarize,鈥� Saveski said.

For this study, the team drew from previous sociology research identifying categories of antidemocratic attitudes and partisan animosity that can be threats to democracy. In addition to advocating for extreme measures against the opposing party, these attitudes include statements that show rejection of any bipartisan cooperation, skepticism of facts that favor the other party鈥檚 views, and a willingness to forgo democratic principles to help the favored party.

The researchers tackled the problem from a range of disciplines including information science, computer science, psychology and communication.听

The team created a web extension tool coupled with an artificial intelligence large language model that scans posts for these types of antidemocratic and extreme negative partisan sentiments. The tool then reorders posts on the user鈥檚 X feed in a matter of seconds.听

Then, in separate experiments, the researchers had a group of participants view their feeds with this type of content downranked or upranked over seven days and compared their reactions to a control group. No posts were removed, but the more incendiary political posts appeared lower or higher in their content streams.

The impact on polarization was clear.听

鈥淲hen the participants were exposed to less of this content, they felt warmer toward the people of the opposing party,鈥� said co-lead author , an assistant professor at Johns Hopkins University. 鈥淲hen they were exposed to more, they felt colder.鈥澨�

Before and after the experiment, the researchers surveyed participants on their feelings toward the opposing party on a scale of 1 to 100. The attitudes among the participants who had the negative content downranked improved on average by two points 鈥� equivalent to the estimated change in attitudes that has occurred among the general U.S. population over a period of three years.听

The researchers are now looking into other interventions using a similar method, including ones that aim to improve mental health. The team has also made the code of the current tool available, so other researchers and developers can use it to create their own ranking systems independent of a social media platform鈥檚 algorithm.

鈥淚n this work, we focused on affective polarization, but our framework can be applied to improve other outcomes, including well-being, mental health and civic engagement,鈥� Saveski said. 鈥淲e hope that other researchers will use our tool to explore the vast design space of potential feed algorithms and articulate alternative visions of how social media platforms could operate.鈥�

Additional co-authors on this study include of Northeastern and , and of Stanford.

This work was supported in part by the National Science Foundation, the Swiss National Science Foundation and a Hoffman-Yee grant from the Stanford Institute for Human-Centered Artificial Intelligence.听

For more information, contact Saveski at msaveski@uw.edu.

This story was adapted from a by Stanford University.

An organization drafts a job listing with artificial intelligence. Droves of with chatbots. Another AI system sifts through those applications, passing recommendations to hiring managers. Perhaps AI avatars conduct screening interviews. This is increasingly the state of hiring, as people seek to streamline the stressful, tedious process with AI.

Yet research is finding that hiring bias 鈥� against people with disabilities, or certain races and genders 鈥� permeates large language models, or LLMs, such as ChatGPT and Gemini. We know less, though, about how biased LLM recommendations influence the people making hiring decisions.听

In a new 91爆料 study, 528 people worked with simulated LLMs to pick candidates for 16 different jobs, from computer systems analyst to nurse practitioner to housekeeper. The researchers simulated different levels of racial biases in LLM recommendations for resumes from equally qualified white, Black, Hispanic and Asian men.听

When picking candidates without AI or with neutral AI, participants picked white and non-white applicants at equal rates. But when they worked with a moderately biased AI, if the AI preferred non-white candidates, participants did too. If it preferred white candidates, participants did too. In cases of severe bias, people made only slightly less biased decisions than the recommendations.

The team Oct. 22 at the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society in Madrid.听

鈥淚n one survey, 80% of organizations using AI hiring tools said they don鈥檛 reject applicants without human review,鈥� said lead author , a 91爆料 doctoral student in the Information School. 鈥淪o this human-AI interaction is the dominant model right now. Our goal was to take a critical look at this model and see how human reviewers鈥� decisions are being affected. Our findings were stark: Unless bias is obvious, people were perfectly willing to accept the AI鈥檚 biases.鈥�

The team recruited 528 online participants from the U.S. through surveying platform , who were then asked to screen job applicants. They were given a job description and the names and resumes of five candidates: two white men and two men who were either Asian, Black or Hispanic. These four were equally qualified. To obscure the purpose of the study, the final candidate was of a race not being compared and lacked qualifications for the job. Candidates鈥� names implied their races 鈥� for example, Gary O鈥橞rien for a white candidate. Affinity groups, such as Asian Student Union Treasurer, also signaled race.

In four trials, the participants picked three of the five candidates to interview. In the first trial, the AI provided no recommendation. In the next trials, the AI recommendations were neutral (one candidate of each race), severely biased (candidates from only one race), or moderately biased, meaning candidates were recommended at rates similar to rates of bias in real AI models. The team derived rates of moderate bias using the same methods as in their 2024 study that looked at bias in three common AI systems.听

Rather than having participants interact directly with the AI system, the team simulated the AI interactions so they could hew to rates of bias from their large-scale study. Researchers also used AI generated resumes, rather than real resumes, which they validated. This allowed greater control, and AI-written resumes are increasingly common in hiring.

鈥淕etting access to real-world hiring data is almost impossible, given the sensitivity and privacy concerns,鈥� said senior author , a 91爆料 associate professor in the Information School. 鈥淏ut this lab experiment allowed us to carefully control the study and learn new things about bias in human-AI interaction.鈥�

Without suggestions, participants鈥� choices exhibited little bias. But when provided with recommendations, participants mirrored the AI. In the case of severe bias, choices followed the AI picks around 90% of the time, rather than nearly all the time, indicating that even if people are able to recognize AI bias, that awareness isn鈥檛 strong enough to negate it.

鈥淭here is a bright side here,鈥� Wilson said. 鈥淚f we can tune these models appropriately, then it’s more likely that people are going to make unbiased decisions themselves. Our work highlights a few possible paths forward.鈥�

In the study, bias dropped 13% when participants began with an , intended to detect subconscious bias. So companies including such tests in hiring trainings may mitigate biases. Educating people about AI can also improve awareness of its limitations.

鈥淧eople have agency, and that has huge impact and consequences, and we shouldn’t lose our critical thinking abilities when interacting with AI,鈥� Caliskan said. 鈥淏ut I don鈥檛 want to place all the responsibility on people using AI. The scientists building these systems know the risks and need to work to reduce systems鈥� biases. And we need policy, obviously, so that models can be aligned with societal and organizational values.鈥�

, a 91爆料 doctoral student in the Information School, and , a postdoctoral scholar at Indiana University, are also co-authors on this paper. This research was funded by The U.S. National Institute of Standards and Technology.

For more information, contact Wilson at kywi@uw.edu and Caliskan at aylin@uw.edu.