The research, led by physics professor Elizabeth Goldschmidt and published in Nano Letters, describes an integrated nanophotonic platform built on thin-film lithium niobate, one of the leading materials for modern quantum optics experiments. The device dopes this waveguide with erbium atoms and uses a technique called spectral hole burning to arrange those atoms into an atomic frequency comb, a carefully spaced pattern of absorption frequencies that can catch incoming photons and re-emit them after a predetermined delay. According to the team, the device preserved quantum information with high fidelity, stored multiple photons simultaneously, and exceeded storage times of one microsecond, all on a chip that could in principle be manufactured commercially at scale.

To appreciate why a microsecond matters, it helps to consider the scale involved. The storage region on such a chip is roughly a centimeter across, a distance that light crosses in a few tens of trillionths of a second. Holding a photon for a full microsecond therefore means delaying it by a factor of tens of thousands relative to its natural transit time, effectively compressing meters of optical delay line into a device small enough to fit on a fingertip. For quantum chips, where operations must be synchronized and photons must wait their turn in a queue of quantum logic steps, that kind of on-demand delay is not a luxury. It is a prerequisite for building processors and networks that actually work.

There are, broadly speaking, two established routes to delaying light, and each has failed the quantum community in a different way. The first is the brute-force approach of conventional photonics: send the light down a longer path, using coiled optical fibers or arrangements of mirrors to stretch its journey. The trouble is that photons are easily absorbed by matter, and every additional meter of travel increases the odds that a photon simply vanishes. For the long delays relevant to quantum information processing, the survival probability of a photon in a conventional delay line becomes vanishingly small, making the approach hopelessly inefficient. A quantum memory cannot afford to lose the very qubits it is supposed to protect.

The second route is more elegant in principle. Instead of making light travel farther, one can couple photons to atoms whose quantum states are highly coherent, allowing the photonic information to be transferred into an atomic excitation, stored there, and later retrieved. Atomic ensembles can hold quantum states for far longer than a photon can survive in a fiber. But integrating such coherent atoms with scalable nanophotonic platforms has remained largely out of reach, because the exotic materials and cryogenic laboratory conditions that make long atomic coherence possible rarely coexist with the fabrication demands of chip manufacturing. This gap between laboratory demonstrations and manufacturable technology has been one of the defining bottlenecks of the field.

The Illinois team’s platform attempts to close that gap by combining two ingredients that had previously lived in separate worlds. Thin-film lithium niobate offers strong optical confinement, low loss, and wafer-scale integration, which is why it has become a workhorse material for both classical and quantum photonic chips. Erbium atoms, meanwhile, are prized in quantum memory research because their electronic transitions sit in the telecommunications band and can exhibit long coherence times, particularly when embedded in suitable crystalline hosts. By doping erbium into the lithium niobate waveguide itself, the researchers created a structure in which the light and the atoms that store it share the same nanoscale volume, maximizing their interaction without requiring the photon to leave the chip.

The storage mechanism relies on spectral hole burning, a technique in which a tunable laser is used to selectively excite and shelve subsets of atoms according to the precise frequencies they absorb. Rather than letting the erbium ensemble absorb light in a broad, featureless band, the researchers sculpted its absorption spectrum into a comb-like pattern with evenly spaced frequency peaks. When a photon enters the device, its energy is coherently shared among the comb’s teeth, and the resulting quantum interference causes the excitation to rephase after a fixed interval, at which point the photon is re-emitted in its original form. The storage time is set by the spacing of the comb, giving the researchers a knob with which to control how long the light waits.

Priyash Barya, an electrical engineering graduate student and co-first author of the paper, emphasized that no one else has stored light on a chip in a platform with this potential for scalability, describing the approach as the only way of achieving this result on one of the leading platforms for quantum optics and quantum information systems. Daren Chen, a physics graduate student and the paper’s other co-first author, framed the achievement in terms of the field’s central difficulty, noting that long delays remain an outstanding problem in quantum information processing and that the team wanted to demonstrate a promising approach using their nanophotonic platform. The demonstration of storage exceeding one microsecond, with multiple photons held at once, positions the device among the most capable integrated quantum memories reported to date.

What may ultimately matter as much as the performance numbers is the device’s manufacturability. Goldschmidt stressed that this is not a one-off bespoke device, arguing that its promise lies in its simplicity: the team has taken something that could typically only be done in a highly specialized laboratory environment and recast it in a platform that can be made commercially at scale by people who know nothing about quantum optics. That framing points toward a future in which quantum memories are not hand-built instruments but standard components, fabricated in the same facilities that produce telecommunications hardware and integrated into larger quantum systems by engineers rather than specialists.

Challenges remain before the platform can anchor a practical quantum computer or network. Goldschmidt noted that a useful version of the device will need better retrieval efficiency and longer storage times, goals the group plans to pursue by refining the atomic frequency comb and experimenting with erbium isotopes that are more resistant to noise. Her lab is also exploring additional applications for spectrally tailoring atomic ensembles to build other devices for quantum photonics, treating the memory as one component in a broader program of building capabilities on the integrated lithium niobate platform. If those efforts succeed, the humble act of making a photon wait could become one of the quiet foundations of the quantum internet, a piece of engineering so routine that the extraordinary physics inside it is all but invisible.

Subject of Research: On-chip quantum memory for photon storage in erbium-doped thin-film lithium niobate

Article Title: Illinois researchers give quantum light a place to wait

Article References: Illinois researchers give quantum light a place to wait. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: quantum memory, photon storage, thin-film lithium niobate, erbium doping, atomic frequency comb, spectral hole burning, nanophotonics, quantum information, integrated photonics, quantum networks, telecom photons, University of Illinois

The book, titled “AI Through the Experts’ Eyes: Communicating Complex Ideas” and published by the University of Pittsburgh Press, examines the daily work of AI and machine learning researchers and the communication practices that shape public understanding of the field. Gallagher, who is affiliated with the university’s School of Information Sciences and is teaching two courses on artificial intelligence this semester, set out to help experts communicate about their work without hype, and to help nonexperts, particularly writing teachers and educators wrestling with the role of AI in their classrooms, think more critically about the technology. The project began as an effort to deepen his own understanding of machine learning and natural language processing techniques for his research, but when the COVID-19 pandemic derailed his plans to visit AI research centers in person, he pivoted to a different methodology: interviewing the researchers directly about their work and how they describe it.

That pivot produced an unusually rich dataset. Gallagher interviewed more than 100 AI and machine learning experts, most of them based in academia and drawn primarily from the disciplines of computer science and physics. What he heard from them stands in stark contrast to the picture of artificial intelligence that dominates headlines and popular culture. The public, he notes, may envision killer robots when they think about AI, but the reality is mostly mundane: the GPS software that guides a vehicle through traffic, the spam filter that quietly sorts an email inbox, the recommendation systems that shape what people see online. These are the actual products of the field, and creating them is not a matter of conjuring intelligence from silicon but of methodical, repetitive engineering work: designing a model, obtaining feedback on its performance, and revising it over and over again.

“It’s boring. It’s math, a lot of math, and meetings and day-to-day normalcy,” Gallagher said, describing what the researchers told him about their working lives. The gap between that reality and the public image is not accidental. Gallagher observed that the leaders of AI companies tend to talk about “fanciful answers and magic in the sky, not linear algebra,” a rhetorical choice that serves marketing purposes but leaves the public with a distorted sense of what the technology is and how it works. Linear algebra, the branch of mathematics concerned with vectors and matrices, underlies nearly every modern machine learning system, from the neural networks that classify images to the large language models that generate text. Understanding that foundation, Gallagher argues, is the first step toward demystifying the field.

One of the most technically substantive concepts the book explores is what researchers call the alignment problem, and Gallagher is candid about why it concerns him more than the cinematic scenarios of Terminator or HAL 9000. “I’m less concerned with Terminator and HAL 9000 and more concerned with the systematic error that didn’t get picked up as a bug in the code, called the alignment problem,” he said. “The program still works but it’s not exactly what you wanted it to do, and it’s now doing something kind of bad.” In other words, the danger is not a malevolent machine but a well-functioning one that has been optimized toward the wrong objective, producing systematic errors that no single line of faulty code reveals.

To make the alignment problem concrete, Gallagher turns in the book to a real example: a garbage-detection program called TrashCan, designed to help clean up the ocean garbage patches that accumulate plastic waste across vast stretches of open water. The challenge of such a system illustrates how difficult it is to teach a machine what counts as trash. If the model is trained on objects that are not meant to be in the water, it can learn to identify and collect garbage. But edge cases multiply quickly. “Maybe you have a buoy that’s supposed to be there and is not trash, until it sinks onto the ocean bottom and it is trash,” Gallagher explained. “That’s an alignment problem if it collects buoys that are supposed to be there.” Training data introduces further complications: if a model is trained on sea life, what happens when it encounters land animals that have ended up in the water? It might classify them as trash and collect them, with potentially harmful consequences. “The problem is not an evil robot,” he said. “It’s bad programming leading to an unintended result.”

If the technology itself is less dramatic than the headlines suggest, why does the hype persist? Gallagher locates much of the answer in the hypercompetitive atmosphere of a field that is changing at breakneck speed. Researchers in both academia and industry are expected to publish frequently, and the dominant venue for that publication is the conference rather than the peer-reviewed journal. Conferences move far faster than journals, which can take many months or even years to release a paper, and that speed creates pressure. Under such conditions, Gallagher argues in the book, researchers may be pushed to overstate their findings or understate their limitations, inflating results to stand out in an environment where thousands of papers compete for attention.

The pressure does not end with publication, and this is where the book’s analysis of scientific communication becomes especially pointed. Nearly everyone Gallagher interviewed mentioned the expectation of building a public relations campaign around each paper. “Now you must advertise the paper on social media. It also needs a blog post, a GitHub repository of the code, shared results on LinkedIn, Bluesky, even YouTube videos,” he said. “You can be a researcher and you also have to be a content creator and influencer, producing a conference paper and also six other content genres.” The consequence, as Gallagher wrote in the book, is that “the current AI publication landscape may lower publication quality while possibly sensationalizing scientific results.” A system that rewards volume, visibility, and self-promotion over careful, hedged, peer-scrutinized claims is a system structurally inclined to produce hype, regardless of the intentions of individual researchers.

Against that structural drift, the book offers a set of concrete recommendations for countering the hype cycle. First, Gallagher said, researchers should stress that AI is a type of automation designed to serve a specific need, whether that means detecting trash in the ocean or making sense of vast amounts of scientific data, rather than a general-purpose intelligence hovering above ordinary engineering. Second, AI and machine learning researchers should be trained and encouraged to translate their research for many different audiences, including nonexperts, and to discuss their work in the media, including explaining the technical, granular aspects of their field on long-form platforms such as YouTube, where nuance has room to breathe in a way that a social media post does not. Third, Gallagher calls for changes to incentives in academic publishing, particularly for conference proceedings, so that venues are no longer pressured to produce a high quantity of articles within a short time frame at the expense of quality and accuracy.

Finally, the book has practical advice for nonexperts who want to understand artificial intelligence more accurately: seek out technical AI researchers rather than business leaders promoting their products or podcasters seeking an audience. Many researchers, Gallagher noted, maintain YouTube channels devoted to explaining the technical details and core concepts of the field, and engaging with those primary sources can demystify the technology in a way that secondhand commentary cannot. The broader lesson of the book is that the story of artificial intelligence is not a story of magic or menace but a story of people: mathematicians and engineers iterating on models, wrestling with alignment problems, and navigating a publication culture that often rewards exaggeration. Listening to those people, Gallagher argues, is the most reliable way for the public, and especially for educators shaping the next generation, to develop a clear-eyed, critical understanding of what AI can do, what it cannot, and why the gap between the two matters so much.

Subject of Research: How artificial intelligence researchers communicate their work and the hype surrounding AI

Article Title: Book counters AI hype by examining how researchers talk about their work

Article References: Book counters AI hype by examining how researchers talk about their work. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: artificial intelligence, machine learning, AI hype, science communication, alignment problem, academic publishing, John Gallagher, University of Illinois, natural language processing, research interviews, TrashCan, AI education

The largest of the new awards, valued at $620,095, places Chen in the principal investigator’s seat for a project titled “Public Library Services for Older Adults in the AI Era: Status, Gaps, Solutions, and Recommendations.” Chen will collaborate with Ping Li of the City University of New York’s Queens College on an effort to strengthen public library services for older adults, a population that often faces the steepest barriers when new technologies arrive. The project will work directly with four public library systems in Illinois, New York, and Washington, DC, grounding its findings in the operational realities of institutions that serve millions of patrons. Rather than treating older adults as an afterthought in technology adoption, the research aims to develop new service models designed explicitly for this group in an era when artificial intelligence increasingly mediates access to information.

The deliverables planned for the project read like a practical roadmap for library systems nationwide. The team intends to produce practical guidelines for developing programs and services focused on older adults, training materials for librarians who must now field questions about AI tools they may never have used themselves, and literacy courses that teach older adults how to navigate AI-mediated information environments. The project will also generate evidence-based recommendations intended to inform library policy and practice, closing the loop between research findings and the institutional decisions that determine whether older patrons benefit from or are excluded by technological change. Both of Chen’s funded projects begin in September 2026 and are expected to run through August 2029, a three-year horizon that allows for iterative design, testing, and refinement across the partner library systems.

Chen’s second award, on which she serves as co-principal investigator, tackles a different frontier of the same technological shift. “ORAL-AI: High Efficacy Content Representation and User Engagement of Oral Digital Collections with Multimodal LLMs” is led by Haihua Chen of the University of North Texas and received $490,396. The project confronts a genuine technical problem: multimodal large language models—systems that can process combinations of text, audio, and images—are being promoted as tools for searching and summarizing archival collections, yet their limitations in the delicate context of oral history remain poorly understood. Oral histories are not ordinary documents. They carry the voices, cadences, and memories of individual narrators, often recounting events of profound historical significance, and errors introduced by an AI system can distort testimony in ways that violate archival integrity.

The ORAL-AI team plans to evaluate those limitations systematically and then build the infrastructure that responsible AI-assisted access requires. Central to the effort is a structured benchmark dataset, which will allow researchers to measure how well multimodal models actually perform on oral history materials rather than relying on anecdotal impressions. The project will also develop a modular, open-source framework supporting three capabilities: cross-modal retrieval, which lets users find relevant passages across different media types; evidence-grounded question answering, which ties the system’s answers back to specific source material instead of allowing free-form generation; and narrative summarization, which condenses long testimonies while preserving their meaning. The research team will collaborate with the Densho Digital Repository and other nationally significant oral collections, ensuring that the framework is tested against real archives with real stakes rather than synthetic test data.

While Chen’s projects probe the intersection of AI and adult services, Sara Schwebel is directing a grant that reaches younger audiences through the material culture of the book itself. Schwebel, professor and director of the Center for Children’s Books, is principal investigator for “Reading the Revolution: Telling the Story of the Nation’s Founding to America’s Youth,” a one-year project valued at nearly $100,000. The project takes an exhibit-and-field-trip model as its organizing structure and brings together an unusually rich set of campus and community partners: the University of Illinois Library, Skeuomorph Press & BookLab, and the Champaign County Forest Preserve. At a moment when public understanding of American history is intensely contested, the project bets on a time-tested method—putting young people in direct contact with historical artifacts and the books that have carried the founding story to each new generation.

The interpretive and digital exhibit at the heart of the project will draw on the University of Illinois library’s remarkable collections, including the Rare Book and Manuscript Library, the Sousa Archives and Center for American Music, and the Social Sciences, Health, and Education Library’s School Collection. That last holding is particularly significant for the project’s aims, since the School Collection documents how children’s literature has transmitted national history to young readers across decades of changing pedagogy and politics. To complement the physical and digital exhibit, the project will create a digital collection and a readers’ advisory guide prepared by the editorial board of The Bulletin of the Center for Children’s Books, one of the field’s most respected review journals. Supplemental materials will be developed by Center for Children’s Books staff together with a “Reading the Revolution” Scholars Council drawn from other colleges and universities, giving the project scholarly depth beyond a single campus.

The programming built around the exhibit is designed to reach multiple audiences simultaneously. Plans include American history-themed storytimes for young children, school-aged library programming, field trips for middle school students throughout the region, and professional development workshops for area librarians and teachers—the adults who will carry the project’s resources into classrooms and libraries long after the grant period ends. Coordination of this ambitious slate falls to Center for Children’s Books Assistant Director Suzan Alteri, Associate Professor Ryan Cordell, doctoral student Owen Monroe, and additional graduate students, a staffing model that doubles as training for the next generation of youth services and digital humanities professionals.

The fourth grant shifts the focus to the librarians themselves. Kahyun Choi serves as a consultant on “Asset-Based AI Toolkit: Leveraging Librarian Cultural Wealth for AI Literacy,” a three-year, $455,066 project led by Hee Rin Lee of Michigan State University. The project’s premise is a deliberate inversion of the deficit framing that often surrounds professional development: instead of asking what librarians lack in their understanding of artificial intelligence, it asks what assets they already possess—what the project calls librarian cultural wealth—and how those strengths can be recognized and leveraged to build and sustain AI literacy programs. The concrete output is a web-based interactive toolkit that librarians can use to identify their own capabilities and translate them into programming for their communities.

Choi’s role in the project draws directly on her distinctive research profile. As an expert in AI for music and poetry within the field of library science, she will provide subject-matter expertise, curate supplementary resources, and offer iterative feedback as the toolkit is developed. Her involvement is not new to this line of inquiry: the toolkit project builds on the team’s earlier IMLS-funded AI literacy work, which produced a program for public libraries with Choi as co-principal investigator. That continuity matters, because AI literacy is not a one-time curriculum but a moving target—each new generation of models changes what patrons need to know and what librarians need to teach.

Taken together, the four National Leadership Grants sketch a portrait of where library and information science research is heading. The projects share a conviction that intelligent technologies will not simply be absorbed by libraries and archives on their own terms; they must be studied, benchmarked, and shaped by the professionals who steward the nation’s information resources. They also share a methodological commitment to partnership—with public library systems in three jurisdictions, with a nationally significant oral history repository, with campus special collections and a community forest preserve, and with peer institutions across the country. Whether the subject is an older adult learning to question an AI chatbot, a researcher probing whether a language model can faithfully summarize a survivor’s testimony, a middle schooler encountering the Revolution through a rare book, or a librarian discovering that her existing expertise is the foundation for teaching AI literacy, the Illinois-led projects treat information institutions not as casualties of technological change but as the places where that change can be made humane, accurate, and broadly accessible.

Subject of Research: IMLS-funded information science research on AI in libraries, oral history archives, children's literature, and librarian AI literacy

Article Title: IMLS grants support information science projects from Illinois researchers

Article References: IMLS grants support information science projects from Illinois researchers. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: IMLS, information science, artificial intelligence, public libraries, older adults, oral history, multimodal large language models, children's literature, AI literacy, digital exhibits, University of Illinois, library services

The project, described in a new article in the Journal of Agromedicine, began with a practical goal: giving Extension educators a way to learn more about agricultural injuries in their own communities and to compare patterns across different regions. Salah Issa, an Illinois Extension specialist and associate professor in the Department of Agricultural and Biological Engineering, part of the College of Agricultural, Consumer and Environmental Sciences and The Grainger College of Engineering, explained that the team eventually broadened the scope to include search options tailored to researchers, whose data demands differ considerably from those of frontline educators.

The result is the Agricultural Injury Dashboard, a web-based platform covering agricultural injuries in five Midwestern states: Iowa, Illinois, Michigan, Minnesota, and Wisconsin. The dashboard contains tracks for researchers and educators to customize their queries according to specific purposes, allowing users to search across a variety of parameters and view the results as interactive graphs and charts. Rather than presenting static tables or downloadable spreadsheets, the platform responds dynamically to user input, making it possible to explore the underlying data from multiple angles without any specialized analytical training.

Lead author Sihan Li, who helped develop the dashboard as a doctoral student in Agricultural and Biological Engineering and now continues the work as a visiting Extension outreach associate, emphasized the interactivity at the heart of the design. Everything on the platform responds to selection: clicking on a given year and a certain type of injury causes every chart to highlight that specific injury type and display its associated age, gender, and fatality distribution. Because the charts work in tandem, users can progressively hone in on the data according to their needs, narrowing from broad regional trends down to the specific demographic and circumstantial details of particular incident categories.

The development process began with a systematic review of existing sources where users could look up agricultural injury information. The researchers found that these resources were not very user-friendly, a shortcoming that limited their usefulness for the very audiences—educators, safety specialists, and community planners—who most needed timely and accessible data. The team set out to build something functional, efficient, and easy to use, and they refined the design through pilot testing with different user groups, incorporating feedback at each stage to improve navigation, clarity, and the relevance of the available filters.

To populate the dashboard, the researchers drew on the AgInjuryNews database, a resource that categorizes agricultural injuries reported in news coverage across the United States and Canada. For the initial release, they included AgInjuryNews data from the five Midwestern states spanning 2016 to 2024. The dataset provides rich contextual information for each incident, including the cause and type of injury, the location, the time of occurrence, and victim demographics such as age and gender. In addition, each record carries classification codes from the Occupational Injury and Illness Classification System, known as OIICS, and the Farm and Agricultural Injury Classification, or FAIC, giving the platform a standardized structure that supports rigorous comparison across years, states, and injury categories.

Li and Issa built the interactive dashboard using Microsoft Power BI, a business analytics platform that provides a wide range of data visualization capabilities. The choice allowed the team to link multiple visualizations so that they update in concert, a technical feature that underpins the dashboard’s ability to highlight selected injury types across every chart simultaneously. The partnership with AgInjuryNews supplied the initial data foundation, but the researchers plan for future updates to be populated by their own data collection efforts, giving the University of Illinois team direct control over the quality and timeliness of the records feeding the platform.

That in-house data operation is already substantial. According to Issa, the University of Illinois database for agricultural injuries is based on news clippings, and the team also asks the Illinois Department of Public Health to review the compiled information so that any missing cases can be added. He described the resulting dataset as probably one of the best available for fatal agricultural injuries in the state. While this means the dashboard will be primarily focused on Illinois going forward, Issa noted that users from other states should find it easy to adapt the platform to their own purposes by adding their own datasets, an approach that could extend the tool’s benefits well beyond the Midwest.

The researchers argue that access to a resource providing an overview of agricultural injuries is essential to education and prevention. Because accidents, especially fatal ones, happen rarely enough within any single community that patterns may go unnoticed, Issa stressed the importance of staying abreast of what is happening across the wider region. When trends of incidents become visible across multiple communities, Extension services and safety organizations can launch targeted programs to educate farmers and address emerging hazards before they grow into larger problems. In this way, the dashboard functions not merely as a record of past events but as an early-warning system that transforms scattered incident reports into actionable regional intelligence.

Looking ahead, the team hopes to expand the dashboard so that it captures not just the occurrence of accidents but also their monetary and human impact on communities, adding an economic and social dimension to the existing injury data. The research article, titled “Design of an Interactive Agricultural Injury Dashboard: Enhancing Data Visualization and Stakeholder Engagement Using AgInjuryNews Data,” is published in the Journal of Agromedicine. The project was partially supported by the Centers for Disease Control and Prevention’s National Institute for Occupational Safety and Health, with additional research support provided through the Hatch Act of 1887 from the U.S. Department of Agriculture’s National Institute of Food and Agriculture. The work also credits AgInjuryNews.org for providing access to the agricultural injury data on which the platform’s initial dataset was built, a collaboration that illustrates how open data resources and university research teams can combine to produce tools with immediate practical value for rural safety.

Subject of Research: Development of an interactive dashboard for visualizing and analyzing agricultural injury data to support safety education and prevention.

Article Title: U of I researchers develop interactive tool to track agricultural injuries, strengthen prevention efforts

Article References: U of I researchers develop interactive tool to track agricultural injuries, strengthen prevention efforts. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: agricultural safety, agricultural injuries, data visualization, University of Illinois, AgInjuryNews, Extension education, farm fatalities, Power BI, Journal of Agromedicine, injury prevention, Midwest agriculture, occupational safety