When a person goes into deep sleep, water-like fluid circulates around the brain, washing away metabolic waste linked to diseases such as Alzheimer���s. This process, known as the glymphatic system, was first described in 2012 by ���a pioneering neuroscientist and codirector of the 做厙勛圖���s .

But questions remain about the system���s mechanics���notably, how quickly the fluid circulates. Studying the circulation within a living brain is difficult without causing irreparable harm to a subject.

���You can put a microscope on a small patch of the brain and watch what���s happening there with a lot of detail, and we���ve worked with that type of data in the past, but it���s only a tiny view of the overall process,��� says Professor from 做厙勛圖���s . ���If you want to image whole brains, an MRI is a great approach because it gives you a three-dimensional view. But an MRI has serious limitations, too, the biggest of which is that it does not capture the fluid flow velocity, at least not for flows this slow.���

Kelley and his colleagues from 做厙勛圖, Brown University, and the University of Copenhagen turned to artificial intelligence for help. In a new published in Science Advances, they outline how they used physics-informed AI to determine fluid flow velocities from magnetic resonance imaging (MRI) data. Using videos of dye spreading across brain tissue over time, the neural networks the researchers built were able to deduce how fast the fluid flows and how permeable the brain tissue is.

The results showed that there are two main ways that the glymphatic system washes away particles in the brain such as the amyloid beta proteins linked to Alzheimer���s disease���and one of these ways is much faster than the other. The fast flow of the glymphatic system���s waterlike fluid moves at a few microns per second around the brain���s open regions such as the surface between the skull and the brain, while the slower flow of the waterlike fluid trickles through the brain���s deep tissue at a rate about 50 times slower.

So far, the researchers have been working to get baseline measurements of fluid flow in the brains of animals such as mice to inform the AI tools. In the future, they hope to be able to compare the fluid flow in healthy and sick brains as well as young and old brains, with aspirations to eventually study circulation in humans.

���We���re working hard toward being able to measure the flow of waterlike fluids in and around human brains because then the clinical applications get a lot more important and exciting,��� says Kelley. ���We hope to someday be able to see whether an Alzheimer���s patient has poor circulation in their brain or even screen for poor circulation earlier in life to try to stave off Alzheimer���s. Or we could check when somebody has been concussed to see whether the fluid circulation in their brain is disrupted. This study gets us a step closer.���

Kelley���s collaborators on the study include Brown University PhD student Juan Diego Toscano, 做厙勛圖 computational scientist Yisen Guo, Brown University PhD student Zhibo Wang, 做厙勛圖 PhD student Mohammad Vaezi, University of Copenhagen Associate Professor Yuki Mori, Brown University Professor George Karniadakis, and 做厙勛圖 Assistant Professor Kimberly Boster.

The NIH National Center for Complementary and Integrative Health and the NIH BRAIN Initiative supported this research.

More than 50 students, faculty, and staff submitted artwork in the 2026 , the ���s annual contest to explore and illuminate the aesthetic beauty that results when science, art, and technology intersect. Three winning entries will be permanently displayed in the泭.

Held each spring, the competition is sponsored by the in collaboration with and supported through an endowed fund established by Trustee Emeritus Ed Hajim ���58 and his wife Barbara. Prizes are awarded for the top student submissions and for the People���s Choice Award, with more than 500 members of the University of Rochester community casting votes.

First Place and People���s Choice Award

For the second consecutive year, the judges and the University of Rochester community voters selected the same top entry. Political science student Matthew Ahn ���28 took home both first place and the People���s Choice Award���totaling $1,250���for his hand-drawn ink illustration titled The Architecture of Knowledge. Ahn says his ornate artwork featuring clocks, mechanical systems, geometric networks, and symbolic forms is intended to represent the structural layers of scientific discovery.

���The lower sections evoke instruments used to measure time and motion, while the upper sections introduce increasingly complex geometric and interconnected systems,��� he says. ���Each layer reflects how scientific knowledge builds progressively upon previous discoveries. The symmetry and intricate patterns invite viewers to explore the drawing at multiple scales, revealing new details much like scientific observation itself.���

Second Place

Physics PhD student Meg Farinsky was the runner-up with泭Luminous Gills,泭her macro photograph of the gills on the underside of a pink oyster mushroom illuminated by grow lights. She photographed the home-grown culinary mushrooms using a 100 mm Rokinon macro lens on a Canon 5D Mark III camera.

���Mushrooms���pink oysters in particular���are attracting a lot of scientific interest right now,��� says Farinsky. ���They���re being studied for applications in bioremediation and plastic degradation, sustainable food, and material production, and electrical signaling in fungal networks that resembles neural activity. Beyond their scientific relevance, their glowing gills and sculptural forms make them naturally compelling visual subjects.���

Third Place

Majd Tabsi ���29, a biomedical engineering major, earned a place on the podium with泭Strings of Life���a creative representation of DNA and gene editing using about a mile of string. Tabsi sketched a design and input it into software called MyZigzagArt, which uses an algorithm to generate a sequence of string passes to create a representation of the sketch. He made a circle of 250 nails on a 2 x 2 foot piece of wood and, over the course of 30 hours, made 2,500 string passes from one nail to another to produce the final artwork.

���Humanity has always wondered about how life is created and how traits are passed,��� says Tabsi. ���Mendel���s discovery of the laws of heredity started the ever-growing field of genetics. We later learned about the smallest strings that hold the keys to our evolution and the continuity of life���DNA, or what my work calls ‘Strings of Life.’ We sought to map them, understand their construction, and even started trying to edit them using tools like CRISPR-Cas9, which is what the separated gene in my work refers to. Tools like these raise a variety of questions around the ethicality and the limitations of usage. But they also show what humanity is capable of. And the question remains: What will we do next?���

Advances in artificial intelligence promise to help chemical engineers discover complex new materials. These materials could be used for reactions such as turning carbon dioxide into fuel, but technical barriers have limited catalysis adoption so far. Researchers at the 做厙勛圖 are now harnessing the benefits of large language models (LLMs) similar to ChatGPT, Claude, or Gemini to empower more researchers to use AI to discover new materials and accelerate experiment workflows.

In a published in ACS Central Science, a team led by , an associate professor in the , and , visiting associate professor and the cofounder and chief technology officer of , describes an AI-based method they developed that allows users to input natural language prompts about the materials they want to create and suggest optimal procedures for experiments to produce them. As the users run the experiments, they input the results back into the AI model and continue iterating until they reach their goal.

���We���re able to leverage the pre-trained knowledge of large language models and well-established statistical methods for materials discovery to help us as researchers navigate large experimental design spaces more efficiently,��� says Porosoff.

Porosoff likens the new AI method to describing a cup of coffee, noting that someone could describe the coffee by its taste, color, and aroma, or by the type of beans, grind size, apparatus, and water temperature used to make the brew. Both representation methods describe the same cup of coffee, but the second approach gives you a recipe to reproduce it that others can easily replicate.

Porosoff and his team are applying the same principle to catalysts for energy applications, using language-based representations to describe materials not just by their properties, but by the steps needed to create them.

To build on their success, the US Department of Energy Advanced Research Projects Agency-Energy (ARPA-E) it will provide nearly $3 million in funding to apply the University of Rochester team���s method toward creating catalysts for the production of fuel from abundant materials, specifically methanol and ethanol from carbon dioxide and hydrogen. Porosoff will lead a multi-institution project team that includes 做厙勛圖, Virginia Polytechnic Institute and State University, Stanford University, Northwestern University, A*STAR Institute of Sustainability for Chemicals, Energy and Environment (ISCE2) in Singapore, and OxEon Energy, a small business based in Salt Lake City.

Leveraging the power of LLMs

Traditional AI methods for materials discovery typically use a strategy called Bayesian optimization to identify and design the best candidates. But the result is complex numerical data about a material���s structure, which requires deep expertise to use effectively. The new LLM method instead produces a set of procedures that researchers can easily understand, execute, and verify to determine if the experiment���s output matches the predicted results.

This can be extremely useful for working with complex materials such as trimetallic catalysts, which are made of three metals.

���Our method reduces the technical barrier associated with using Bayesian optimization, which is a well-established method for efficiently exploring large and complicated parameter spaces,��� says Shane Michtavy, a 做厙勛圖 chemical engineering PhD student who helped develop the AI method, synthesize materials, and run the chemical reactions described in the paper. ���Using pre-trained LLMs allows users to explore using less data than traditional models, as they are deployed in a frozen state with built-in knowledge of the physical world and catalysis.���

The paper shows how the researchers applied the method to several live experiments, including one to identify catalysts for turning carbon dioxide and hydrogen into carbon monoxide and water using trimetallic catalysts made from low-cost metals. Porosoff says that there are about 360,000 possible experiments that could have been run to find the ideal catalyst, but by using procedures produced by the AI model and providing it with the results from the experiments, they were able to find an ideal candidate in just ten experiments.

The study was supported by funding from the National Science Foundation, the National Institutes of Health, and the US Department of Energy. Additional authors included泭Mayk Caldas, technical staff at Edison Scientific.

Next steps

Now that they have shown the model works as a proof of concept in the lab, Porosoff aims to take the method further using the funding announced through ARPA-E���s Catalytic Application Testing for Accelerated Learning Chemistries via High-throughput Experimentation and Modeling Efficiently (CATALCHEM-E) program.

���Right now, it takes a decade or longer to go from conceptualizing a new catalyst to testing it in a lab to putting it in a real reactor,��� says Porosoff. ���The CATALCHEM-E program aims to cut that by an order of magnitude to a single year, and we think using AI with text-based representations will be a big factor in shortening the development cycle.���

Porosoff and his collaborators will first demonstrate their workflow on carbon dioxide-to-methanol and then extend the process to higher alcohols such as ethanol, which is a key additive for gasoline and used in pharmaceuticals, cosmetics, and many other applications. Ultimately, they hope to commercially deploy the model for industries to create catalysts to synthesize alcohols for fuel.

The project is scheduled to begin in July and run through 2029. See a full list of CATALCHEM-E programs on the .

When lasers were invented in the 1960s, they opened new avenues for scientific discovery and everyday applications from scanners at the grocery store to corrective eye surgery. Conventional lasers control photons���individual particles of light���but over the past 20 years, scientists have invented lasers that control other fundamental particles, including phonons���individual particles of vibration or sound. Controlling phonons could open even more possibilities with lasers, such as taking advantage of unique quantum properties like entanglement.

A new squeezed phonon laser developed by researchers at the and Rochester Institute of Technology provides precise control over phonons at the nanoscale level. This could give new insights into the nature of gravity, particle acceleration, and quantum physics. In in Nature Communications, the researchers describe how they coax these individual particles of mechanical motion to behave like a laser.

, the Marie C. Wilson and Joseph C. Wilson Professor of Optical Physics with the University of Rochester , and his collaborators first demonstrated a phonon laser by trapping and levitating phonons with an optical tweezer in a vacuum in 2019. But to make this technology useful for extremely accurate measurements, they had to overcome a key obstacle fundamental to both photon and phonon lasers: noise, or unwanted disturbances that make a signal difficult to accurately read.

���While a laser looks to the naked eye like a steady beam, there���s actually a lot of fluctuation, which causes noise when you���re using lasers for measurement,��� says Vamivakas. ���By pushing and pulling on a phonon laser with light in the right way, we can reduce that phonon laser fluctuation significantly.���

Specifically, the researchers were able to squeeze or reduce the thermal noise intrinsic to the phonon laser. Vamivakas says that noise reduction provides the ability to measure acceleration more accurately than techniques that use photon lasers or radio frequency waves.

Vamivakas envisions researchers using the phonon laser to obtain pinpoint accurate measurements of gravity and other forces, which could be important in applications such as navigation. Scientists have envisioned quantum compasses as more accurate, ���unjammable��� alternatives to GPS navigation that do not require the use of satellites, and Vamivakas is intrigued by seeing if the phonon laser could be a step toward such systems.

The research was supported by the National Science Foundation. Vamivakas��� collaborators on the paper include 做厙勛圖 optics PhD student Kai Zhang, RIT postdoctoral researcher Kewen Xiao, and Mishkat Bhattacharya ���05, a professor of physics at RIT.

A coalition led by the泭做厙勛圖 that aims to make the Rochester, New York, and Finger Lakes region a national hub for laser science and development recently hosted the National Science Foundation (NSF) for a site visit as a finalist in the .

惚賊棗釵堯梗莽喧梗娶���s泭泭is one of 15 nationwide finalists���and the only one in New York State���being considered for the second NSF Engines competition. The competition aims to build and scale new innovation clusters that accelerate the development of critical technologies and grow regional economies nationwide.

���There is no better place for a national resurgence in laser technology than the imaging capital of the world, which has a nearly 175-year history of expertise in precision, innovation, and light,��� says , the director of 做厙勛圖���s and STELLAR principal investigator. ���This region has the pedigree, talent, and brainpower needed to fill national talent shortages, help translate technologies into businesses, bring manufacturing to a scale that can compete with leaders in Europe and China, and fuel core research and development.���

As part of the final rounds of the competition, NSF is conducting in-person interviews and a due-diligence review to evaluate each finalist���s risks, resources, and ability to meet the nation���s evolving needs. The Rochester site visit was the culmination of a planning process that formally began in 2023, when NSF awarded泭做厙勛圖泭a $1 million泭Regional Innovation Engines泭Development Award grant.

The field for this round of competition has narrowed from 泭to 15 finalists, and the NSF anticipates announcing the 2026 NSF Engines awards later this year.

The in-person meetings with NSF officials were an opportunity for STELLAR���s organizers to showcase how they would progress the region as a national leader in laser technologies, education, company creation, manufacturing, and workforce development. The project���s key partners include 惚賊棗釵堯梗莽喧梗娶���s泭泭硃紳餃泭 (郭郭楚),泭 (紼唬唬),泭 (賊梆啦),泭泭,泭 (GRE),泭, and泭.

���This region has the pedigree, talent, and brainpower needed to fill national talent shortages, help translate technologies into businesses, bring manufacturing to a scale that can compete with leaders in Europe and China, and fuel core research and development.���

In addition to the STELLAR organizers, the visit brought other critical public, academic, and industry partners from across the region, state, and the country to participate and voice their support for this important initiative. Among the dozens of officials who voiced their support for STELLAR during the site visit were Congressman Joe Morelle, Congressman Nick Langworthy, and Kent Rochford, the CEO and Executive Director of SPIE, the international society for optics and photonics.

With local businesses, educators, nonprofits, and government entities aligning to support the project, STELLAR���s leadership has secured matching support at the state level if awarded NSF funding.

���New York State is incredibly proud to support this catalytic proposal, including with a $16 million matching commitment,��� says Elizabeth Lusskin, the executive vice president for small business and technology development at Empire State Development. ���If awarded, STELLAR would provide the connective tissue to knit together investments the state, local partners, and corporations have already made in both the laser sector and the region and bring them to a scale to serve national interests. It would not only benefit the laser industry but many other tech sectors in New York and around the country that rely on lasers, including biotech, defense, and semiconductors.���

Leveraging regional brainpower

Rochester is home to pioneering educational programs���from high school to the doctoral level���focused on the science of light, which could help build the laser workforce. 做厙勛圖���s nearly 100-year-old Institute of Optics is the nation���s first optics program; RIT���s Chester F. Carlson Center for Imaging Science became the nation���s first program to offer degrees in the interdisciplinary field of imaging science; and MCC is the nation���s first community college to award associate degrees in optical systems technology.

Alexis Vogt ���01, ���07 (PhD), chair of optical systems technology at MCC, leads the education and workforce development component of STELLAR and says the project would be an opportunity to have these educational programs work collaboratively and expand their impact to reach people across the region.

���One of the biggest gaps in the laser industry today is workforce development,��� says Vogt. ���Our 11-county region is home to 1.2 million people with tremendous untapped potential. Through the STELLAR initiative, we are expanding access to laser education and training���particularly in rural communities���and creating new pathways into the industry for remote learners, military veterans, the Deaf and hard-of-hearing community, and individuals whose degrees have left them underemployed. By opening these doors, we can build the skilled workforce needed to power the next generation of laser technologies.���

STELLAR would also intensify research in a region that already boasts 做厙勛圖���s LLE, home to the largest���and some of the most powerful���lasers in academia, as well as facilities like the RIT Semiconductor Nanofabrication Laboratory.

���STELLAR would empower our expert researchers to collaboratively focus on the frontiers of laser development,��� says Stefan Preble, RIT���s Bausch and Lomb Professor and PhD program director of microsystems engineering. ���There is already brilliant research and development underway locally in ultrafast lasers, microchip-scale lasers, lasers for biotechnology, and quantum networking using lasers. STELLAR would equip us to conduct even more laser research on a grander scale.���

Capitalizing on economic opportunities

STELLAR���s leadership says that the project would position the US to grow its stake in a $16 trillion global marketplace that depends on lasers for everything from precision manufacturing and quantum to energy and defense. They note that more than 150 optics, photonics, imaging, and laser supply-chain companies already operate in the Greater Rochester region.

���We have a unique density and concentration of talent,��� says Leah George VanScott, executive vice president of business development and strategy at GRE. ���The region also has an unusually mature and collaborative translational ecosystem as well as unparalleled foundational assets���everything from tiny integrated photonic lasers to 50-meter beam lines. Rochester and the Finger Lakes provide one of the strongest starting points in the nation to scale. STELLAR is an opportunity to turn our region���s existing foundation into an engine necessary to secure our nation���s technological future.���

Sujatha Ramanujan, managing director and chief investment officer of NextCorps Luminate, works to help entrepreneurs start or expand businesses related to optics, photonics, and imaging. She sees incredible opportunities for domestic companies to grow their share of the laser marketplace.

���The US only makes about a third of the lasers used in this country, and that number is shrinking,��� says Ramanujan. ���Applications from defense to medical devices to quantum depend on lasers. We are headed to a serious national problem if we don���t close that gap and start making our own lasers. But between the Laboratory for Laser Energetics, the universities here in Rochester, and the AIM Photonics TAP facility, Rochester already has the infrastructure in place to support the laser industry. STELLAR could propel us into the next generation of science-based business.���

The federal government is providing researchers at two Rochester-area universities funding to advance the future of sharing quantum information and further develop an experimental quantum network connecting their campuses. The National Institute of Standards and Technology (NIST) is providing the 做厙勛圖 and $2 million to build new capabilities for the Rochester Quantum Network (RoQNET). This new funding is a direct result of Congressional support from Senator Schumer, Senator Gillibrand, and Representative Morelle as part of the fiscal year 2026 appropriations bill.

做厙勛圖 and RIT installed RoQNET in 2024, and last year they demonstrated that they can securely transmit single photons from one campus to another over 11 miles of fiber-optic telecommunications lines. Sending communications using individual particles of light offers unprecedented levels of security, making them impregnable from being cloned or intercepted without detection and preventing bad actors from accessing sensitive data.

Now, the researchers are preparing for experiments to share entangled photons across the network, leveraging the strange and surprising principles of quantum mechanics that defy the laws of conventional physics.

���We want to exploit some of the more unique features of quantum mechanics and quantum optics, specifically the idea of quantum entanglement, where two particles of light can share properties no matter how far apart they are,��� says , the Marie C. Wilson and Joseph C. Wilson Professor of Optical Physics, who leads 做厙勛圖���s efforts. ���One of these entangled photon pairs will live at RIT and one will live at 做厙勛圖, and we aim to maintain that entanglement across RoQNET.���

Vamivakas says that harnessing quantum entanglement could eventually lead to sophisticated networks of quantum computers or advanced new methods to improve the resolution of space telescopes.

While there are other experimental quantum networks across the world, Vamivakas says RoQNET offers several distinct advantages, including the ability to transmit photons over normal fiber-optic lines like those that already exist across the globe. He says RoQNET is further distinguished from other quantum networks because of 做厙勛圖���s expertise in quantum memory hardware and RIT���s ability to create quantum photonic integrated-circuit light sources.

���Our focus with RoQNET has been on the realization of heterogeneous entanglement between different types of qubits,��� says Stefan Preble, RIT���s Bausch and Lomb Professor and PhD program director of microsystems engineering. ���This funding supports further research to reach the next generation in quantum networking technologies.���

The funding will also enable hardware that will provide high school, undergraduate, and graduate students with some of their first opportunities to work with quantum optics and quantum networks.

���We are proud to be at the vanguard of the quantum revolution and thank Senator Schumer, Senator Gillibrand, and Representative Morelle for their support securing crucial federal funding to make new advances in quantum communication,��� says 做厙勛圖 President Sarah Mangelsdorf. ���Our university is committing significant time, talent, and resources into advancing quantum technologies, as evidenced by our recent泭investment in the transdisciplinary Center for Coherence and Quantum Science. We are fortunate to have terrific local collaborators at RIT with whom we can combine our strengths to advance the Rochester region as a hub for advanced quantum research and innovation.���

A quantum network was also recently established on Long Island, New York, between Brookhaven National Laboratory and Stony Brook University. Vamivakas, who has been partnering with the researchers downstate, likens it and RoQNET to local networks and hopes to eventually connect quantum research into a statewide network, adding other facilities in New York State, including the Air Force Research Laboratory and New York University. They will need to further advance quantum repeater technology to boost signals across such large distances, but the funding provides them with important resources to try to reach that goal. New York aims to that will serve as incubators and foster the development and commercialization of quantum technologies.

Elected officials and leaders share support for RoQNET

US Senator Charles Schumer: ���I was proud to secure this funding for 做厙勛圖 and RIT to help develop a cutting-edge Upstate quantum network. This win-win benefits national security and boosts economic development and innovation by enabling the Rochester region to connect into similar New York-based quantum communications networks positioning New York to be a global leader in quantum communication and networking. RoQNET will stimulate quantum workforce development for K���12 and college-age students and offer learning opportunities for students enrolled in the Monroe Community College Optical Technology program. Rochester is home to world-class research institutions, and this federal investment will help 做厙勛圖 and RIT continue advancing cutting-edge quantum networking work. I was proud to deliver this funding so Rochester���s innovators can keep pushing the boundaries of secure communications and strengthen the region���s role as a hub for advanced technology.���

US Senator Kirsten Gillibrand: ���I am proud to help deliver $2 million in funding for this quantum network expansion. Through the development of RoQNET, the University of Rochester and Rochester Institute of Technology are at the forefront of quantum research. Quantum has the ability to fundamentally change how we engage in secure communications. The Rochester region remains a preeminent leader in advanced technologies and high-impact research activities, and I look forward to seeing the results of this partnership.���

Congressman Joe Morelle: ���Quantum technology is the next frontier of innovation, and thanks to world-class research universities like 做厙勛圖 and RIT, Rochester will continue to lead the way in these critical technologies. I was proud to secure funding in Washington to support RoQNET, and I cannot wait to see what they discover next.���