Research

PhD student’s research addresses how adolescent social media use predicts in-person friendship networks

Shayla Brown — Wed, 04 Feb 2026 18:03:18 +0000

PhD student’s research addresses how adolescent social media use predicts in-person friendship networks Shayla Brown Wed, 02/04/2026 - 13:03

�鶹��Ƶ doctoral student Caroline Toth looks at how adolescents’ social media use is associated with the structure of their in-person friendship networks. Her research shows that friendships were more likely among youth with similar levels of problematic social media use and digital status seeking.

Photo by Getty Images

“This reflects a social network mechanism known as homophily, which is essentially love of the same, meaning that youth select friends who are similar to themselves in problematic social media use and digital status seeking. By doing this study, we're showing that friendships aren't forming at random, but being shaped in part by digital habits and values that are shared among friendship networks,” said Toth, a graduate research assistant in George �鶹��Ƶ’s Applied Developmental Psychology Program in the Department of Psychology.

Her first paper on the topic was recently published at the Journal of Children and Media.

“Adolescence is a very sensitive time for social processing. Teens are very sensitive to social cues,” said Toth, who earned her master’s degree from the same George �鶹��Ƶ program in 2021.

Caroline Toth. Photo provided.

Toth’s paper, “Problematic social media use, availability distress, and digital status seeking: Links to friendship network structure among U.S. adolescents,” explores how adolescents use social media to feel connected to others and gain social standing.

“Previous research has mainly examined links between social media use and disrupted sleep, anxiety, and depression. However, as a social networks scholar, I'm really interested in how adolescents’ online behaviors and emotional reactions to social media affect the structure of their friendship network,” Toth said.

This research is especially relevant to Generation Alpha, born between 2010 and 2024. When adolescents select friends who engage in similar problematic online behaviors, the resulting shared emotional thoughts, reactions, and behaviors can start to feel expected and encouraged within these friendship groups, said Toth.

This can be especially harmful because teens use social media to gain popularity and approval. The normalization of problematic online behaviors within friend groups makes them appear necessary for social success, explained Toth.

An example of problematic social media use is excessive use of social media to the point that it impacts your ability to focus on school or work. Within friend groups, adolescents are likely to reinforce this problematic usage because it’s what they see their friends doing. Toth emphasizes that algorithms are becoming more sophisticated and increasing adolescents’ exposure to highly engaging, emotionally charged content, keeping them constantly active online.

“It's critical that we continue to examine the constant feedback loop between the demands of the peer group and adolescents’ own emotional reactions to their online activities, so that we can help adolescents develop better coping mechanisms and survival skills for online life,” she said.

Toth states that interventions focused on improving self-regulation of social media use and emotional regulation show promise in helping young people resist algorithmic content manipulation, and take back control of their social media usage.

Olga Kornienko. Photo provided.

Toth and her mentor Olga Kornienko, an associate professor of applied developmental psychology, are collaborating with a public school district in Arizona to understand sources the role of social relationships as sources of risk and resilience in the lives of contemporary youth.

Working out of George �鶹��Ƶ’s Peer Networks and Development Lab, Toth and Kornienko are collecting data from ninth-grade students through a series of questions about their social media use, culture, and psychological well-being.

They are collaborating with scholars from University of California, Davis, and Arizona State University, where Kornienko earned her PhD in 2010.

“Our study is not specific to any one social media platform. We were able to capture the broader digital experience that adolescents have online that reflects their everyday life. We hope it will remain relevant to Gen Alpha and beyond because even as individual platforms go in and out of use or new ones pop up, they're still going to have those core aspects of promoting constant engagement and peer feedback,” said Toth.

Toth said that while she didn’t have much experience with academic publishing before coming to graduate school at George �鶹��Ƶ, she hit the ground running. “We started [this research project] in my very first semester, so it was a very important part of my professional development working with Dr. Kornienko,” Toth said. “[She taught me about] so many different aspects of peer interaction and networks, social media, and how they're all interconnected.”

“It’s been such a pleasure seeing Caro grow as a researcher and academic,” said Kornienko. “She is addressing a very complex topic and contributing to more positive adolescent experiences.”

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College of Humanities and Social Sciences

Paid sick leave as disease prevention

Taylor Thomas — Tue, 03 Feb 2026 17:18:24 +0000

Paid sick leave as disease prevention Taylor Thomas Tue, 02/03/2026 - 12:18

In This Story

Suyoung Kwon

Key Takeaways:

New research from the �鶹��Ƶ College of Public Health suggests paid sick leave should be understood not only as an employee benefit, but as a preventive public health intervention.
Among home service workers who regularly enter private homes, such as home nurses and aides and appliance repair technicians, researchers found that stress and perceived infection risk rise before a confirmed diagnosis, when workers must decide whether to stay home sick or risk losing income.
When paid sick leave is unavailable, those pressures compound. The George �鶹��Ƶ study links lack of paid sick leave to higher job stress and lower job satisfaction, effects that ripple beyond individual workers into public health.

Photo via Freepik.

Home service workers—those who provide care, inspections, or repairs inside private homes—can often lack paid sick leave, making illness a direct financial risk. New research from �鶹��Ƶ College of Public Health suggests paid sick leave should be understood not only as an employee benefit, but as a preventive health intervention.

In the study led by assistant nursing professor Suyoung Kwon, paid sick leave was linked to lower perceived infection risk, reduced job stress, and higher job satisfaction. During the early months of COVID-19, the research team surveyed more than 1,600 home service workers in South Korea, including home nurses, childcare workers, appliance repair technicians, and gas meter inspectors.

Notably, workers reported that their highest level of stress was not after a confirmed diagnosis of COVID, but during the window when workers are deciding whether to show up sick or stay home.

“Paid sick leave can function much like personal protective equipment or vaccination for workers in high-contact roles,” said Kwon. “It reduces exposure before harm occurs.”

The study was published in the Journal of Occupational and Environmental Medicine.

Study findings

Workers with paid sick leave reported significantly lower perceived risk of COVID-19 exposure than those with unpaid leave or no leave at all.
Higher perceived risk of infection was associated with greater job stress, which in turn predicted lower job satisfaction. Paid sick leave interrupted that chain.
Workers with no access to sick leave experienced both direct and indirect drops in job satisfaction, suggesting compounded harms when workers lack any safety net.

Why it matters

Many home service workers enter multiple private homes each day. When paid sick leave is available only after a documented diagnosis (like a positive test for COVID-19), workers face a high-stakes choice during their most contagious period: lose income or risk exposing others.

The researchers contend that paid sick leave should be treated as a preventive mechanism that allows workers to stay home when symptoms first appear—before diagnosis or transmission. As policymakers revisit pandemic lessons and prepare for future public health emergencies, this study suggests that expanding paid sick leave is not only a worker protection, but a population-level prevention strategy.

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Brainpower inspires energy-efficient AI

Teresa Donnellan — Thu, 22 Jan 2026 16:57:20 +0000

Brainpower inspires energy-efficient AI Teresa Donnellan Thu, 01/22/2026 - 11:57

The key to energy-efficient artificial intelligence (AI) could lie in the human brain. �鶹��Ƶ researchers Maryam Parsa (PI) and Giorgio Ascoli (co-PI) are leading a Department of Energy-funded project to make AI dramatically more efficient by mimicking the brain’s computing strategies.

Maryam Parsa. Photo provided.

“AI today is powerful but extremely energy-hungry,” said Parsa, an assistant professor in George �鶹��Ƶ’s Department of Electrical and Computer Engineering who specializes in neuromorphic computing. “We want to understand why the human brain is so efficient and why AI is not—and then bridge that gap.”

Unlike traditional AI models that rely on continuous signals and massive data centers, the brain uses sparse, event-driven signals called spikes.

“Any one neuron only fires a spike here and there, but the timing of those spikes carries a lot of meaning,” explained Ascoli, a Distinguished University Professor in George �鶹��Ƶ’s Bioengineering Department specializing in neuroscience. “That’s why the brain can do so much with so little energy.”

To replicate this efficiency holistically, Parsa and Ascoli teamed up with Akhilesh Jaiswal at the University of Wisconsin–Madison and Pedram Khalili at Northwestern University. The team is building a full-stack neuromorphic computing system spanning devices, circuits, architectures, algorithms, and applications.

Each principal investigator is contributing specific expertise to the system:

Ascoli contributes neuroscience insights using Izhikevich models, mathematical frameworks that capture real neuron spiking dynamics, to inform system design.
Khalili develops spintronic devices, components that utilize magnetic materials to harness an electron's quantum properties of spin and charge for processing and storing data. By naturally exhibiting neuron-like temporal behavior, these devices are expected to reduce energy demands.
Jaiswal designs analog circuits to integrate these spintronic devices into functional architectures.
Parsa leads the project as well as neuromorphic learning algorithms and application-level integration, exploring diverse learning approaches to optimize speed, precision, resiliency, privacy, and energy savings.

Giorgio Ascoli. Photo provided.

“Izhikevich models allow neuromorphic systems to mimic the timing, diversity, and variability of real neurons, enabling more brain-like learning and efficiency,” Ascoli said. His extensive experience with these models, including simulating hippocampal networks, provides the biological foundation for the project.

“Instead of forcing existing devices to mimic brain behavior, we asked: What if we leverage new devices that naturally exhibit these dynamics?” Parsa said. This approach ensures innovations at the hardware level align with biologically inspired algorithms, creating a cohesive system rather than isolated solutions.

By introducing greater biological complexity and leveraging novel spintronic devices, the team hopes to reduce AI’s energy footprint while improving performance metrics such as accuracy and privacy. Early results are promising; even simple changes to spike distributions improved privacy without sacrificing accuracy.

The research also explores learning algorithms beyond the traditional. “Our goal is to see how different neuron models and learning rules interact and which combinations optimize for speed, precision, or energy savings,” Parsa said.

Ultimately, the project could transform AI by making it greener and more brain-like. “If we succeed, we’ll have systems that learn faster, consume less power, and operate more like the brain,” said Ascoli.

“This is not just about making AI more efficient,” said Parsa. “It’s about rethinking computing from the ground up."

In This Story

Maryam Parsa

Giorgio Ascoli

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Electrical and Computer Engineering

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Collaboration with Children’s National Hospital aims to reduce child mortality rates caused by Sickle Cell kidney failure

Katarina Benson — Tue, 20 Jan 2026 22:12:42 +0000

Collaboration with Children’s National Hospital aims to reduce child mortality rates caused by Sickle Cell kidney failure  Katarina Benson Tue, 01/20/2026 - 17:12

Children with Sickle Cell Disease (SCD), a genetic disorder affecting red blood cells, experience terrible bouts of pain when the blood cells contort into a sickle shape and block small blood vessels, hindering oxygen delivery to critical organs. Whenever these bouts occur in the kidneys, the filtration system of the kidneys can be irreversibly damaged. Kidney failure is associated with a 30% death rate in SCD patients. 

In partnership with Children’s National Hospital, scientists at �鶹��Ƶ have discovered a new way to detect early signs of kidney damage in childhood patients with SCD. A recently awarded two-year $200,000 grant from the Commonwealth Health Research Board will support a team led by George �鶹��Ƶ systems biologist and research fellow Marissa Howard; Children’s National Comprehensive Sickle Cell Disease Program Director Andrew Campbell, MD; and urinary biomarker expert Oliver John Semmes, of Old Dominion University.

Marissa Howard with collaborator Andrew Campbell, MD, at Children's National Hospital. Photo provided

The multi-institutional trial leverages unique proteomic expertise blended within the Children’s National SCD Program’s ongoing clinical study of the progress of SCD within pediatric patients over time and will transform kidney disease detection in these SCD patients.

According to the Sickle Cell Disease Association of America, SCD is a global health problem that affects an estimated 100,000 people in the United States, with about 2,000 babies annually being diagnosed with the disease at birth. With each SCD crisis, kidney damage accumulates, increasing the risk of total kidney failure mortality for SCD patients. Early detection of kidney damage, especially in childhood, allows for rapid treatment and prevention of long-term kidney damage-related effects.

“Our team will analyze the actual damaged mitochondria, the power houses of the cell, that are crippled by lack of oxygen that occurs during each sickle-cell crisis,” said Howard, who first reported this pathway and methodology to rescue mitochondrially damaged cells in her prior National Institutes of Health-supported and published research.  

“We measure these damaged mitochondria shed by the kidney in a few milliliters of urine so we can develop an early warning system for the onset of kidney damage,” said Howard, an expert in isolation of extracellular vesicles (EVs), small packages of cellular information shed into the urine, and the methods required to complete this analysis. “Our partners at Children’s National are known for identifying innovative approaches to treat SCD. This exciting collaboration is collectively motivated by our ultimate goal of reducing the high mortality rate caused by Sickle Cell kidney failure in children affected by this disease.”   

 Children’s National Comprehensive Sickle Cell Disease Program, among the largest in the country, follows more than 1,500 patients from birth through age 21 and is one of the few centers offering gene therapy treatments for SCD. The program has amassed a large base of patient samples, some of which will be analyzed in this project.  

Campbell, a physician-scientist caring for SCD patients at Children’s National, will oversee the recruitment, consenting, and collection of biological samples throughout the study's duration, as well as coordinate the delivery of these samples to George �鶹��Ƶ’s CAP/CLIA certified laboratory facilities for analysis. 

While current screening methods rely on established urine tests for albumin, this groundbreaking research will use advanced proteomic analysis to detect kidney injury at its earliest stages—before conventional tests show abnormalities.

“We've watched as silent kidney damage progresses year after year, often going undetected until significant harm has occurred,” said Campbell. “By identifying kidney disease earlier, we can intervene sooner with more effective disease-modifying therapies and curative treatments, potentially preventing or slowing progression to kidney failure.”

According to Campbell, this grant is more than funding—it's a commitment to SCD patients and their families, a collaboration to build the next generation of diagnostics, which can fundamentally change how to manage SCD and protect the health of children and young adults living with this debilitating disease.

“This partnership leverages our collective strengths to address a compelling global challenge that might have a multitude of applications,” shared Lance Liotta, MD, codirector of George �鶹��Ƶ’s Center for Proteomics and Molecular Medicine (CAPMM), who will certify that all work performed is under CAP/CLIA compliance for the data to be presented to the FDA for future clinical application.  

George �鶹��Ƶ is highly regarded for offering real world, hands-on learning experiences for its science students, and this medical research project is no exception. As part of George �鶹��Ƶ’s Office of Student Creative Activities and Research Undergraduate Research Scholars Program, Honors College student Naureen Zahra, who had interned the previous summer at the National Institutes of Health on a project focused on curing SCD with bone marrow transplants, performed portions of this experimental study over the summer.  

 Naureen Zahra interned at the National Institutes of Health. Photo provided

“One of my educational goals was to become an expert in performing key molecular biology techniques such as western blots to analyze the presence of proteins like PINK1, which plays a key role in regulating mitochondrial health,” said Zahra, a neuroscience major. The team worked to isolate EVs from Children’s National patient urine samples and analyze protein data. “Working with these scientists on actual patient samples connected me to real-world outcomes, expanding my sense of empathy for what these children are going through.” 

In addition to its potential to improve the lives of those affected by SCD, this research effort may also have even wider impact.   

“These findings could also serve as a new marker for mitochondrial damage for a variety of diseases including Parkinson’s, heart disease, and diabetic kidney disease,” said Howard. “In addition, urinary biomarkers could offer a predictive tool to guide SCD therapy management and therapy effectiveness more holistically.”

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partnerships

Research

Honors College

NASA-funded wildfire digital twin could save assets and lives with pollution prediction, burn forecasting

Sarah Holland — Thu, 15 Jan 2026 19:29:20 +0000

NASA-funded wildfire digital twin could save assets and lives with pollution prediction, burn forecasting Sarah Holland Thu, 01/15/2026 - 14:29

One year ago, the Palisades and Eaton Fires ravaged the coast of Southern California. Combined, the fires killed 28 people, destroyed more than 16,000 structures, and displaced tens of thousands of residents.

Wildfires are notoriously difficult to predict due to the multitude of factors that affect their growth, spread, and speed. That uncertainty makes it difficult for response teams to know who should be evacuated to avoid both active flames and hazardous air pollution. Mass evacuations require cooperation and communication across numerous departments, and getting those systems activated can take precious time that evacuees might not have if the fire is spreading at a rate of seven and a half football fields per minute as the Palisades fire did.

Chaowei "Phil" Yang. Photo by Office of University Branding.

One researcher at George �鶹��Ƶ is working on a solution. Chaowei “Phil” Yang, professor in the Geography and Geoinformation Science Department in the College of Science, has teamed up with researchers from California State University—Los Angeles (CSU-LA), NASA Jet Propulsion Laboratory, and the City of Los Angeles to develop a wildfire digital twin to understand fire evolution and air pollution impact.

“The goal is to develop an artificial intelligence (AI)-based system that can provide real-time, high-resolution simulation and forecasting of wildfire behavior and model the resulting air pollution and air quality impacts for better informed public health responses,” Yang said.

Air pollution is a critical metric here. “Inhaling wildfire smoke can cause serious and long-lasting damage to the breathing system,” said Yang. “We need to get those people impacted to safety as well as those in direct line of the spreading flames.”

Yang, who is the director of the Center for Intelligent Spatial Computing for Water/Energy Science, has worked on several projects using his knowledge of geospatial cyberinfrastructure and spatial cloud computing to study the impacts of major global events on air quality. When the wildfires hit southern California, Yang felt that his expertise and resources could be used to help mitigate future wildfire disasters.

Yang’s work ties directly to improving human health, well-being, and preparedness, as well as building a climate-resilient society, two key solutions in George �鶹��Ƶ’s Grand Challenge Initiative, the university's research focus to enable us to live in a world of our choosing.

“Looking at the damage increasing because of climate change, to both assets and people’s life and health, that really triggered us,” Yang said. “We really felt that we needed to do something to address it.”

The digital twin will integrate a range of data from a diverse set of sources—such as satellites, UAVs, ground observations, and citizen reports—in order to forecast and simulate the progress of a wildfire and the mitigation of potential interventions. Everything from fuel sources, moisture, load, and consumption to wind speeds and temperatures to real-time sensors for the fire are pulled into the system.

George �鶹��Ƶ’s high-performance computing (HPC) cluster is used to automate the data interpretation process, while machine learning modeling calibrates the data sets to increase accuracy. As more data sets become available, the model’s accuracy will increase.

“When we eventually provide information to firefighters and local agencies, we will give them a range of possibilities and a confidence level in those possibilities, such as ‘the fire will move in this direction with about 90% confidence, or 20% confidence,’” Yang explained. “That’s important for them when they’re trying to make these quick decisions about where to put fire fighters. It makes the information actionable instead of just data sets.”

While working on a bold solution to a grand challenge, the project is also an opportunity for students of all levels—from high school through post-doctoral—to get hands-on experience in cloud computing and digital transformation to address grand challenges.

Anusha Srirenganathan, PhD Earth Systems and Geoinformation Sciences ’25, joined the project during her time at George �鶹��Ƶ. “Working closely with researchers from different disciplines helped me grow as a collaborator, and my work on the project strengthened my abilities in large-scale satellite data processing, spatial cloud computing, and AI/ML modeling for environmental applications,” she said.

“We’re using these capabilities to cultivate the next generation workforce,” said Yang. “It’s eye-opening for the students and paves a path for them to become the future leaders of the nation.”

Securing a safer future for both the current and future generations is what drives Yang in his work. And he sees this work on a wildfire digital twin as only the beginning of what’s possible with the technology.

Yang said his team is already working on a Chesapeake Bay digital twin project to develop more accurate flood forecasting, and collaborating with Daniel Rothbart at the Carter School on a conflict resolution digital twin with an alert system. “There could be possibilities for this technology to help predict other natural disasters or forecasting conflict as it evolves,” Yang said. “It’s exciting to get the chance to utilize our knowledge and tools to address the grand challenges we’re facing today to hopefully save lives and reduce asset loss.”

“This experience has shown me that research can provide real value when it matters most,” Srirenganathan said.

Topics

Research

College of Science

Department of Geography and Geoinformation Science

GCI-Grand Challenge Initiative

Center for Intelligent Spatial Computing for Water/Energy Science

Artificial Intelligence

GCI-Climate

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Printing play: A path forward for children with prosthetics

Nathan Kahl — Thu, 15 Jan 2026 13:32:50 +0000

Printing play: A path forward for children with prosthetics Nathan Kahl Thu, 01/15/2026 - 08:32

In This Story

Quentin Sanders

A child who uses a lower-limb prosthesis faces unique challenges when it comes to activities most children take for granted, such as running and being active. Without access to a prosthetic device designed to support these movements, children may miss out not only on the simple joys of play, but also on critical opportunities for physical development during key stages of growth.

Quentin Sanders. Photo by Ron Aira/Office of University Branding

�鶹��Ƶ researcher Quentin Sanders is part of a collaborative research team working to make high-performance prosthetic limbs more affordable, accessible, and better tailored to the needs of active children. Sanders, along with Jonathon Schofield, an associate professor at the University of California, Davis, and Garrett Melenka, an associate professor at York University, received a three-year, $500K grant from the National Science Foundation to support the project, which began in September 2025.

Most standard foot or leg prostheses are built for basic walking, not for the kind of active movement that helps children develop strength, balance, and coordination, the benefits that come from running and jumping with friends.

“This limitation can be especially challenging for children,” said Sanders, who has a joint appointment in the Department of Bioengineering and the Department of Mechanical Engineering. “Kids want to run and play. When they can’t, they’re more likely to avoid physical activities, such as sports, or even stop using their prosthesis altogether.”

A 3D-printed running blade prototype, created using the technique Sanders and his colleagues are developing. Photo provided

Often seen in competitive events such as the Paralympic Games, running blades are curved, spring-like prosthetic feet made from carbon fiber that mimic the energy return of a biological foot. While these devices offer an alternative option to standard leg prostheses and can enable children to run, they are expensive and often inaccessible. A single running blade can cost several thousand to tens of thousands of dollars and is typically not covered by insurance because it is considered nonessential, rather than medically necessary.

Some charitable organizations help families access these devices, but demand far exceeds supply, and for growing children, one blade is rarely enough. “Kids grow quickly,” said Sanders. “As they get taller, they outgrow their prostheses, which means going through multiple blades over time.”

The research team has several goals, starting with identifying what children truly need from an activity-enabling prosthesis. The researchers are examining how motivation to be active, physical growth, and different types of movement influence prosthetic performance in everyday settings. “What do you use it for? What don’t you use it for? What do you wish you could do?” said Sanders.

The team is also analyzing how children move while using their current running blades, studying activities such as running, jumping, and changing direction to better understand the biomechanics and physical demands involved.

Finally, the team will take a close look at how today’s running blades perform under real-world demands. “We want to understand how stiff they are, how much load they can handle, and when they might fail,” said Sanders. “We can then combine that information with what kids tell us they want to do and use it to guide our 3D printing process—creating blades that are better suited for active play.”

Sanders and his collaborators are using an advanced additive manufacturing approach known as continuous-fiber 3D printing, in which carbon fibers are embedded within the printed plastic to reinforce the prosthetic structure. This method enables the creation of strong, lightweight devices that can be tailored to a child’s size, growth, and activity needs.

“By combining manufacturing, composite materials, prosthetics, design, and biomechanics, this project provides an innovative and holistic solution,” said Melenka.

The technique is already used to produce strong, lightweight components in the aerospace and automotive industries, but it has seen limited adoption in prosthetic design. This project represents one of the first efforts to apply it systematically to activity-enabling prostheses for children.

“What’s most exciting about this project is the collaboration across institutes, disciplines, and areas of expertise,” said Schofield. “We are bringing together researchers in movement mechanics, advanced materials manufacturing, and pediatric lower-limb prosthetic care. We are also incorporating the lived experience of children and families, along with clinicians who work with these patients every day.”

The work also has the potential for industry disruption, according to Sanders. “If we can show that a child’s physical measurements and movement needs can be translated into a digital model and then used to 3D print a customized prosthesis, existing prosthetic manufacturers would begin to integrate this approach into their production pipelines.”

For Sanders, the motivation goes beyond technical innovation. “I’ve always been interested in translating new technologies into real-world solutions,” he said. “Helping people with disabilities and impairments is why I got into this field in the first place.”

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mechanical engineering

3D Printing

Research

National Science Foundation

Fuse at �鶹��Ƶ Square

GCI-Health

George �鶹��Ƶ secures $1.5M to launch cutting-edge AI data center research lab

Martha Bushong — Thu, 15 Jan 2026 12:02:47 +0000

George �鶹��Ƶ secures $1.5M to launch cutting-edge AI data center research lab Martha Bushong Thu, 01/15/2026 - 07:02

�鶹��Ƶ is taking a major step toward advancing clean energy and digital infrastructure innovation with the launch of the Virginia AI Data Center Research Lab at �鶹��Ƶ Square in Arlington. This initiative is made possible through funding from the Virginia Clean Energy Innovation Bank and Virginia Energy. The $1.5 million grant establishes a first-of-its-kind research and workforce development initiative and positions the commonwealth as the national leader in grid-interactive, AI-driven digital infrastructure.

Liling Huang, lab director (center) cuts ribbon of the Virginia AI Data Center research lab. Other guests (from left) Micah Till, Andre Marshall, Dean Ken Ball, Glenn Davis, and Jennifer Andos. Photo by Ron Aira/Office of University Branding.

Why Virginia

Virginia sits at the center of the world’s digital infrastructure, hosting the largest concentration of data centers globally and supporting mission-critical cloud, AI, and national security operations. The rapid emergence of AI-driven workloads has dramatically increased power demand, grid complexity, and the need for a highly skilled workforce.

As AI workloads surge, Virginia utilities face unprecedented interconnection requests and a shortage of engineers and technicians skilled in AI computation, power systems, cooling, and clean energy integration.

“We are on the frontlines to see how data centers, and in particular AI use, are changing the power grid and the information available to those who rely on it—both as the owners of underlying infrastructure and end users ourselves,” said Micah Till, manager, electric transmission system protection engineering, Dominion Energy Virginia. “George �鶹��Ƶ is an important partner for us in that. The work here will allow stakeholders across our nation to proactively and collaboratively work toward the smooth adoption and integration of clean energy and AI data center technologies.”

Workforce Development and Research Goals

The Virginia AI Data Center Research Lab will provide hands-on training for George �鶹��Ƶ students, K–12 and community college programs, industry-led workforce development and certification, and internship and apprenticeship pipelines. It will promote joint research and development projects with utilities, data centers, and leading institutions. It will enable real-world experimentation on critical infrastructure challenges, including grid-responsive AI workload management, power quality and harmonics optimization, thermal-electrical interactions, small modular reactor and renewable energy integration, and data center resilience and energy efficiency.

The facility will generate Virginia's first publicly available open-source dataset capturing real data center electrical and thermal telemetry—data that is not commercially available—enabling statewide research and innovation that will benefit universities, utilities, and industry partners.

George �鶹��Ƶ professor Liling Huang speaks with Glenn Davis, director of the Virginina Department of Energy (right) and Andre Marshall, George �鶹��Ƶ's vice president for research, innovation, and economic development. Photo by Ron Aira/Office of University Branding.

Voices from Partners and Leadership

“Virginia Energy’s investment in George �鶹��Ƶ’s Virginia AI Data Center Research Lab underscores our commitment to pairing world-class digital infrastructure with smart, sustainable energy systems,” said Glenn Davis, the director of the Virginia Department of Energy. “By combining AI, advanced data center technology, and grid-interactive research in an open, university-based platform, this project helps ensure that Virginia continues to advance our clean energy and workforce goals. Virginia didn’t become the data center capital of the world by accident. We earned it. And labs like this one are how we keep earning it.”

“We are honored to receive this support from the Virginia Clean Energy Innovation Bank and Virginia Energy to launch the Virginia AI Data Center Research Lab,” said Ken Ball, dean of George �鶹��Ƶ’s College of Engineering and Computing. “This lab will give our students, faculty, and partners access to state-of-the-art AI and data center infrastructure, enabling cutting-edge research, hands-on training, and open-source data resources that will help secure George �鶹��Ƶ’s and Virginia’s positions as global leaders in digital infrastructure and energy innovation.”

Powering Artificial Intelligence

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Artificial Intelligence

Energy

Electrical Engineering

George �鶹��Ƶ team earns $2.1M to study seizure dynamics

Teresa Donnellan — Tue, 13 Jan 2026 15:06:52 +0000

George �鶹��Ƶ team earns $2.1M to study seizure dynamics Teresa Donnellan Tue, 01/13/2026 - 10:06

In This Story

John Cressman

Parag Chitnis

Remi Veneziano

The National Institutes of Health (NIH) awarded a �鶹��Ƶ research team $2.1 million to create a system that could revolutionize the study of seizures.

The project combines the expertise of Parag Chitnis and Remi Veneziano (Department of Bioengineering), and Rob Cressman (Department of Physics and Astronomy) to develop an imaging and sensing system for identifying areas of the brain with potassium ion dysregulation.

“Potassium is one of those ions that actually inhibits activation of cells. If there's a dysfunction in those ions, you get runaway signaling. A seizure is an example of that,” said Cressman, who has studied epilepsy since his postdoctoral work. “We’re trying to develop tools that help us understand that better.”

From left, Parag Chitnis, Rob Cressman, and Remi Veneziano. Photo by Evan Cantwell/Office of University Branding.

When the biocompatible nanosensors, made of DNA and light-sensitive dye, encounter a local change in potassium concentration, they change their shape resulting in a change in their near-infrared fluorescence signal.

“We want to detect even very small changes in potassium,” said Veneziano, whose work involves synthesizing new nanomaterials from DNA particles, proteins, and lipids. “The system we’ve built is modular, versatile, and highly sensitive.”

These signals can be mapped in real-time using a near-infrared imaging device to understand dynamic changes in ion concentration.

One of the most promising applications is image-guided surgical intervention for epilepsy. By pinpointing seizure foci through potassium mapping, surgeons may be able to perform precise tissue ablation, removing only the affected areas.

“If you can identify the focus and remove that part of the tissue, you can sometimes stop the seizures,” said Cressman.

The four-year NIH-funded project, previously funded by the National Science Foundation (NSF), builds on years of research supported by George �鶹��Ƶ’s Center for MedTech Innovation.

“This grant allows us to translate our NSF-funded feasibility work into real-world applications,” said Chitnis, who studies brain-body interactions using light and ultrasound. “We’re not just studying disease—we’re building tools that could guide future interventions.”

The toolkit will be tested across multiple platforms—from cell cultures to brain slices to intact mammalian models—allowing researchers to study bioelectrical activity at various scales.

The team also sees potential for assessing functional recovery after traumatic brain injury. Scar tissue can disrupt potassium regulation, and the nanosensors may help identify regions at risk of developing seizure foci. As the research progresses, the team hopes to open new pathways for understanding and treating complex brain disorders.

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Groundbreaking study reveals hidden complexity in human genetics

Teresa Donnellan — Mon, 12 Jan 2026 18:32:03 +0000

Groundbreaking study reveals hidden complexity in human genetics Teresa Donnellan Mon, 01/12/2026 - 13:32

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Amarda Shehu

Sometimes, in genetics, two wrongs do make a right. A research team recently showed that two harmful genetic variants, when occurring together in a gene, can restore function—proving a decades-old hypothesis originally proposed by Nobel laureate Francis Crick. Their study, published in the Proceedings of the National Academy of Sciences (PNAS), not only experimentally validated this theory but also introduced a powerful artificial intelligence (AI)-driven approach to genetic interpretation led by �鶹��Ƶ researchers.

The project began when Aimée Dudley, a geneticist at the Pacific Northwest Research Institute (PNRI), approached �鶹��Ƶ Chief AI Officer Amarda Shehu after following her lab’s work on frontier AI models for predicting the functional impact of genetic variation. That conversation sparked a collaboration that married PNRI’s experimental expertise with George �鶹��Ƶ’s computational innovation to discover some surprising ways variant combinations can shape human health.

The problem

Every year one in three Americans is diagnosed with a genetic disorder. Symptoms manifest in infancy for about 70% of individuals. Sadly, 35% die before the age of 5. Advancements in clinical genomics offer hope to better understand and possibly treat these disorders.

“High-throughput genomic screening has been a wonderful feat for humanity,” said Shehu, “but one of its side effects is that it has produced massive amounts of data, outpacing our ability to interpret what that data means for health and disease.”

Research in the Shehu lab has for years focused on building frontier AI models to advance genetic interpretation, but all data available link only isolated, single variants to measured functional activity. Because each person's genome contains billions of base pairs, with about five million variants existing between two individuals’ genomes, looking at one variant at a time rather than combinations of variants could only reveal so much.

“It looked like we had hit a wall,” Shehu said, “that is, until Dr. Dudley contacted my lab more than a year ago.”

On the left, a 3D landscape derived from variant–variant measurements shows distinct functional regions emerging from pairwise interactions. On the right, these regions map onto a multimeric protein structure, where variants in separate spatial zones can be sequestered into different active sites, allowing functional recovery. This visualization captures the structural logic underlying positive epistasis and illustrates how AI-enabled analysis links genetic variation to protein function, a key, groundbreaking result Dudley and Shehu's labs published in the Proceedings of the National Academy of Sciences. Image provided.

The proof

Dudley’s lab was convinced that the key was to account for variant combinations in a gene, also called epistasis. They measured functional effects of variant combinations in the DNA of a key enzyme, argininosuccinate lyase (ASL), a lack of which results in urea cycle disorder, a rare but devastating condition.

The researchers tested thousands of variant combinations that resulted in no enzyme activity when on their own and found that a significant portion of them had high levels of enzyme activity when in combination with each other. In other words, two defective variants, when combined, can recover function.

“This was the most puzzling thing that I could not believe when Dr. Dudley showed it to me. Sometimes in biology, zero plus zero equals 100%,” said Shehu.

Shehu said that Crick, who shared the Nobel Prize in Physiology or Medicine 1962 with James Dewey Watson and Maurice Wilkins for their discoveries concerning the molecular structure of DNA, had hypothesized this could happen.

“Crick had a fancy word for it—variant sequestration,” said Shehu, “But until Dr. Dudley, no one had demonstrated it.”

The progress

Once Dudley’s lab confirmed the phenomenon experimentally, George �鶹��Ƶ researchers turned to AI to see if it could predict similar effects across other genes. Using the ASL data from Dudley’s lab, George �鶹��Ƶ computer science PhD student Anowarul Kabir developed a machine learning model to predict the effects of variant combinations. Then, he applied the model to a structurally similar but evolutionarily distinct protein, fumarase (FH). The algorithm achieved 99.6 percent accuracy in predicting regained function within ASL and 91 percent accuracy in FH.

“The really cool thing about this,” said Shehu, “is that the model learned both sequence and structural patterns and was able to transfer its knowledge to another gene.”

This breakthrough suggests that with experimental data from a few genes, AI can help scale variant effect prediction to a broad set of genes. The PNAS publication estimates that as many as 4% of the genes in the human genome could have the same types of effects seen for ASL and FH.

The paradigm shift

This breakthrough marks a paradigm shift in clinical genomics for precision medicine. By considering variant combinations rather than isolated, single variants, clinicians can deliver faster, more accurate diagnoses and life-saving interventions for families facing rare diseases. They can also prioritize therapeutic treatments based on specific epistatic profiles of patients or clinical trial participants.

“Clinical genomics has been stuck in a rut for decades. We’ve shown that you need to look at combinations of variants to fully understand their impact,” said Shehu. “Our AI model expands coverage from one gene to another, accelerating interpretation and bringing us closer to true precision medicine.”

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Machine Learning in Health Care

Research

Artificial Intelligence

New Report Warns of Sharp Federal Job Losses in D.C. Region

Andrew J Schappert — Mon, 12 Jan 2026 15:23:31 +0000

New Report Warns of Sharp Federal Job Losses in D.C. Region Andrew J Schappert Mon, 01/12/2026 - 10:23

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Terry Clower

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A new report issued this week by the Stephen S. Fuller Institute at the Schar School of Policy and Government estimates the Washington, D.C., region’s federal workforce shrank by nearly 53,000 employees from December 2024 to November 2025—a contraction not seen in more than two decades.

The remaining federal workforce now stands at 327,100, the lowest level since late 2001, underscoring the scale and speed of the decline.

Compounding the job losses is the economic “knock-on effect in the form of household spending that those jobs support,” said Terry Clower, director of the Schar School’s Center for Regional Analysis at �鶹��Ƶ.

He estimated that in addition to the loss of 52,900 jobs effectively triggers another 20,000 job losses across the regional economy as spending falls.

Clower likened the moment as reminiscent of “changes that hit industries in the Midwest in the 1970s,” a comparison that highlights the potential for lasting economic disruption.

The Steven S. Fuller Institute’s Washington Economy Watch report can be found at this webpage.

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Groundbreaking study reveals hidden complexity in human genetics

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