Jie Wu, an engineering graduate student, was studying a type of striking white beetle found in Southeast Asia and attempting to figure out how to mimic its brilliant color when an unexpected discovery upended the experiment.

Jie and I had been hoping to identify naturally occurring whitening pigments that could be used in paper and paints. The beetle’s white exoskeleton is made from a compound called chitin, which is a type of carbohydrate – one that is also commonly found in crab and lobster shells.

Read the full article in The Conversation here: https://bit.ly/4uBteYr

“Peatlands are essential carbon stores, but as temperatures warm, this carbon is in danger of being released as carbon dioxide and methane,” says Kostka, who is also the associate chair for Research in the School of Biological Sciences and the director of Georgia Tech for Georgia’s Tomorrow. Understanding the ratio of carbon dioxide to methane is critical, he adds, because while both are greenhouse gasses, methane is significantly more potent.

Kostka is the corresponding author of a new study unearthing how and why peatlands are producing carbon dioxide and methane. 

The research, “Northern peatland microbial communities exhibit resistance to warming and acquire electron acceptors from soil organic matter,” was published this summer in Nature Communications, and was led by co-first authors Borja Aldeguer-Riquelme, a postdoctoral research associate in the Environmental Microbial Genomics Laboratory, and Katherine Duchesneau, a Ph.D. student in the School of Biological Sciences.

The study builds on a decade of research at the Oak Ridge National Lab’s Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment, a long-term research project in Minnesota that allows researchers to warm whole sections of wetland from tree top to bog bottom.

“Over the past 10 years, we’ve shown that warming in this large-scale climate experiment increases greenhouse gas production,” Kostka says. “But while warming makes the bog produce more methane, we still observe a lot more CO2 production than methane. In this paper, we take a critical step towards discovering why — and describing the mechanisms that determine which gases are released and in what amounts.”

Methane mystery

The subdued methane production in peatlands has been a long-standing mystery. In water-saturated wetlands, oxygen is scarce, but microbes still need to respire — a type of ‘breathing’ that allows them to produce energy for metabolic function. Without oxygen, microbes use nitrate, sulfate, or metals to respire — still releasing carbon dioxide in the process. However, if these ingredients aren’t present, microbes ‘breathe’ in a way that releases methane.

Since nitrate, sulfate, and metals are relatively rare in peatlands, methane production should be the most likely pathway, but surprisingly, observations show the opposite. “In both fieldwork and lab experiments, peatlands produce much more carbon dioxide than methane,” Kostka explains. “It’s puzzling because the soil conditions should help methane production dominate.”

To solve this mystery, the team leveraged a suite of cutting-edge genetic tools called “omics” —  metagenomics (studying DNA), metatranscriptomics (studying RNA), and metabolomics (a technique used to study the “leftovers” of metabolism), providing a detailed look under the hood of the microbial “engine” that cycles organic matter in wetlands. It also gave a new window into the diversity of soil microbes in wetlands: 80 percent of the organisms identified in the study were new at the genus level.

‘Omics’ innovations

Over the course of several years, the team collected samples from a peatland enclosed in an experimental chamber that was slowly warmed, then analyzed the samples using omics to see how they changed. Initially, they hypothesized that warming the soil would cause microbial communities to change quickly. “Microbes can evolve and grow rapidly,” Kostka says. “But that didn’t happen.”

The DNA-based methods showed that while the microbial communities stayed largely stable, the bog did release more greenhouse gasses as it warmed. To assess the metabolic potential of the microbes, Duchesneau and Aldeguer-Riquelme constructed microbial genomes, investigating how they were decomposing the organic matter in peatlands and cycling carbon.

“We found that microbial activity increases with warming, but the growth response of microbial communities lags behind these changes in physiological or metabolic activity,” Kostka says. He cautions that this doesn’t necessarily mean that wetland communities won’t change as climates warm — just that these shifts might come behind metabolic ones. 

A diversity of discoveries

And the methane? The team believes that microbes may be breaking down organic matter to access the key ingredients for producing carbon dioxide — nitrate, sulfate, and metals — though more research is currently underway to investigate this.

“Doing this type of integrated omics research in soil systems is still incredibly difficult,” Kostka says. The challenge is multifaceted: the research leverages years of experiments, long-term datasets, advanced laboratory techniques, and fieldwork innovations. 

At SPRUCE, experimental chambers are about 1,000 square feet. While it’s an impressive experimental setup, researchers still must be careful: “We need to take soil samples for many years, so if we take too many, there’d be no soil left!” Kostka explains. “Part of our research involves developing better, non-destructive sampling techniques.”

The other challenge lies in what makes these peatlands so unique: it’s very hard to detect small changes because of the sheer diversity of organisms present. “Every time we conduct this type of research, we learn more about these incredible systems,” he says. “There’s always something new.”

DOI: https://doi.org/10.1038/s41467-025-61664-7

Funding: The Office of Biological and Environmental Research, Terrestrial Ecosystem Science Program and Genomic Science programs, under the US Department of Energy (DOE); the Environmental Molecular Sciences Laboratory, a DOE Office of Science User Facility sponsored by the Biological and Environmental Research program. The SPRUCE experiment is funded by the Biological and Environmental Research program in the U.S. Department of Energy’s Office of Science.

School of Earth and Atmospheric Sciences Professor Yuhang Wang and his team studied the factors affecting SO₂ and sulfate concentrations during winter and summer in the “Rust Belt” — from New York through the Midwest — and the Southeast regions of the U.S. over two decades (2004 to 2023). Supported by the National Science Foundation and Georgia Tech’s Brook Byers Institute for Sustainable Systems, the team also developed an ensemble machine learning approach to project seasonal patterns until 2050. 

“Power plants, particularly those burning coal and oil, are a major source of SO₂ emissions in these regions,” says Wang, who co-authored, with Ph.D. students Fanghe Zhao and Shengjun Xi, the study recently published in Environmental Science & Technology Letters. 

Seasonal differences in atmospheric chemistry 

In the U.S., the chemistry in the atmosphere varies among the seasons. During summer, solar radiation from ample sunlight activates oxidant reactions that produce hydrogen peroxide (H₂O₂) in the atmosphere. The supply of H₂O₂ is determined by the amount of emitted air pollution, and once in the atmosphere, H₂O₂ can oxidize SO₂ quickly into sulfate aerosols in the aqueous phase. 

Sulfate aerosols from the oxidation of SO₂ contribute to the formation of particulate matter less than 2.5 micrometers in diameter (PM2.5). Particulate sulfate poses significant environmental and public health risks, including air pollution, acid rain, and circulatory and respiratory issues. 

“The supply of H₂O₂ in summer is eight times greater than in winter — a huge difference — which means sulfate concentrations are generally higher in summer and a reduction in SO₂ emissions leads to a proportional decrease in sulfate concentrations,” explains Wang. “When SO₂ emissions exceed the available supply of H₂O₂ in winter, the reduction in sulfate concentrations can be much smaller because of a ‘chemical damping’ effect that causes sulfate levels to decline more slowly than SO₂ emissions.” 

Narrowing the disparities between seasonal sulfate levels 

The study’s two-decade observations revealed distinct patterns in the reduction of SO₂ emissions and sulfate concentrations during winter and summer. 

While SO₂ emissions significantly decreased in both seasons­ over time — primarily from the Clean Air Act and more power plants transitioning from coal to natural gas — the reduction of sulfate concentrations initially showed large seasonal differences. However, over the past decade, the disparity between winter and summer sulfate levels narrowed as SO₂ emissions decreased.

According to Wang, the seasonal disparity of sulfate was caused by changing chemical regimes in winter over time. Although the lower supply of H₂O₂ remained stable in winter, SO₂ wintertime emissions were higher from 2004 to 2013, then dropped below the level of H₂O₂ after 2013 — reaching parity with the levels of reduced SO₂ emissions in the summer. 

“When you have this complexity of atmospheric chemistry, there is a non-linear effect in winter — as SO₂ emissions decreased, sulfate aerosol production efficiency increased until 2013, then flattened as of today. The reduction in sulfate aerosols initially lagged behind the decrease in SO₂ emissions but eventually caught up as a result of sustained air quality control efforts,” says Wang. “Conversely, there is a simple, linear effect in summer — the more SO₂ emissions, the more sulfate aerosols in the atmosphere — and if you reduce one, the other is reduced by the same proportion.”

Decades-long full impact 

From now until 2050, the researchers’ machine learning projections indicate a continuing decrease of winter and summer sulfate levels, which are currently around 20 percent, as SO₂ emission controls achieve comparable efficacy across the seasons. 

“We’re now seeing the full impact from the Clean Air Act,” concludes Wang, “and the nation’s sustained effort in pollution reduction is key to improving air quality and health outcomes.”

“It’s not just about creating jobs, it’s about filling them,” says Tom Kurfess, Regents’ Professor in mechanical engineering and executive director of the Georgia Tech Manufacturing Institute (GTMI). “To do that, we need to show students how exciting and innovative manufacturing can be. Manufacturing has really changed over the past few years. Today, going from an idea to a physical part is much easier to do. It is fun and exciting to bring ideas to life and to actually hold the results in your hands.”

GTMI is working to reignite student interest in the art and science of making through its new K–12 initiative: the Advanced Manufacturing Pathways (AMP) Program. Modeled after Georgia Tech’s Rural CS Initiative, AMP empowers schools with faculty expertise, cutting-edge equipment, and a hands-on curriculum to give students early exposure to the tools, technologies, and creativity behind modern manufacturing while building a pipeline of future talent ready to thrive in high-tech careers.

Funded by the Southwest Georgia Regional Commission (SWGRC), AMP is kicking off in three school districts this fall — Decatur County, Thomas County, and the city of Thomasville  — with plans to expand to additional schools in the spring of 2026. The program will start by engaging more than 200 students through hands-on learning, virtual instruction, and in-person lab experiences led by Georgia Tech researchers and faculty.

“Here in Southwest Georgia, we believe that opportunities like this are vital for integrated learning in schools and for growing our future workforce,” says Beka Shiver, economic development and transportation planner for SWGRC. “Workforce development and K-12 integration are at the heart of our Southwest Georgia Ecosystem Building Project, and we are so pleased to be able to provide funding for this program.”

The launch of the AMP Program is centered around Design, Build, Race, a course putting a modern spin on the classic pinewood derby. Students will use digital design, 3D printing, and machining to build and race custom cars, while also learning how to collect and analyze performance data to improve their designs and predict outcomes. The course blends engineering with data science, sparking curiosity and showing students how modern manufacturing is powered by both technical skills and smart data. 

“This program delivers real-world industry experience to students while strengthening the talent pipeline that drives innovation, competitiveness, and resilience in advanced manufacturing”, says Steven Ferguson, interim director of operations at GTMI and one of the project’s leaders. “After more than 20 years of driving education and workforce development innovation, I’m more energized than ever to help launch the AMP program to open doors for students and advance U.S. manufacturing leadership.”

Building the Blueprint

Before it evolved into the AMP Program, Design, Build, Race was a course developed by GTMI research engineer Kyle Saleeby in 2023. Originating in GTMI’s Advanced Manufacturing Pilot Facility (AMPF), the course was designed to introduce Morehouse and Georgia Tech students to the possibilities of modern manufacturing through digital design, 3D printing, machining, and competitive creativity.

“Even after the first week, it was powerful to watch students discover how exciting it is to design and manufacture a competition-ready car in a matter of hours,” said Saleeby. “That’s when I knew we were onto something special.”

Saleeby teamed up with Ferguson to transform the course into a broader initiative. The duo engaged colleagues from STEM@GTRI and secured funding from SWGRC to modify the curriculum and scale the course for a high school audience. 

“We are thrilled that we have been able to take the lessons learned during the development of the Rural Computer Science Initiative and expand opportunities for students in Southwest Georgia,” says Sean Mulvanity, a senior research associate in the Georgia Tech Research Institute. Mulvanity is one of the founders of the initiative and has been a key contributor to the AMP Program. “We hope this program can grow and expose students across the state to the field of advanced manufacturing.” 

Though granted by the SWGRC, funds for the program were provided by Georgia Artificial Intelligence in Manufacturing, a statewide initiative founded by GTMI and Georgia Tech’s Enterprise Innovation Institute to advance AI-driven manufacturing.

To bring AMP into classrooms, Southern Regional Technical College helped set up labs and provide technical support, ensuring schools were ready to launch. 

“At all levels, the community has rallied around this program,” says Saleeby. “Providing students with a unique experience learning advanced manufacturing technologies will open countless career opportunities. I cannot wait to see where they go.” 

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They’re in our TVs, refrigerators, and washing machines, helping us live comfortable lives. They also help us stay alive as part of the medical network, used in pacemakers, blood pressure monitors, and MRI machines, among other things. Also, our national economic and defense systems rely on them. Basically, semiconductors control and manage the flow of information in the machinery that keeps the world going.

And right now, at Georgia Tech, researchers are working to innovate chip technology to ensure that U.S. semiconductor development is globally competitive, reliable, sustainable, and resilient, today and in the future.

“If you look at semiconductors, or the whole area of computing, it spans across Georgia Tech — across many different schools and disciplines,” said Arijit Raychudhury, professor and Steve W. Chaddick Chair in the School of Electrical and Computer Engineering (ECE). “Starting with physics and chemistry, where we essentially learn how different types of materials will react, to materials science and engineering, to electrical engineering and computer engineering, to computer science.”

It's a diverse, multidisciplinary enterprise from bottom to top, Raychudhury noted. And there is still plenty of room at the bottom, as theoretical physicist Richard P. Feynman famously said more than 60 years ago, predicting that one day we’d be making things at the atomic level. We are. It’s a familiar realm to Victor Fung and his lab, where they are designing new materials for semiconductors from the ground up, atom by atom.

“We are interested in exploring how to translate the latest advances in AI and machine learning to aid in accelerating computational materials simulations and materials discovery,” said Fung, assistant professor in the School of Computational Science. “We’ve been developing methods which can accurately predict a wide range of materials’ properties, to greatly facilitate high-throughput materials screening.”

Fung’s lab is using AI to discover previously unstudied materials with the electronic properties to build into chips. This approach to creating “designer” semiconductors would be significantly faster and cover more of the materials space than current methods.

Improving the Landscape

Smaller, more efficient, and more powerful are all part of the constantly evolving landscape in semiconductor research and development. It’s a very expensive landscape. While many chips are about the size of a fingernail, they are among the most complex human-made objects on Earth. Just building a semiconductor fabrication factory costs billions of dollars.

For a chemical engineer like Michael Filler, that sounds like opportunity.

“Chemical engineers think about how we produce products on a massive scale,” said Filler, associate professor in the School of Chemical and Biomolecular Engineering and associate director of the Institute for Electronics and Nanotechnology (IEN).

Filler, whose research involves the growing of semiconductor components, like transistors, from seed particles, is aiming to help democratize the process of chip development, bringing down the cost substantially while maintaining performance. In a not too distant future, that could mean an individual at home printing a chip on a machine similar to a 3D printer.

“Imagine a laser printer that can literally spit out custom electronics in a matter of minutes,” Filler said. “We’re big believers in the individual’s ability to be creative and know what they want to build for their applications. Ultimately, we’re interested in giving makers and prototypers opportunities to customize electronics.”

He’s in the right place for the far-reaching research he has in mind, adding, “We are so blessed with great facilities at Georgia Tech. It would be hard to imagine working somewhere else, because very few places have the diversity and quality of tooling we have here.”

IEN, which facilitates much of the semiconductor research at Georgia Tech, is based in the Marcus Nanotechnology Building, with its state-of-the-art micro/nano fabrication facilities such as the shared cleanroom space and a laser machine lab for micromachining.

But it is the range of expertise and creativity among faculty and students who are making IEN and Georgia Tech a thought leader in semiconductor research. This is evidenced by Tech’s recent grant of $65.7 million from the Semiconductor Research Corporation and the Defense Research Projects Agency to launch two new interdisciplinary research centers.

Events like Georgia Tech Chip Day (May 2) and Nanowire Week, an international gathering happening in Atlanta in October, also speak to Tech’s growing influence in this area.

Answering the Call

The Covid-19 pandemic clarified just how difficult it can be to make more chips. A shortage of semiconductors affected the supply of phones, computers, and other commonly used items during the global shutdown. Increased demand, depleted reserves, and too few manufacturing plants and workers significantly crippled the supply chain.

“The high degree of geographic concentration in certain parts of the semiconductor supply chain has recently created a heightened risk of supply interruptions,” said Chip White, Schneider National Chair in Transportation and Logistics and professor in the H. Milton Stewart School of Industrial and Systems Engineering (ISyE). “Such interruptions and resulting wild fluctuations in semiconductor demand can threaten the nation’s public health, defense, and economic security.”

With that in mind, translational supply chain research is going on in several places on campus, White said, including the Supply Chain and Logistics Institute and the NSF AI Research Institute for Advances in Optimization. White and his colleagues are developing software platforms for stress testing manufacturing supply chains. The goal is to identify vulnerabilities and risk mitigation procedures to design and operate next generation supply chains for critical industries such as the semiconductor industry, to improve global competitiveness and strike a balance between market forces and national security.

In an effort to address and feed the next generation demand for chips, the Biden administration recently launched a massive effort to outcompete China in semiconductor manufacturing, offering $39 billion in funding incentives for companies seeking to build plants in the U.S.

Another related area of importance in the ongoing development of semiconductors is growing the workforce of the future, and that includes a new wave of researchers. This is a role that Jennifer Hasler takes seriously.

“I have a strong interest and belief in mentoring,” said Hasler, ECE professor and founder of the Integrated Computational Electronics lab at Georgia Tech. She’s proven, theoretically at least, that the technology already exists to build a silicon-based version of the human cerebral cortex (which would cost billions of dollars to design and build), but one of her favorite roles is working with new, young faculty.

“It’s a personal thing for me, but it’s one of the coolest things I’m involved in,” she said. “When they come to Georgia Tech, they see how big this place is, bigger than a company. I like to say to them, ‘Let’s calm down, take a breath, you’re good, so let’s go make some cool stuff. Let’s get some momentum going.’”

For Raychowdhury, director of the new Center for the Co-Design of Cognitive Systems (part of the JUMP 2.0 program), developing the skilled workforce of the future means answering the call of the nation.

“This is one of the largest ECE departments in the country, with many, many talented students,” he said. “And given the need and shortage of skilled professionals in this particular area, I think it’s critical for us to create that kind of pipeline.” Last year, ECE undergraduate students started taking a new, two-semester course, sponsored by Apple, in which they actually build microprocessors from scratch.

“This is completely new,” Raychowdhury said. “It’s expensive to offer this course, but we plan to keep doing it and we’re in conversations with other companies that want to invest in workforce development. So, in addition to doing fantastic research, we want to be sensitive to the needs of the country and a new generation.”

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The possible treatment, which could become the first non-drug, non-surgical, long-acting therapy for glaucoma, uses the injection of a natural and biodegradable material to create a viscous hydrogel — a water-absorbing crosslinked polymer structure — that opens an alternate pathway for excess fluid to leave the eye. 

“The holy grail for glaucoma is an efficient way to lower the pressure that doesn’t rely on the patient putting drops in their eyes every day, doesn’t require a complicated surgery, has minimal side effects, and has a good safety profile,” said Ross Ethier, professor and Georgia Research Alliance Lawrence L. Gellerstedt Jr. Eminent Scholar in Bioengineering in the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University. “I am excited about this technique, which could be a game-changer for the treatment of glaucoma.”

The research, which was supported by the National Eye Institute and the Georgia Research Alliance, was published Dec. 7 in the journal Advanced Science. The research was conducted in animals, and shows that the approach significantly lowered the intraocular pressure.

As many as 75 million people worldwide have glaucoma, which is the leading cause of irreversible blindness. Glaucoma damage is caused by excess pressure in the eye that injures the optic nerve. Current treatments attempt to reduce this intraocular pressure through the daily application of eyedrops, or through surgery or implantation of medical devices, but these treatments are often unsuccessful.

To provide an alternative, Ethier teamed up with Mark Prausnitz, professor and J. Erskine Love Jr. Chair in the School of Chemical and Biomolecular Engineering at Georgia Tech, to use a tiny hollow needle to inject a polymer preparation into a structure just below the surface of the eye called the suprachoroidal space (SCS). Inside the eye, the material chemically crosslinks to form the hydrogel, which holds open a channel in the SCS that allows aqueous humor from within the eye to drain out of the eye through the alternative pathway.

There are normally two pathways for the aqueous humor fluid to leave the eye. The dominant path is through a structure known as the trabecular meshwork, which is located at the front of the eye. The lesser pathway is through the SCS, which normally has only a very small gap. In glaucoma, the dominant pathway is blocked, so to lessen pressure, treatments are created to open the lesser pathway enough to let the aqueous humor flow out.

In this research, the hydrogel props open the SCS path. A hollow microneedle less than a millimeter long is used to inject a droplet (about 50 microliters) of the hydrogel-precursor material. That gel structure can keep the SCS pathway open for a period of months.

“We inject a viscous material and keep it at the site of the injection at the interface between the back of the eye and the front of the eye where the suprachoroidal space begins,” Prausnitz said. “By opening up that space, we tap a pathway that would not otherwise be utilized efficiently to remove liquid from the eye.”

The injection would take just a few minutes, and would involve a doctor making a small injection just below the surface of the eye in combination with numbing and cleaning the injection site. In the study, the researchers, including veterinary ophthalmologist and first author J. Jeremy Chae, did not observe significant inflammation resulting from the procedure.

The pressure reduction was sustained for four months. The researchers are now working to extend that time by modifying the polymer material — hyaluronic acid — with a goal of providing treatment benefits for at least six months. That would coincide with the office visit schedule of many patients.

“If we can get to a twice-a-year treatment, we would not disrupt the current clinical process,” Prausnitz said. “We believe the injection could be done as an office procedure during routine exams that the patients are already getting. Patients may not need to do anything to treat their glaucoma until their next office visit.”

Beyond extending the time between treatments, the researchers will need to demonstrate that the injection can be repeated without harming the eye. The procedure will also have to be tested in other animals before moving into human trials.

“The idea of having a ‘one-and-done’ treatment that lasts for six months would be particularly helpful for those whose access to healthcare is non-optimal,” Ethier said. “Having a long-acting therapy would have an additional advantage during times of pandemic or other disruption when access to healthcare is more difficult.”

This research was supported by a grant from the National Eye Institute (R01 EY025286) and by the Georgia Research Alliance. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies.

Mark Prausnitz serves as a consultant to companies, is a founding shareholder of companies, and is an inventor on patents licensed to companies developing microneedle-based products (Clearside Biomedical). These potential conflicts of interest have been disclosed and are being managed by Georgia Tech. J. Jeremy Chae, Jae Hwan Jung, Ethier, and Prausnitz are listed as co-inventors on an IP filing related to this study.

CITATION: J. Jeremy Chae, et al., “Drug-free, Non-surgical Reduction of Intraocular Pressure for Four Months After Suprachoroidal Injection of Hyaluronic Acid Hydrogel.” (Advanced Science, 2020) https://doi.org/10.1002/advs.202001908

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A research team led by Yuhang Hu, associate professor in the George W. Woodruff School of Mechanical Engineering and the School of Chemical and Biomolecular Engineering, has created a new material designed to do exactly that. In a new study published in Advanced Materials, Hu and her collaborators describe a groundbreaking class of “living” polymers that can grow, shrink, heal, and even regenerate long after fabrication.

Their work combines advances in chemistry, mechanics, and materials design into a polymer platform that could reshape how engineered products are built, maintained, and recycled.

Read the full story on the George W. Woodruff School of Mechanical Engineering website.

The demand for electricity to power AI data centers is skyrocketing, placing immense pressure on traditional energy sources.  

To help find answers, researchers at the Institute for Neuroscience, Neurotechnology, and Society (INNS) are using math, data, and AI to unlock the secrets of thought. Together they are helping turn the brain’s raw electrical “noise” into real insights about how people think, move, and perceive the world.

Fair warning: Prepare your neurons for the complexity of this brain research ahead.

Building AI Like a Brain

What if artificial neurons in AI programs were arranged as they are in the brain?

AI programs would then help us understand why the brain is organized the way it is. This neuro-AI synthesis would also work faster, use less energy, and be easier to interpret. Creating such systems is the goal of Apurva Ratan Murty, an assistant professor of Psychology who is creating topographic AI models like the one above of three domains — vision, audition, and language inspired by the brain. In the near future, he predicts doctors might be able to use these patterns to predict the effects of brain lesions and other disorders. “We’re not there yet,” he says. “But our work brings us significantly closer to that future than ever before.”

Computing Thought and Movement

How cats walk keeps Chethan Pandarinath on his toes. This biomedical engineer uses sensors to analyze how two sets of feline leg muscles — flexors and extensors — are controlled by the spinal cord. Understanding how that happens could help patients partially paralyzed from spinal cord injuries, strokes, or progressive neuro-degenerative diseases get back on their feet again. “My lab is using AI tools that allow us to turn complex spinal cord activity data into something we can interpret. It tells us there’s a simple underlying structure behind the complex activity patterns,” says the associate professor.

Revealing the Brain’s Spike Patterns

“The brain is like a symphony conductor,” says Simon Sponberg. “Individual instruments have some independent control, but most of the music comes from the brain’s precise coordination of notes among the different players in the body.” This physics professor studies the fantastically fast-beating wings of the hummingbird-sized hawk moth (Manduca sexta). Its agile flight movement comes as a result of spikes in electrical activity in 10 muscles. Sponberg found something that surprised him — the brain focuses less on creating the number of spikes than in orchestrating their precise patterns over time. To Sponberg, every millisecond matters. “We are just beginning to understand how the nervous system first acquires precisely timed spiking patterns during development,” he says.

Predicting Decisions Through Statistics

Put a mouse in a maze with food far away, and it will learn to find it. But life for mice — and people — isn’t so simple. Sometimes they want to explore, only want water, or just want to go home. What’s more, animals make decisions based on their history, not just on how they feel at the moment. To dig deeper into the decision-making process, Anqi Wu, an assistant professor in the School of Computational Science and Engineering, is giving mice more options. By using a new computational framework called SWIRL (Switching Inverse Reinforcement Learning), her findings have outperformed models that fail to take historical behavior into account. “We’re seeking to understand not only animal behavior but also human behavior to gain insight into the human decision-making process over a long period of time,” she says.

Modeling the Mind’s Wiring With Math

Connectivity shapes cognition in the cerebral cortex, a layered structure in the brain. The visual cortex, in particular, processes visual data from the retina relayed through the Lateral Geniculate Nucleus (LGN) in the thalamus, and directs it to the correct cognitive domain in the brain. How it does this is the mystery that computational neuroscientist Hannah Choi wants to solve. “The big question I’m interested in is how network connectivity patterns in the architecture of the LGN are related to computations,” says this assistant math professor. To find answers, she shows mice repeated image patterns such as flower-cat-dog-house and then disrupts the pattern. The goal? To grasp how the thalamus’s nonlinear dynamical system works. If scientists and doctors better understand how brain regions are wired together, such knowledge could lead to better disease treatment.

This story was originally published through the Georgia Tech Alumni Magazine. Read the original publication here.

From fighter jets to medical devices, today’s most advanced machines depend on parts as intricate as their missions. These components aren’t just geometrically complex — they’re made from specialized metals engineered to withstand extreme heat, friction, and wear. But that strength comes with a challenge. How do you shape metals tough enough to survive the heat of a jet engine? 

That’s one of the main findings from a study by AI Caring on what older adults expect from explainable AI (XAI).

AI Caring is one of three AI Institutions led by Georgia Tech and funded by the National Science Foundation (NSF). The institution supports AI research that benefits older adults and their caregivers.

Niharika Mathur, a Ph.D. candidate in the School of Interactive Computing, was the lead author of a paper based on the study. The paper will be presented in April at the 2026 ACM Conference on Human Factors in Computing Systems (CHI) in Barcelona.

Mathur worked with the Cognitive Empowerment Program at Emory University to interview 23 older adults who live alone and use voice-activated AI assistants like Amazon’s Alexa and Google Home.

Many of them told her they feel excluded from the design of these products.

“The assumption is that all people want interactions the same way and across all kinds of situations, but that isn’t true,” Mathur said. “How older people use AI and what they want from it are different from what younger people prefer.”

One example she gave is that young people tend to be informal when talking with AI. Older people, on the other hand, talk to the agent like they would a person.

“If Older adults are talking to their family members about Alexa, they usually refer to Alexa as ‘she’ instead of ‘it,’” Mathur said. “They tend to humanize these systems a lot more than young people.”

Good Explanations

The study evaluated AI explanations that drew information from four sources of data:

• User history (past conversations with the agent)
• Environmental data (indoor temperature or the weather forecast)
• Activity data (how much time a user spends in different areas of the home)
• Internal reasoning (mathematical probabilities and likely outcomes)

Mathur said older users trust the agent more when it bases its explanations on data from the first three sources. However, internal reasoning creates skepticism.

Internal reasoning means the AI doesn’t have enough data from the other sources to give an explanation. It provides a percentage to reflect its confidence based on what it knows.

“The overwhelming response was negative toward confidence scores,” Mathur said. “If the AI says it’s 92% confident, older adults want to know what that’s based on.”

This is another example that Mathur said points to generational preferences.

“There’s a lot of explainable AI research that shows younger people like to see numbers in explanations, and they also tend to rely too much on explanations that contain numerical confidence. Older adults are the opposite. It makes them trust it less.”

Knowing the Context

Mathur said that AI agents interacting with older adults should serve a dual purpose. They should provide users with companionship and support independence while reducing the caretaking burden often placed on family members. 

Some studies have shown that engineers have tended to favor caretakers in the design of these tools. They prioritize daily tasks and routines, leaving some older adults to feel like they are merely a box to be checked.

She discovered that in urgent situations, older users prefer the AI to be straightforward, while in casual settings, they desire more conversation.

“How people interact with technological systems is grounded in what the stakes of the situation are,” she said. “If it had anything to do with their immediate sense of safety, they did not want conversational elaboration. They want the AI to be very direct and factual.”

Not Just Checking Boxes

Mathur said AI agents that interact with older adults are ideally constructed with a dual purpose. They should provide companionship and autonomy for the users while alleviating the burden of caretaking that is often placed on their family members. 

Some studies have shown that engineers have strayed toward favoring caretakers in the design of these tools. They prioritize daily tasks and routines, leaving some older adults to feel like they are a box to be checked.

“They’re not being thought of as consumers,” Mathur said. “A lot of products are being made for them but not with them.”

She also said psychological well-being is one of the most important outcomes these tools should produce. 

Showing older adults that they are listened to can significantly help in gaining their trust. Some interviewees told Mathur they want agents who are deliberate about understanding their preferences and don’t dismiss their questions.

Meeting these needs reduces the likelihood of protesting and creating conflict with family members.

“It highlights just how important well-designed explanations are,” she said. “We must go beyond a transparency checklist.”

Georgia Tech researchers are making AI easier to understand through their work on Transformer Explainer. The free, online tool shows non-experts how ChatGPT, Claude, and other large language models (LLMs) process language. 

Transformer Explainer is easy to use and runs on any web browser. It quickly went viral after its debut, reaching 150,000 users in its first three months. More than 563,000 people worldwide have used the tool so far.

Global interest in Transformer Explainer continues when the team presents the tool at the 2026 Conference on Human Factors in Computing Systems (CHI 2026). CHI, the world’s most prestigious conference on human-computer interaction, will take place in Barcelona, April 13-17.

[Related: GT @ CHI 2026]

“There are moments when LLMs can seem almost like a person with their own will and personality, and that misperception has real consequences. For example, there have been cases where teenagers have made poor decisions based on conversations with LLMs,” said Ph.D. student Aeree Cho.

“Understanding that an LLM is fundamentally a model that predicts the probability distribution of the next token helps users avoid taking its outputs as absolute. What you put in shapes what comes out, and that understanding helps people engage with AI more carefully and critically.”

A transformer is a neural network architecture that changes data input sequence into an output. Text, audio, and images are forms of processed data, which is why transformers are common in generative AI models. They do this by learning context and tracking mathematical relationships between sequence components.

Transformer Explainer demystifies how transformers work. The platform uses visualization and interaction to show, step by step, how text flows through a model and produces predictions.

Using this approach, Transformer Explainer impacts the AI landscape in four main ways:

• It counters hype and misconceptions surrounding AI by showing how transformers work.
• It improves AI literacy among users by removing technical barriers and lowering the entry for learning about AI.
• It expands AI education by helping instructors teach AI mechanisms without extensive setup or computing resources.
• It influences future development of AI tools and educational techniques by providing a blueprint for interpretable AI systems.

“When I first learned about transformers, I felt overwhelmed. A transformer model has many parts, each with its own complex math. Existing resources typically present all this information at once, making it difficult to see how everything fits together,” said Grace Kim, a dual B.S./M.S. computer science student. 

“By leveraging interactive visualization, we use levels of abstraction to first show the big picture of the entire model. Then users click into individual parts to reveal the underlying details and math. This way, Transformer Explainer makes learning far less intimidating.”

Many users don’t know what transformers are or how they work. The Georgia Tech team found that people often misunderstand AI. Some label AI with human-like characteristics, such as creativity. Others even describe it as working like magic.

Furthermore, barriers make it hard for students interested in transformers to start learning. Tutorials tend to be too technical and overwhelm beginners with math and code. While visualization tools exist, these often target more advanced AI experts.

Transformer Explainer overcomes these obstacles through its interactive, user-focused platform. It runs a familiar GPT model directly in any web browser, requiring no installation or special hardware. 

Users can enter their own text and watch the model predict the next word in real time. Sankey-style diagrams show how information moves through embeddings, attention heads, and transformer blocks.

The platform also lets users switch between high-level concepts and detailed math. By adjusting temperature settings, users can see how randomness affects predictions. This reveals how probabilities drive AI outputs, rather than creativity.

“Millions of people around the world interact with transformer-driven AI. We believe that it is crucial to bridge the gap between day-to-day user experience and the models' technical reality, ensuring these tools are not misinterpreted as human-like or seen as sentient,” said Ph.D. student Alex Karpekov. 

“Explaining the architecture helps users recognize that language generated by models is a product of computation, leading to a more grounded engagement with the technology.” 

Cho, Karpekov, and Kim led the development of Transformer Explainer. Ph.D. students Alec Helbling, Seongmin Lee, Ben Hoover, and alumni Zijie (Jay) Wang (Ph.D. ML-CSE 2024) and Minsuk Kahng (Ph.D. CS-CSE 2019) assisted on the project. 

Professor Polo Chau supervised the group and their work. His lab focuses on data science, human-centered AI, and visualization for social good.

Acceptance at CHI 2026 stems from the team winning the best poster award at the 2024 IEEE Visualization Conference. This recognition from one of the top venues in visualization research highlights Transformer Explainer’s effectiveness in teaching how transformers work.

“Transformer Explainer has reached over half a million learners worldwide,” said Chau, a faculty member in the School of Computational Science and Engineering. 

“I'm thrilled to see it extend Georgia Tech's mission of expanding access to higher education, now to anyone with a web browser.”

Georgia Tech researchers have created a new AI model for decision-focused learning (DFL), called Diffusion-DFL. Recent tests showed it makes more accurate decisions than current approaches.

Along with optimizing industrial output, Diffusion-DFL lowers costs and reduces risk. Experiments also showed it performs across different fields. 

Diffusion-DFL doesn’t just surpass current methods; it also predicts more accurately as problem sizes grow. The model requires less computing power despite these high-performance marks, making it more accessible to smaller enterprises.

Diffusion-DFL runs on diffusion models, the same technology that powers DALL-E and other AI image generators. It is the first DFL framework based on diffusion models.

“Anyone who makes high-stakes decisions under uncertainty, including supply chain managers, energy operators, and financial planners, benefits from Diffusion-DFL,” said Zihao Zhao, a Georgia Tech Ph.D. student who led the project. 

“Instead of optimizing around a single forecast, the model evaluates many possible scenarios, so decisions account for real-world risk and become more robust.”

[Related: GT @ ICLR 2026]

To test Diffusion-DFL, the team ran experiments based on real-world settings, including:

• Factory manufacturing to meet product demand
• Power grid scheduling to meet energy demand
• Stock market portfolio optimization

In each case, Diffusion-DFL made more accurate decisions than current methods. It also performed better as problems became larger and more complex. These results confirm the model’s ability to make important decisions in real-world scenarios with noisy data and uncertainty.

The experiments also show that Diffusion-DFL is practical, not just accurate. Training diffusion models is expensive, so the team developed a way to reduce memory use. This cut training costs by more than 99.7%. As a result, Diffusion-DFL can reach more researchers and practitioners.

“Our score-function estimator cuts GPU memory from over 60 gigabytes to 0.13 with almost no loss in decision quality, reducing the requirement for massive computing resources,” Zhao said. “I hope this expands Diffusion-DFL into other domains, like healthcare, where decisions must be made quickly under complex uncertainty."

Beyond decision-making applications, Diffusion-DFL marks a shift in DFL techniques and in the broader use of generative AI models. 

In supply chain management, planners estimate future demand before deciding how much product to stock. In this DFL problem, engineers align ML models with predetermined decision objectives, like minimizing risk or reducing costs. 

One flaw of DFL methods is that they optimize around a single, deterministic prediction in an uncertain future.

Diffusion-DFL takes a different approach. Instead of making a single guess, it determines a range of possible outcomes. This leads to decisions based on many likely scenarios, rather than on a single assumed future.

To do this, the framework uses diffusion models. These generative AI models create high-quality data from images, text, and audio. 

The forward diffusion process involves adding noise to data until it becomes pure noise. Models trained via forward diffusion can reverse diffusion. This means they can start with noisy data and then produce meaningful insights from training examples. 

Real-world data is often noisy and uncertain. Traditional DFL methods struggle in these conditions, but diffusion models are designed to handle them.

Because of this, Diffusion-DFL can explore many possible outcomes and choose better actions. Like image-generation AI, the model works well with complex data from different sources. This enables its use across different industries.

“Diffusion models have achieved significant success in generative AI and image synthesis, but our work shows their potential extends far beyond that,” said Kai Wang, an assistant professor in the School of Computational Science and Engineering (CSE).

“What makes Diffusion-DFL unique is that the specific downstream application guides how the model learns to handle uncertainty.

“Whether we are scheduling energy for power grids, balancing risk in financial portfolios, or developing early warning systems in healthcare, we can explicitly train these highly expressive models to navigate the unique complexities of each domain.”

Zhao and Wang collaborated with Caltech Ph.D. candidate Christopher Yeh and Harvard University postdoctoral fellow Lingkai Kong on Diffusion-DFL. Kong earned his Ph.D. in CSE from Georgia Tech in 2024.

Wang will present Diffusion-DFL on behalf of the group at the upcoming International Conference on Learning Representations (ICLR 2026). Occurring April 23-27 in Rio de Janeiro, ICLR is one of the world’s most prestigious conferences dedicated to artificial intelligence research.

“ICLR is the perfect stage for Diffusion-DFL because it brings together the exact community that needs to see the bridge between generative modeling and high-stakes decision-making for real-world applications,” Wang said.

“Presenting Diffusion-DFL allows us to challenge the traditional training framework of diffusion models. It’s about sparking a broader conversation on how we can align the training objectives of generative AI directly with actual, downstream decision-making needs.”

In December, The Conversation hosted a webinar on AI’s revolutionary role in drug discovery and development.

Science and technology editor Eric Smalley interviewed Jeffrey Skolnick, eminent scholar in computational systems biology at Georgia Institute of Technology, and Benjamin P. Brown, assistant professor of pharmacology at Vanderbilt University.

Skolnick has developed AI-based approaches to predict protein structure and function that may help with drug discovery and finding off-label uses of existing drugs. Brown’s lab works on creating new computer models that make drug discovery faster and more reliable. Below is a condensed and edited version of the interview.

Let’s start with the big picture. How is AI changing biomedical research and drug discovery, and what is the potential we are talking about?

Skolnick: The upside, potentially, is very large. One of the frustrating things about drug discovery is that, in spite of the fact that the people doing it are extraordinarily intelligent and have done an extraordinarily good job, the success rate is very low. About 1 in 5 drugs will have negative health effects that outweigh its benefits. Of the ones that pass, roughly half don’t work.

In drug development, there are several key issues: Can you predict which target is driving a particular disease? Once this target is identified, how can you guarantee the drug is going to work and isn’t simultaneously going to kill you?

These are outstanding problems in drug discovery in which AI can play an important, though not 100% guaranteed, role. Unlike us, AI can look at basically all available knowledge. On a good day it makes strong and true connections called “insights,” and on a bad day it does what is called “hallucinating” and sees things that are weak and probably false.

At the end of the day, many diseases do not have a cure. Most diseases are maintained, such as high cholesterol or autoimmune conditions. A treatment for cancer might buy you five years, and now you’re in Stage 4 and you’ve exhausted all the standard care drugs. AI can play a role to suggest alternatives where there are none.

Let’s give some basic definitions here. When we use the word drug, we’re talking about a wide range of therapies. Can you explain the range – we’ve got small molecule drugs, biologics, gene therapies, cell therapies.

Brown: We have fairly large molecules in our bodies called proteins. They are like machines that carry out specific functions and interact with one another. Oftentimes, when we’re trying to treat disease, we’re trying to alter functions of specific proteins. Many drugs, like aspirin and Tylenol, are small molecules that can fit into a protein and change its function. Fundamentally, drugs don’t have to just interact with proteins, but this is a major way in which our current repertoire of medications work.

There are also proteins that act like drugs, such as antibodies. When you receive a vaccine for a virus, your body is basically given instructions on how to develop antibodies. These antibodies will target some part of that virus. Your body is creating these big molecules, much bigger than aspirin, to go and interact with foreign proteins in a different way. Gene therapy is a larger step beyond that.

So these modalities – molecule, protein, antibody or gene – are very different types of molecules. They have different scales and rules, so the way you approach designing and discovering them various widely.

Can you briefly explain artificial neural networks, and what the “deep” in deep learning means?

Skolnick: AlphaFold, developed by DeepMind, involved understanding how neural networks worked. They built a network with a lot of inputs, which are stimuli, and outputs with different weights, similar to how your brain actually works. These simple connections, or neurons, have reinforcement learning.

They also created sophisticated neural networks, such as transformers, which do specific things like a special-purpose tool that can learn, and they added a mechanism called “attention,” which amplifies critical details. Super neural networks with transformers is what we call deep learning. These now have literally billions, if not trillions, of parameters.

Essentially, these machines can learn higher order correlations between events, meaning the patterns of conditional interactions that depend on the properties of multiple things simultaneously. In these higher order correlations, AI has the potential to see previously unknown things that are embedded in petabytes (a unit of data equivalent to half of the contents of all U.S. academic research libraries of biological data.

AlphaFold, which predicts three-dimensional, bioactive forms of a protein, has millions of sequences and a couple of hundred thousand structures. It can tell you, based on a particular pattern, what small molecule to design that sticks to a protein to induce some kind of structural shift.

How is this technology being used in biomedical research to understand molecular dynamics or, essentially, the biological processes involved in health and disease?

Brown: In 2013, there was a Nobel Prize for molecular dynamics simulations, computational tools that help you understand the motions of molecules as they move according to physics. There’s a huge body of scientific research built around those ideas.

AI and deep learning are large right now, but it’s worth mentioning that for the last decade and a half, people have been using much smaller machine learning algorithms to help design drugs. A lot of the ideas, such as [using machine learning for virtual screening], are not new and have been in practice for a while.

With AlphaFold’s technologies to help people design proteins and predict their structure, we’ve changed how we think about a lot of these problems. We have this new repertoire of approaches to build ideas around and to start thinking about drug discovery.

From 20 years ago to now, what has today’s AI technology done in terms of scale of change in this process?

Skolnick: A lot of diseases, like cancers, are caused by a collection of malfunctioning proteins. AI now allows us to start to think conceptually about how these diseases are organized and related to each other.

Diseases tend to co-occur. For example, if you have hyperthyroidism, you’re very likely to develop Alzheimer’s. Kind of weird, right? We can look at pieces, but AI can look at all the information, integrate the collective behavior and then identify common drivers. This allows you to construct disease interrelationships which offer the possibility of broad spectrum treatments that could treat whole collections of diseases rather than narrow-spectrum treatments.

Relatedly, AI also can help us understand disease trajectories. Diseases that tend to co-occur often present themselves consecutively. You have disease 1, it gives you disease 2, then gives you disease 3. This suggests that if you go back to the root with disease 1, you may be able to stop a whole bunch of stuff. You can’t analyze millions of trajectories and millions of data without a tool, so you couldn’t do this before.

This holds a lot of promise, but one also must be careful not to overpromise. It will help, it will accelerate, but it is not a substitute yet for real experiments, real clinical validation and trials.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

“A small vial of genetically engineered cells can contain multiple millions of dollars’ worth of intellectual property and require several years of work to develop,” said Corey Wilson, a professor in Georgia Tech’s School of Chemical and Biomolecular Engineering (ChBE). “Accordingly, the protection of high-value engineered cell lines has become critically important to the biotechnology industry.”

Wilson and his research team have published their findings in Science Advances demonstrating the effectiveness of their new biological security technology, known as GeneLock™, in protecting high-value engineered cell lines.

GeneLock is a cybersecurity-inspired technology that protects valuable genetic material directly at the DNA level. To demonstrate its strength, Wilson’s team conducted what they describe as a first-of-its-kind biohackathon, detailed in the new paper, to simulate unauthorized access. 

“GeneLock greatly improves our ability to protect high-value engineered cell lines by expanding security from the lab environment to the genetic level,” Wilson said.

Economic Impact

What are the stakes? Estimates place the global market for high-value genetic materials at more than $1.5 trillion, projected to reach $8 trillion by 2035. The use of these materials ranges from advanced medicines and proprietary research enzymes to specialty chemicals and sustainable materials.

Currently, the protection of high-value cell lines depends on physical safeguards such as restricted lab access and secure facilities, Wilson explained.

“The key weakness of physical security measures is once circumvented, there are typically no measures in place to protect valuable cells from theft, abuse, or unauthorized use,” Wilson said. 

“Once a sample leaves the building, the DNA it carries typically remains fully functional. This is like placing an unlocked cellphone in a desk drawer. Anyone who gains access to the drawer can view sensitive content on the phone­­­­­­­—or in this case will have full access to the valuable cell line.”

Genetic Passcode Protection

The GeneLock biological security technology developed by Wilson and his team places a passcode on engineered cells, akin to those used on ATM machines and protected cellphones.

Instead of leaving a valuable gene in readable form, the team scrambles the DNA sequence of interest. The scrambled genetic asset remains in a nonfunctional state unless the living cell where it resides receives the correct sequence of chemical inputs. Those inputs act as a molecular passcode.

“Only the right combination, delivered in the right order, rearranges the DNA into a working form,” Wilson said.

Biohackathon Security Test

To evaluate the technology, the researchers organized a blue team and a red team in what they describe as an ethical biohackathon. The blue team designed the encrypted DNA sequence, while the red team was challenged to discover the correct chemical passcode through experimentation in a gray box exercise, meaning the red team had partial knowledge of the system but did not have access to the internal designs. 

“This approach for testing security strength is commonly used in cybersecurity,” Wilson explained. 

The blue team engineered the system inside Escherichia coli, or E. coli, a bacterium widely used in biotechnology. The protected asset was a fluorescent protein gene selected as a measurable stand-in for commercially valuable targets. When the correct chemical sequence was applied, the fluorescence turned on. Without the correct passcode, the gene remained scrambled and the cells could not fluoresce green. 

“In practice, most DNA sequences produce valuable proteins or chemicals that are essentially invisible to the human eye, requiring specialized devices or experiments to observe,” Wilson said. “If the biohackathon were conducted with a standard commercially valuable target, the penetration testing would have taken more than 10 times longer to complete, years instead of months.”

The biohackathon results showed a dramatic reduction in risk. GeneLock reduced the probability of unlocking the genetic asset by random search to about 1 in 85,000 (a 0.001% chance), assuming the unauthorized user had access to the required chemical inputs.

Without access to those inputs, “the likelihood of success by chance becomes effectively negligible,” said Dowan Kim (Georgia Tech PhD 2024), co-lead author of the study.

Commercial Uses and What’s Next 

Although the researchers used a non-commercial fluorescent protein as a test case, the implications extend much further. Many biotechnology companies rely on proprietary engineered strains. New England Biolabs, for example, produces more than 265 non-disclosed enzymes in E. coli, each representing a high-value cell line. 

Protein-based drugs are also manufactured in living cells, and proprietary metabolic pathways are used to produce specialty chemicals, bioplastics, and high-value ingredients. 

“In each case, the genetic blueprint inside the cell represents intellectual property that can be protected by our technology,” said Ishita Kumar, a PhD candidate in ChBE and co-lead author of the study.

While the team’s current focus is on protecting intellectual property in the form of high-value cells, future iterations aim to strengthen biological security more broadly. 

“We are currently developing protection measures to mitigate unauthorized use or release of sensitive cell lines that can be potentially hazardous to human health or the environment,” Wilson said.

“As it stands, GeneLock represents an important shift in biological security, enabling, for the first time, protection of valuable cells at the genetic level, even after physical security measures have been bypassed,” he added. 

The work is already moving toward commercialization. The team filed a provisional patent application with the U.S. Patent and Trademark Office in February 2026 and is forming a company to deploy the technology.

This research was funded by a grant from the National Science Foundation.

CITATION:

Dowan Kim, Ishita Kumar, Mohamed Hassan, Luisa F. Barraza-Vergara, Christopher A. Voigt, and Corey J. Wilson, “Protecting cells at the genetic level and simulating unauthorized access via a biohackathon,” Science Advances, 2026.

Today, he is the chief executive officer of Andson Biotech, a growing biotools startup he co-founded with Andrei Fedorov, associate chair for graduate studies and the Rae S. and Frank H. Neely Chair at the George W. Woodruff School of Mechanical Engineering. The company is commercializing a breakthrough technology Chilmonczyk developed during his doctoral research that simplifies the development and production of cell and gene therapies.

Read the full story on the George W. Woodruff School of Mechanical Engineering website.

The two‑day event took place Feb. 4 – 5, coinciding with the Critical Minerals Ministerial hosted by U.S. Secretary of State Marco Rubio in Washington, D.C., on Feb. 4, which brought together more than 50 nations to strengthen and diversify global critical mineral supply chains. During this ministerial, U.K. Minister Seema Malhotra and U.S. Under Secretary of State Jacob Helberg signed a Critical Minerals Memorandum of Understanding, strengthening bilateral cooperation between the United States and the United Kingdom on critical mineral supply chains. 

These broad efforts are supported by White House Executive Order 14363, which defines the Genesis Mission and aims to accelerate scientific discovery through AI. The order identifies critical minerals supply chain resilience as a national security imperative.

In Atlanta, these themes were brought to life in real time. The GEMs-4 workshop brought together researchers, policymakers, national labs, industry leaders, and workforce organizations from both the U.S. and the U.K. to address shared challenges in technology translation, permitting, investment, and talent development. 

The state of Georgia’s integrated ecosystem, linking research universities, legacy industries, technical colleges, national labs, and public‑private partnerships, served as a case study. Presenters highlighted how existing industrial assets in the Southeast are being incorporated into emerging clean energy and critical minerals supply chains, offering a model for other regions seeking to build capabilities around extraction, processing, and manufacturing.

A U.K. member of Parliament representing Cornwall, where the U.K. has lithium reserves and deep critical mineral expertise, joined the convening, as well as representatives from the U.K. Critical Mineral Association, Camborne School of Mines, and the University of Kent. Together, they explored opportunities and challenges, from a fundamental science to a commercialization perspective grounded in real-world experience. 

The alignment between the ministerial in Washington and the expertise present in Atlanta demonstrated the value of state-level engagement and how national agreements translate into practical collaboration on the ground. 

“The Southeast has the research depth, industrial footprint, and collaborative spirit needed to lead in critical minerals innovation,” said Yuanzhi Tang, Georgia Power Professor in the School of Earth and Atmospheric Sciences, executive director of the Strategic Energy Institute, and founding director of the Center for Critical Mineral Solutions at Georgia Tech. “GEMs‑4 showed what’s possible when universities, industry, and government partners align around shared priorities.” 

Day one featured strategic dialogue on critical mineral resources, innovation pathways, and partnership models. A recurring theme was the co-production of critical minerals alongside major mineral commodities. “Many critical minerals are produced as byproducts of larger mining operations, making it essential to integrate recovery strategies into existing mineral industries rather than developing entirely new extraction systems,” noted Crawford Elliott, professor of geosciences at Georgia State University.

Day two transitioned to field‑based learning, led by Paul Schroeder, professor of geology at the University of Georgia. Participants visited active operations to better understand how regional industrial strengths can support national and international supply chain goals. Schroeder said, “Connecting people to the long-standing mineral extraction economy at the mining and plant sites, where the work gets done with an amazingly skilled workforce, underscores the unique role of Georgia’s place‑based capacity in advancing national and transatlantic supply chain goals.”

Organizers emphasized that resilient supply chains rely on regional capabilities built over time through university collaboration, industry partnerships, and community engagement. With three years of inter‑university coordination now underpinning the GEMS platform, the 2026 workshop demonstrated how the Southeast is contributing actionable models for U.S.-U.K. cooperation.

“Ecosystem-building at this scale requires participation from every part of the value chain, and we are encouraged by the model GEMs presents,” said Rachel Galloway, Consul General at British Consulate General Atlanta. “The collaboration across universities, industry, and government is exactly what enables long‑term impact on both sides of the Atlantic.”

Through focused dialogue and partnership-building, the symposium strengthened transatlantic collaboration, highlighted regional strengths, and accelerated innovation and translation across the critical minerals value chain, from resource characterization and processing to recycling, manufacturing, and deployment.

For more information about the GEMS initiative, visit: https://gems.research.gatech.edu/.

A team of researchers led by Ankur Singh, the Carl Ring Family Professor in the George W. Woodruff School of Mechanical Engineering and professor in the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, has been awarded up to $6 million from the Defense Threat Reduction Agency (DTRA) of the U.S. Department of Defense to accelerate the development of medical countermeasures (MCMs) against deadly biological threats that endanger public health, national security, and warfighters.

DTRA’s mission is to provide solutions that enable the Department of Defense, the U.S. government, and international partners to deter strategic threats. A key priority is advancing new or improved MCMs that can be deployed before or after exposure to biological or chemical agents.

Singh’s multi-year project, Systematic Human Immune Engineering for Lethal Disease (SHIELD) Countermeasures, aims to create a threat-agnostic platform that transforms how respiratory pathogens and toxins are studied. The platform is designed to speed up the discovery, development, and production of immune-based countermeasures.

Read the full story on the George W. Woodruff School of Mechanical Engineering website.

“The topic of the Suddath Symposium changes every year, which allows the Georgia Tech research community to annually learn about recent advances on a specific topic from across the immense fields of bioengineering and bioscience,” said Nicholas Hud, Regents’ Professor in the School of Chemistry and Biochemistry and Associate Director of IBB.

The symposium also included presentation of the 2026 Suddath Award, which recognizes outstanding graduate research. This year’s award was presented to Myeongsoo Kim, a Ph.D. candidate in the Bioengineering Graduate Program, for his work at the intersection of cell engineering, cancer treatment, and biomedical imaging. The award is presented each year by members of the Suddath family, including Vincent Suddath, grandson of Bud and a current freshman at Georgia Tech majoring in mathematics.

The symposium and award honor the legacy of F. L. “Bud” Suddath and his lasting contributions to the Institute and the wider Georgia Tech research community.

“Bud was influential in promoting the growth of bioscience research at Georgia Tech, efforts that helped establish IBB in the 1990s,” Hud said. “Bud’s research interests were at the forefront of structural biology, a field that laid the foundation for much of what we know today about biology at the molecular level. It’s fitting that we honor Bud’s contributions by annually providing the Georgia Tech community with the opportunity to learn about research on a timely topic within the biological sciences.”

Symposium co-chairs Tara Deans and Mark Styczynski said that in addition to upholding the legacy of Bud Suddath, the event also provides a unique setting and opportunity for both established researchers and trainees to interact over the course of the two day event. The intimate format of the symposium, which is limited to approximately 100 attendees, and the annual selection of a different interdisciplinary topic sets it apart from other symposia.

“The Suddath Symposium is an amazing opportunity to bring multiple world-class researchers right to our trainees’ front door, to hear about their work and connect with them in a small setting that you can’t really find at most conferences,” said Styczynski, who is a professor in the School of Chemical and Biomolecular Engineering. “We are really grateful to IBB and the Suddath family for supporting this unique event.”

Deans, who is an associate professor in the Wallace H. Coulter Department of Biomedical Engineering, highlighted how this year’s theme reflects a broader shift in the field.

“This year’s focus on biomedical applications of synthetic biology highlights a major inflection point in the field: the transition from proof-of-concept systems to human health-relevant technologies,” she said. “The theme also reflects increasing convergence across disciplines; synthetic biology is no longer operating in isolation, but it is deeply intertwined with immunology, machine learning, diagnostics, and clinical translation. Addressing real-world biomedical problems requires this kind of integration, and the symposium captured that shift very clearly.”

The Suddath Symposium annually serves as a cornerstone event for Georgia Tech’s bioengineering and bioscience community — connecting researchers, honoring scientific legacy, and spotlighting the next generation of scientific innovation.

Health and medicine is more than just biological – societal forces can get under your skin and cause illness. Medical sociologists like me study these forces by treating society itself as our laboratory. Health and illness are our experiments in uncovering meaning, power and inequality, and how it affects all parts of a person’s life.

For example, why do low-income communities continue to have higher death rates, despite improved social and environmental conditions across society? Foundational research in medical sociology reveals that access to resources like money, knowledge, power and social networks strongly affects a person’s health. Medical sociologists have shown that social class is linked to numerous diseases and mortality, including risk factors that influence health and longevity. These include smoking, overweight and obesity, stress, social isolation, access to health care and living in disadvantaged neighborhoods.

Moreover, social class alone cannot explain such health inequalities. My own research examines how inequalities related to social class, race and gender affect access to autism services, particularly among single Black mothers who rely on public insurance. This work helps explain delays in autism diagnosis among Black children, who often wait three years after initial parent concerns before they are formally diagnosed. White children with private insurance typically wait from 9 to 22 months depending on age of diagnosis. This is just one of numerous examples of inequalities that are entrenched in and deepened by medical and educational systems.

Medical sociologists like me investigate how all of these factors interact to affect a person’s health. This social model of illness sees sickness as shaped by social, cultural, political and economic factors. We examine both individual experiences and societal influences to help address the health issues affecting vulnerable populations through large-scale reforms.

By studying the way social forces shape health inequalities, medical sociology helps address how health and illness extend beyond the body and into every aspect of people’s lives.

Origins of Medical Sociology in the US

Medical sociology formally began in the U.S after World War II, when the National Institutes of Health started investing in joint medical and sociological research projects. Hospitals began hiring sociologists to address questions like how to improve patient compliance, doctor-patient interactions and medical treatments.

However, the focus of this early work was on issues specific to medicine, such as quality improvement or barriers to medication adherence. The goal was to study problems that could be directly applied in medical settings rather than challenging medical authority or existing inequalities. During that period, sociologists viewed illness mostly as a deviation from normal functioning leading to impairments that require treatment.

For example, the concept of the sick role – developed by medical sociologist Talcott Parsons in the 1950s – saw illness as a form of deviance from social roles and expectations. Under this idea, patients were solely responsible for seeking out medical care in order to return to normal functioning in society.

In the 1960s, sociologists began critiquing medical diagnoses and institutions. Researchers criticized the idea of the sick role because it assumed illnesses were temporary and did not account for chronic conditions or disability, which can last for long periods of time and do not necessarily allow people to deviate from their life obligations. The sick role assumed that all people have access to medical care, and it did not take into account how social characteristics like race, class, gender and age can influence a person’s experience of illness.

Parsons’ sick role concept also emphasized the expertise of the physician rather than the patient’s experience of illness. For example, sociologist Erving Goffman showed that the way care is structured in asylums shaped how patients are treated. He also examined how the experience of stigma is an interactive process that develops in response to social norms. This work influenced how researchers understood chronic illness and disability and laid the groundwork for later debates on what counts as pathological or normal.

In the 1970s, some researchers began to question the model of medicine as an institution of social control. They critiqued how medicine’s jurisdiction expanded over many societal problems – such as old age and death – which were defined and treated as medical problems. Researchers were critical of the tendency to medicalize and apply labels like “healthy” and “ill” to increasing parts of human existence. This shift emphasized how a medical diagnosis can carry political weight and how medical authority can affect social inclusion or exclusion.

The critical perspective aligns with critiques from disability studies. Unlike medical sociology, which emerged through the medical model of disease, disability studies emerged from disability rights activism and scholarship. Rather than viewing disability as pathological, this field sees disability as a variation of the human condition rooted in social barriers and exclusionary environments. Instead of seeking cures, researchers focus on increasing accessibility, human rights and autonomy for disabled people.

A contemporary figure in this field was Alice Wong, a disability rights activist and medical sociologist who died in November 2025. Her work amplified disabled voices and helped shaped how the public understood disability justice and access to technology.

Structural Forces Shape Health and Illness

By focusing on social and structural influences on health, medical sociology has contributed significantly to programs addressing issues like segregation, discrimination, poverty, unemployment and underfunded schools.

For example, sociological research on racial health disparities invite neighborhood interventions that can help improve overall quality of life by increasing the availability of affordable nutritious foods in underserved neighborhoods or initiatives that prioritize equal access to education. At the societal level, large-scale social policies such as guaranteed minimum incomes or universal health care can dramatically reduce health inequalities.

Medical sociology has also expanded the understanding of how health care policies affect health, helping ensure that policy changes take into account the broader social context. For example, a key area of medical sociological research is the rising cost of and limited access to health care. This body of work focuses on the complex social and organizational factors of delivering health services. It highlights the need for more state and federal regulatory control as well as investment in groups and communities that need care the most.

Modern medical sociology ultimately considers all societal issues to be health issues. Improving people’s health and well-being requires improving education, employment, housing, transportation and other social, economic and political policies.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

A team of researchers led by Ankur Singh, the Carl Ring Family Professor in the George W. Woodruff School of Mechanical Engineering and professor in the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, has been awarded up to $6 million from the Defense Threat Reduction Agency (DTRA) of the U.S. Department of Defense to accelerate the development of medical countermeasures (MCMs) against deadly biological threats that endanger public health, national security, and warfighters.

DTRA’s mission is to provide solutions that enable the Department of Defense, the U.S. government, and international partners to deter strategic threats. A key priority is advancing new or improved MCMs that can be deployed before or after exposure to biological or chemical agents.

Singh’s multi-year project, Systematic Human Immune Engineering for Lethal Disease (SHIELD) Countermeasures, aims to create a threat-agnostic platform that transforms how respiratory pathogens and toxins are studied. The platform is designed to speed up the discovery, development, and production of immune-based countermeasures.

Singh leads a collaborative team that includes Cornell University’s Matthew DeLisa and Stanford University’s Michael Jewett. Together, they will integrate immune-engineering technologies with advanced cell-free protein synthesis platforms to discover and manufacture protein-based MCMs. Cell-free protein synthesis is a laboratory technique that efficiently produces proteins without relying on living cells, which can be unpredictable and technically demanding when it comes to expressing complex or toxic proteins and scaling production quickly. The team expects the SHIELD Countermeasures platform to reduce the time and cost of MCM development by more than tenfold.

“The foundational science and cutting-edge tools we develop will ignite future discoveries, ensuring a robust pipeline of advanced protein-based MCMs for chemical and biological defense,” said Singh, who also directs the Center for Immunoengineering at Georgia Tech. “This will significantly enhance national security and equip our warfighters with next-generation biodefense capabilities."

Traditional animal models often fail to accurately replicate human immune responses, and standard tissue cultures lack the complexity required to study how immune cells interact with pathogens. In contrast, human immune organoids and immune-competent devices — built from human cells — are emerging as groundbreaking research tools. These systems recreate key immune features, such as lymph nodes and mucosal environments, within three-dimensional or microengineered platforms.

“Many organoid and engineering devices, often called organ-on-chip platforms, lack immune integration,” Singh said. “Because immunity sits at the center of human health, these limitations have broad consequences. Immune-competent organ-on-chip platforms extend this concept by combining human cells with microfluidic engineering that simulates blood flow, tissue barriers, and chemical gradients.”

Singh has previously published studies on a synthetic human immune chip and an immunocompetent lung on a chip, and has also teamed up with DeLisa previously to use synthetic immune organoids for immuno-profiling antibacterial MCMs.

“It’s about being able to test far larger numbers of candidate protein-based MCMs in a single experiment—and to do it much faster,” DeLisa said. “Cell-free systems allow us to produce MCMs at unprecedented speed and scale, but traditional evaluation methods can’t keep up with those numbers. By combining cell-free MCM production with immune organoid technology, we can assess the potency of dozens or even hundreds of candidates at a time and characterize the resulting immune responses within just a few days.”

By integrating immune cells with tissues such as lung, gut, skin, or vascular systems, these devices allow scientists to observe immune responses in real time, including cell migration, inflammation, and interactions with pathogens or therapeutics. As biological threats evolve, the development and deployment of immune-competent platforms will be critical for rapid, effective countermeasures.

DTRA’s investment in Singh’s work highlights the urgent national priority of strengthening U.S. biodefense capabilities. The SHIELD Countermeasures platform and its cutting-edge technologies promise to transform the nation’s response to biological threats and help safeguard communities from biological and chemical attacks.

A philanthropic gift from the family of J.P. Singh is helping researchers at Georgia Tech push the boundaries of biomedical innovation.  

With imitation learning, humans demonstrate a task and robots learn to copy what they see through cameras and sensors. While at the leading edge of robotics research, this approach is limited by a major constraint: Robots can only work as fast as the people who taught them. 

Now, Georgia Tech researchers have created a tool that smashes that speed barrier. The system allows robots to execute complex tasks significantly faster than human demonstrations while maintaining precision, control, and safety.

The team addresses a central challenge in modern robotics: how to combine the flexibility of learning from humans with the speed and reliability required for real-world deployment. The technology could lead to wider adoption of imitation learning in industrial and household applications and even enable robots to execute humanlike tasks better than ever before. 

“The thing we’re trying to create — and I would argue industry is also trying to create — is a general-purpose robot that can do any task that human hands can do,” said Shreyas Kousik, assistant professor in the George W. Woodruff School of Mechanical Engineering and a co-lead author on the study. “To make that work outside the lab, speed really matters.”

The new tool, SAIL (Speed Adaptation for Imitation Learning), was born out of a cross-campus, interdisciplinary collaboration that brought together expertise in mechanical engineering, robotics systems, and machine learning. The research team includes Kousik; Benjamin Joffe, senior research scientist at the Georgia Tech Research Institute; and Danfei Xu, assistant professor in the School of Interactive Computing, along with graduate students and researchers from multiple labs.

Speed Without Sacrifice

Teaching robots to work faster than the speed of human demonstrations is challenging. Robots can behave differently at higher speeds, and small changes in the environment can cause errors. 

“The challenge is that a robot is limited to the data it was trained on, and any changes in the environment can cause it to fail,” Kousik said.

SAIL addresses this challenge through a modular approach, with separate components working together to accelerate beyond the training data. The system keeps motions smooth at high speed, tracks movements accurately, adjusts speed dynamically based on task complexity, and schedules actions to account for hardware delays. This combination allows robots to move quickly while staying stable, coordinated, and precise.

“One of the gaps we saw was that our academic robotics systems could do impressive things, but they weren’t fast or robust enough for practical use,” Joffe said. “We wanted to study that gap carefully and design a system that addressed it end to end.”

He added, “The goal is not just to make robots faster, but to make them smart enough to know when speed helps and when it could cause mistakes.” 

The team evaluated SAIL’s performance across 12 tasks, both in simulation and on two physical robot platforms. Tasks included stacking cups, folding cloth, plating fruit, packing food items, and wiping a whiteboard. In most cases, SAIL-enabled robots completed tasks three to four times faster than standard imitation-learning systems without losing accuracy.

One exception was the whiteboard-wiping task, where maintaining contact made high-speed execution difficult.

 “Understanding where speed helps and where it hurts is critical,” Kousik said. “Sometimes slowing down is the right decision.”

While SAIL does not make robots universally adaptable on its own, it represents an important step toward robotic systems that can learn from humans without being constrained by human pace.

By showing how learned robotic behaviors can be accelerated safely and systematically, SAIL brings imitation learning closer to real-world use — where speed, precision, and reliability all matter.

Citation: Ranawaka Arachchige, et. al. “SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies,” Conference on Robot Learning (CoRL), 2025. 

DOI: https://doi.org/10.48550/arXiv.2506.11948

Funding: The authors would like to acknowledge the State of Georgia and the Agricultural Technology Research Program at Georgia Tech for supporting the work described in this paper. 

The new study is the first to visualize mosquito flight patterns and provides hard data for improving capture and control strategies. In addition to being a nuisance, mosquitoes transmit diseases such as malaria, yellow fever, and Zika, which cause more than 700,000 deaths every year.

“It’s like a crowded bar,” said David Hu, a professor in Georgia Tech’s George W. Woodruff School of Mechanical Engineering and the School of Biological Sciences, with an adjunct appointment in the School of Physics. “Customers aren’t there because they followed each other into the bar. They’re attracted by the same cues: drinks, music, and the atmosphere. The same is true of mosquitoes. Rather than following the leader, the insect follows the signals and happens to arrive at the same spot as the others. They’re good copies of each other.”

Read more and watch: 
Georgia Tech College of Engineering newsroom and The Conversation

ISyE researchers are applying AI to complex manufacturing environments, including multistage production systems, asset management, quality improvement, and human‑centered manufacturing. Faculty leaders emphasize the importance of contextualizing large volumes of manufacturing data so AI can support reliable decision‑making, efficient operations, and sustainable outcomes. At the same time, the initiative acknowledges challenges such as data integration, system complexity, and the need to balance automation with human involvement. Together, these efforts position ISyE at the forefront of shaping AI‑powered manufacturing systems that are innovative, resilient, and socially responsible.

Read the full article in ISyE Magazine 

Some of Savannah’s many old buildings are expensive to heat and cool, especially in Georgia’s humid summers. They develop leaks. They need routine maintenance. But how does a building owner know where to begin with renovations or repairs? Enter Lamarr.AI, one of the first companies supported by the Partnership for Innovation’s (PIN) new Community Investment program.

“The Community Investment program is matching up faculty-led, faculty-spinoff startup companies that have technology that could be relevant to a community, a government, or to the civic space,” said Katie O’Connor, PIN’s community investment manager. “The company’s product is something that can help a community in a smart cities kind of way.”

Lamarr.AI fits the bill to a T. Its technology and the company grew out of research at Georgia Tech. Lamarr.AI’s technology uses drones, imaging, and artificial intelligence (AI) to assess a building’s envelope and determine the best ways to make these structures more energy efficient.

“The technology is like giving a building an MRI using drones, infrared and regular images, and our own AI,” said Tarek Rakha, Lamarr.AI’s co-founder and CEO. The drones, he explained, detect missing insulation, water intrusion, air escaping, and physical damage. AI and machine learning translate that information into 3-D models that map the defects.

Read more on EI2 Webpage
 

Across more than 1,100 combined respondents, the study uncovers selective temporal stability in energy preferences. Some attributes—such as support for higher RPS targets, reductions in water use, and preferences for online smart‑meter information—remain relatively stable over time. In contrast, others shift considerably: WTP for increasing the rooftop solar share declines by more than 40%, while WTP to protect monthly credit banking rises more than 200%, reflecting heightened awareness of net‑metering debates and rapid growth in rooftop solar adoption.

Importantly, the study reveals that environmental attitudes, measured through New Ecological Paradigm (NEP) scores, once strongly predicted preferences for rooftop solar and smart‑meter technologies in 2017, but these relationships fade or even reverse by 2023—signaling a shift as these technologies transition from niche, identity‑driven goods to mainstream infrastructure. Meanwhile, environmental attitudes continue to robustly shape preferences for RPS increases and water‑use reductions in both survey waves.

Read Full Story on the EPIcenter Webpage

The paper examines various strategies for enhancing the flexibility of data center energy use. One approach is to use backup power systems, such as uninterruptible power supplies, to support the grid during emergencies. Another method involves rerouting computing jobs to different data centers in other locations to balance energy demand. The authors also discuss implementing smart scheduling techniques that shift workloads to off-peak hours, reducing strain on the grid. Additionally, they highlight adjusting processor speeds by lowering CPU (central processing unit) and GPU (graphics processing unit) clock rates to limit power consumption when needed. Finally, the paper suggests pre-cooling data center equipment to limit the energy required for cooling during peak demand periods. Notably, experimental evidence shows that underclocking GPUs can cut power consumption by 40% with only a 22% performance loss, suggesting technical feasibility for demand-response interventions.

Despite these technical options, the authors find that real-world cost considerations and reliability concerns limit widespread adoption. Data center operators generally do not change their behavior in response to electricity prices, as job revenue far outweighs energy costs under normal conditions. For example, a GPU rented at $2 per hour consumes only $0.04 worth of electricity at average prices, making curtailment unattractive except during extreme price spikes. Surveys indicate that operators are reluctant to compromise reliability or deploy backup systems for ancillary services. Consequently, price-based incentives alone are unlikely to drive meaningful flexibility.

Read more on the EPIcenter Webpage
Listen to a podcast on the research here

Kala Jordan has been promoted to Research Scientist II. With a background spanning biology, health informatics, and STEM education, Jordan brings a multidisciplinary approach to her work. She plays a key role in AI‑CARING, leading studies that support the development of personalized collaborative AI systems designed to improve quality of life for older adults.

Noah Posner has been promoted to Senior Research Scientist. As manager of the Interactive Product Design Lab, Posner focuses on interactive experiences grounded in physical interaction. His research spans CAD‑based prototyping, rapid fabrication, and STEAM education, and he teaches courses in physical prototyping and industrial design.

Peter Presti has been promoted to Principal Research Scientist. Over his 22‑year career at Georgia Tech, Presti has collaborated with major industry partners and federal agencies. His research spans sensor systems, biometrics, wearable computing, signal processing, embedded systems, and integrated hardware‑software prototyping.

Richard Starr has been promoted to Senior Research Scientist. Starr oversees the IPaT Secure Data Enclave, developing and managing the institute’s secure infrastructure for healthcare data. His work ensures campus‑wide compliance with HIPAA, IRB requirements, and partnership agreements.

Andrew Zhao has been promoted to Research Scientist II. Zhao, a Georgia Tech alumnus with bachelor’s and master’s degrees in Computer Science, specializes in social computing. His work examines how social media facilitates information flow and connection, particularly around mental health and elections. He supports the CANDOR Portal and AI‑CARING projects, contributing full‑stack development, data pipelines, LLM fine‑tuning, and infrastructure management.

“These promotions are wonderful and well deserved. Hearty congratulations to Andrew, Kala, Richard, Noah, and Peter!” said Michael Best, executive director of IPaT.

“These promotions are a testament to the outstanding capabilities and contributions of IPaT’s research faculty community,” added Maribeth Gandy Coleman, director of research for IPaT.

As new data centers continue to be built and proposed in Georgia, counties and municipalities across the state are considering how to guide this growth. EPIcenter’s data center dashboard provides policymakers, planners, researchers, and community stakeholders with a centralized resource to better understand how data center regulations are being developed and applied across Georgia and the U.S.

“Our Data Center Hub provides Georgia communities with a one-stop shop to understand how their neighbors are managing land-use regulations for data centers,” said Laura Taylor, director of EPIcenter. “It brings together clear, accessible information to help jurisdictions plan when data center growth occurs in their area.”

The dashboard is organized around five thematic areas commonly addressed in data center land-use regulations: Site Planning and Building Design, Infrastructure and Utilities, Environmental and Community Protections, Public Safety and Security, and Lifecycle Governance. Within each theme, users can explore specific regulatory topics and access the relevant ordinances enacted by Georgia communities.

To build the dashboard, EPIcenter researchers conducted a comprehensive review of municipal codes across the state.

“We reviewed municipal codes for about 180 cities and counties across Georgia and identified ordinances that specifically address data center development,” said Yang You, EPIcenter’s research associate who developed the project. “In total, we found 19 data center-specific topics that ordinances tend to cover. We analyzed ordinances across jurisdictions and organized their ordinance provisions into topics such as building placement, setbacks, infrastructure, and environmental considerations to make it easier to compare how different jurisdictions regulate data centers.”

You added that the dashboard also incorporates examples from outside of Georgia. By gathering ordinances from other states and pairing them with Georgia-specific examples, EPIcenter aims to provide a clear framework to help communities efficiently address data center land-use regulation.

The Georgia Data Center Ordinance Hub is available through the Energy Policy and Innovation Center website.

That support, however, begins to disappear in these restrictive cultures once women reach menopause, according to new research from Georgia Tech

Naveena Karusala, an assistant professor in Georgia Tech’s School of Interactive Computing, and master’s student Umme Ammara are working toward improving existing technologies and designing new ones for a demographic they believe has been neglected.

Karusala and Ammara co-authored a paper based on a study they conducted with women in urban Pakistan experiencing menopause.

“Women’s health is understudied in general, but menopause is more neglected than other women’s health issues,” Karusala said. “Our choice to focus on menopause is motivated by expanding how we holistically think about women’s well-being across their lifespan.”

Karusala and Ammara will present their paper in April at the 2026 ACM Conference on Human Factors in Computing Systems (CHI) in Barcelona.

Masking Symptoms

Menopause is diagnosed after 12 consecutive months without a period, vaginal bleeding, or spotting. The transition to menopause, called perimenopause, usually happens over two to eight years.

Hormone changes may cause symptoms such as irregular periods, vaginal dryness, hot flashes, night sweats, trouble sleeping, mood swings, and brain fog.

These symptoms can be debilitating in some cases and affect daily life. However, Ammara said women are pressured to remain silent, maintain appearances, and regulate their emotions to meet social expectations.

“Understanding menopause is important because a woman would be experiencing all these symptoms, and people will not understand those as actual symptoms,” Ammara said. “There’s been resistance to the idea of the medicalization of menopause. People don’t view it as an illness, but as a life transition and something that happens naturally.”

Feeling Isolated

The women interviewed by Karusala and Ammara either stayed at home full-time or were part of the workforce.

The researchers discovered that trusted family members might be the only sources women who stay at home and do not work turn to for disclosure. 

“Women at home have the flexibility to take breaks or work at their own pace, so a lot of their experience is shaped by the emotional barriers they face,” Ammara said. 

“That could come from their husbands and family members. Some are supportive and some are not. They might weaponize it and use that term against them, or they might dismiss what they’re going through.”

Ammara said it might be easier for women in the workforce to confide in their coworkers, but explaining to an employer that they need sick leave for menopause symptoms can be intimidating.

Even in online communities that have enabled women to anonymously share their health experiences, menopause is seldom discussed.

Raising Awareness

Karusala and Ammara argue in their paper that a public health approach could be the most effective way to spark conversation about menopause in a patriarchal culture in which technology use varies.

They said the challenge in implementing technologies geared toward menopause support is that the condition isn’t well understood in public. Improving maternal health, for example, is easier to promote within these societies because of the general understanding that motherhood is important.

“There must be an existing infrastructure to build on,” Karusala said. “For example, menstrual and maternal health are taught in schools and regularly discussed in primary care. Cultural and social meaning and importance are placed on motherhood.

“A lot of that doesn’t exist for menopause. Primary care doctors are unprepared to talk about menopause compared to other health issues.”

Design Solutions

Ammara said that the most effective way for technologies to make an impact on women going through menopause is to directly address systemic power structures around women’s health within Pakistani culture.

It can start with the husbands. 

“Framing the issue for husbands to understand menopause should be at the forefront of designing technology solutions,” she said. 

“In Islamic contexts, we suggest using faith-based framings. This has been proposed for maternal health in prior works that draw on Islamic principles to engage expectant fathers in providing care and support. Framing it around religious responsibility to involve men in the journey can also be done for menopause.”

Read more »

More than 300 leaders from industry, government, and academia gathered on Georgia Tech’s campus for Energy Day, a one-day conference focused on one of today’s most urgent challenges: meeting the rapidly growing energy demands of artificial intelligence (AI).  

Published today in the journal Nature Communications, the paper “Trivalent Titanium in High-Titanium Lunar Ilmenite” confirms titanium in a reduced, trivalent state in a black, metal-rich lunar mineral called ilmenite. It’s a state only possible in low-oxygen environments, conditions researchers refer to as “reducing.”

“Models have suggested that these reducing conditions may have varied at different locations and times across the surface of the Moon,” says lead author Advik Vira, a graduate student in the School of Physics who recently earned his doctoral degree. “We hope our microscopy technique can be a valuable step in mapping and understanding the Moon’s 4.5-billion-year history.”

The team anticipates that their technique could be used on many of the lunar samples collected more than 50 years ago by the Apollo missions in addition to the Apollo Next Generation Samples — a group of lunar samples that have been stored under pristine conditions — and new samples from the planned Artemis missions, with Artemis II slated for launch this spring. The technique might also be applicable to samples collected from the far side of the Moon and returned in 2024 by the Chang’e-6 mission.

“The Moon holds clues not only to its own past, but also to the earliest eras of Earth’s evolution — history that has long since been erased from our planet,” Vira says. “This study is a step toward understanding the history of both and a reminder that there is still so much left to learn from the lunar rocks we’ve brought back to Earth.”

The School of Physics research team included corresponding authors Vira and Professor Phillip First; in addition to graduate student Roshan Trivedi; undergraduate students Gabriella Dotson, Keyes Eames, Dean Kim, and Emma Livernois; and Professor Zhigang Jiang, along with Institute for Matter and Systems Materials Characterization Facility Senior Research Scientist Mengkun Tian; School of Chemistry and Biochemistry Senior Research Scientist Brant Jones and Thom Orlando, Regents' Professor in the School of Chemistry and Biochemistry with a joint appointment in the School of Physics. 

The Georgia Tech team was joined by Addis Energy Senior Geochemist Katherine Burgess; Macalester College Assistant Professor of Geology Emily First; along with Lawrence Berkeley National Laboratory Research Scientist Harrison Lisabeth, Senior Scientist Nobumichi Tamura, and Postdoctoral Fellow Tyler Farr, who recently earned a Ph.D. from Georgia Tech’s George W. Woodruff School of Mechanical Engineering.

CLEVER research

The investigation began with a dark gray rock called a lunar basalt. Formed when ancient magma erupted on the Moon’s surface, minerals crystallized as it cooled — preserving key information in their structures. Billions of years later, the rock was brought to Earth by the 1972 Apollo 17 mission, where a small piece is now stored at Georgia Tech’s Center for Lunar Environment and Volatile Exploration Research (CLEVER), a NASA Solar System Exploration Research Virtual Institute (SSERVI) center led by Orlando.

As a NASA virtual institute, CLEVER supports researchers exploring lunar conditions and developing tools for the upcoming crewed Artemis missions, and provided the lunar samples for this research. The SSERVI also plays a critical role in training the next generation of planetary researchers: both Vira and Farr earned their Ph.D.s while on the CLEVER team.

“At CLEVER, we are very interested in understanding the impacts of space weathering,” Vira says. “We implemented modern sample preparation and advanced microscopy techniques to image samples at the atomic level, and were curious to apply it more broadly to the collection of Apollo rocks in the Orlando Lab. This sample caught our attention.”

“When we imaged an ilmenite crystal from the lunar basalt, what struck us first was how uniform and perfect the crystal structure was,” he recalls. “We found no defects from space weathering and instead saw an undamaged, pristine crystal — undisturbed for 3.8 billion years.”

To investigate further, the team analyzed small chips of the rock with Burgess, a member of the RISE2 SSERVI team and then a geologist at the U.S. Naval Research Laboratory. Using state-of-the-art electron microscopy and spectroscopy techniques, Vira determined the oxidation state of the elements in the ilmenite present. 

In spectroscopy measurements, each element leaves a distinct ‘signature,’ Vira explains. “When we brought our results back to Georgia Tech’s Materials Characterization Facility, Mengkun (Tian) noticed something unusual: the signature showed titanium might be present in the trivalent state.”

The presence of trivalent titanium had long been suspected in this lunar mineral. The team was intrigued. 

A new window into old rocks

With funding from Georgia Tech’s Center for Space Technology and Research (CSTAR), Vira returned to the U.S. Naval Research Laboratory to analyze additional samples. The results confirmed that more titanium was present than the mineral’s formula (FeTiO₃) predicts — indicating a portion of the titanium present was trivalent.

“That led me to place our measurements in terms of the broader geological context,” Vira shares. Working with First, Vira explored how ilmenite with trivalent titanium could help reconstruct the nature of ancient magmas from the Moon, especially the chemical availability of oxygen.

“Because its location on the Moon was noted during the Apollo mission, we know exactly where this rock is from, and we can determine how old the rock is,” he explains. “When coupled with our trivalent titanium measurements, we can use that information to estimate the reducing conditions for this specific region at the specific time our rock formed.”

If the upcoming Artemis missions return samples suitable for the team’s technique, these rocks could provide a new window into ancient lunar geology. The research also highlights that many lunar samples already on Earth could be reexamined to look for trivalent titanium.

“There is still so much to learn from the lunar samples we have already brought to Earth,” Vira says. “It’s a testament to the long-term value of each sample return mission. As technology continues to advance, this type of work will continue to give us critical insights into our planet and our place in the universe for years to come.”

DOI: 10.1038/s41467-026-69770-w

Funding: This work was directly supported by the NASA SSERVI under CLEVER. Researchers were also supported by the NASA RISE2 SSERVI and the Heising-Simons Foundation. Funding for collaborations between the U.S. Naval Research Laboratory and Georgia Tech for the investigation of lunar minerals was provided by the Georgia Tech Center for Space Technology and Research. Sample preparation was performed at the Georgia Tech Institute for Matter and Systems, which is supported by the National Science Foundation. This work utilized the resources of the Advanced Light Source, a user facility supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, and was supported in part by previous breakthroughs obtained through the Laboratory Direct.

The Georgia Scientific Computing Symposium (GSCS) held its annual meeting on February 21 in Athens. Since 2009, the event has hosted researchers from across the Peach State to showcase homegrown advances in scientific computing.

The symposium highlighted Georgia’s reputation as a computing innovation hub. People from around the world come to Georgia universities to lead computing research. By advancing science, engineering, medicine, and technology, their work improves communities at home and abroad.

Faculty and students from Georgia Tech, UGA, Georgia State University, and Emory University presented at the symposium. Georgia Tech participants came from the colleges of Computing, Engineering, and Sciences.

This year’s organizers agreed to meet in Atlanta for the 2027 symposium. Georgia Tech’s School of Computational Science and Engineering (CSE) will host the 19th GSCS.

“From healthcare to computer chip design, scientific computing underpins many of the technological advances we see in our lives,” said Professor Edmond Chow, associate chair of the School of CSE.

“Scientific computing provides the mathematical models, simulations, and data‑driven tools that make modern innovation possible. It allows people to analyze complex systems, test ideas virtually before building them, and make faster, more accurate decisions across nearly every sector of society.”

Professor Haomin Zhou and Assistant Professor Helen Xu delivered two of the symposium’s five plenary talks. 

Zhou presented a new method for solving the Schrödinger equation, a landmark equation in quantum mechanics. Drawing inspiration from the mathematics used in generative artificial intelligence models, his approach develops an algorithm that more effectively simulates waves, particle motion, and other physical systems.

Xu focused on improving how computers move and organize data during complex calculations. Her work uses “cache-friendly” layouts that help computers access data more efficiently, boosting performance for scientific and engineering applications.

“Speaking at GSCS was a great opportunity,” Xu said. “The symposium fostered connections within the scientific computing community and gave us a chance to share exciting research.”

The symposium showcased student work through a poster blitz and a poster session. During the blitz, 36 students each had one minute to introduce their research to the full audience. They then shared more details about their research during the poster session.

The student projects showed the range of fields supported by scientific computing. The session also provided attendees with an opportunity to connect and expand their professional networks, helping grow the field’s future impact.

“As an aerospace engineer by training and aspiring computational scientist, GSCS gave me the platform to network with other researchers in the field while showcasing my own research,” said M.S. student Kashvi Mundra. 

“I was able to connect with scientists across different disciplines whose work intersects with my own in unexpected ways. Those conversations pushed my thinking beyond my own lab's perspective, helping me see my work on physics-informed machine learning for inverse problems in a broader scientific computing context.”

Georgia Tech students who presented posters included:

Abir Haque (CSE), Massively Parallel Random Phase Approximation Correlation Energy via Lanczos Quadrature

Antonio Varagnolo (CSE), Physics-Enhanced Deep Surrogates for the Phonon Boltzmann Transport Equation

Ben Burns (CSE), Infinite-Dimensional Stein Variational Inference with Derivative-Informed Neural Operators

Ben Wilfong (CSE), Shocks without Shock Capturing; Compressible Flow at 1 quadrillion Degrees of Freedom without Loss of Accuracy

Daniel Vickers (CSE), Highly-Parallel Fluid-Solid Interactions for Compressible Flows

Eric Fowler (CSE), High-Performance Tensor Contractions in Computational Chemistry

Haoran Yan (Math), Understanding Denoising Autoencoders through the Manifold Hypothesis: A Geometric Perspective

Kashvi Mundra (CSE), Autoregressive Multifidelity Neural Surrogate Modeling under Scarce Data Regimes

Sebastián Gutiérrez Hernández (Math/CSE), PDPO: Parametric Density Path Optimization

Vivian Zhang (AE), Multifidelity Operator Inference: Non-Intrusive Reduced Order Modeling from Scarce Data

Xian Mae Hadia (CSE), Data Efficiency of Surrogate Models: Learning Physics Data from Full Field Data vs. Inductive Bias from Approximate PDE Solvers

Xiangming Huang (CSE), Neural Operator Accelerated Evolutionary Strategies for PDE-Constraint Optimization

Zhaiming Shen (Math), Understanding In-Context Learning on Structured Manifolds: Bridging Attention to Kernel Methods

Zhongjie Shi (Math), Towards Understanding Generalization in DP-GD: A Case Study in Training Two-Layer CNNs

New cybersecurity research from the Georgia Institute of Technology, however, revealed that crews aboard commercial vessels were often not adequately prepared to manage cyberattacks effectively due to systemic training gaps.

The findings are based on interviews conducted by researchers with more than 20 officer-level mariners to assess the maritime industry’s readiness to handle cybersecurity attacks at sea.

"Historically, cybersecurity research has focused heavily on cyber-physical systems like cars, factories, and industrial plants, but ships have largely been overlooked,” said Anna Raymaker, Ph.D. student and lead researcher.

“That gap is concerning when more than 90% of the world’s goods travel by sea. Recent incidents, from GPS spoofing to ships linked to subsea cable disruptions, show that maritime systems are increasingly part of the global cyber threat landscape.”

The researchers proposed four practical strategies to strengthen maritime cyber defenses and close the training gaps. Their findings were presented recently at the ACM SIGSAC Conference on Computer and Communications Security (CCS).

1. Make Cybersecurity Training Actually Maritime

Many of those interviewed for the study described current cybersecurity training as “boilerplate” — generic modules that don’t reflect real shipboard risks. 

Researchers recommend:

• Role-specific instruction: Navigation officers should learn to detect and identify GPS spoofing. Engineers should focus on vulnerabilities in remotely monitored systems.
• Bridging IT and Operational Technology: Crews need to understand how attacks on IT systems can trigger physical consequences in operational technology — including collisions, groundings, or explosions.
• Hands-on delivery: Replace passive PowerPoints with drills and in-person exercises that build muscle memory.
• Accessible standards: Training must account for the wide range of educational backgrounds across crews and be standardized across ranks.

2. Move Beyond “Call IT”

At sea, crews can’t simply escalate a cyber incident to a shore-based IT department and wait. Operational resilience requires onboard readiness.

Researchers recommend:

• Vessel-specific response plans: Ships need clear, actionable protocols for threats such as AIS jamming or radar manipulation.
• Military-style drills: Adopting MCON (Emission Control) exercises — used by the U.S. Military Sealift Command — can train crews to operate safely without electronic systems.
• Stronger connectivity controls: High-bandwidth satellite systems like Starlink introduce new risks. Clear policies and network segregation are essential to prevent new entry points for attackers.

Related Article: When GPS lies at sea: How electronic warfare is threatening ships and their crews by Anna Raymaker

3. Create Unified, Ship-Specific Regulations

Maritime cybersecurity regulations are often reactive and fragmented. Researchers argue the industry needs a cohesive, domain-specific framework.

Key recommendations include:

• A unified global model: Like the energy sector’s NERC CIP standards, a maritime framework could mandate baseline controls such as encryption, network segmentation, and anonymous incident reporting.
• Rules built for real crews: Regulations designed for large naval operations don’t translate well to smaller merchant or research vessels. Standards must reflect actual shipboard conditions.
• Future-proofing requirements: Autonomous ships and remotely operated vessels expand the cyber-physical attack surface. Regulations must proactively address these emerging technologies.

4. Invest in Maritime-Specific Cyber Research

Finally, the researchers stress that long-term resilience requires deeper technical research focused on maritime systems.

Priority areas include:

• Real-time intrusion detection systems tailored to shipboard protocols.
• Proactive security risk assessments of interconnected onboard systems.
• Cyber-physical modeling to better understand cascading failures in complex maritime environments.

The Bottom Line

Cyber threats at sea are no longer hypothetical. Mariners report real-world incidents ranging from GPS spoofing to ransomware that disrupts global trade.

“Through our interviews with mariners, I saw firsthand how much dedication and pride they take in their work,” said Raymaker. “Our goal is for this research to serve as a call to action for researchers, policymakers, and industry to invest more attention in maritime cybersecurity and support the people who risk their lives every day to keep global trade, food, and energy moving."

A Sea of Cyber Threats: Maritime Cybersecurity from the Perspective of Mariners was presented at CCS 2025. It was written by Raymaker and her colleagues, Ph.D. students Akshaya Kumar, Miuyin Yong Wong, and Ryan Pickren; Research Scientist Animesh Chhotaray, Associate Professor Frank Li, Associate Professor Saman Zonouz, and Georgia Tech Provost and Executive Vice President for Academic Affairs Raheem Beyah.

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Amino acids also play a critical role commercially where they are manufactured and added to pharmaceuticals, dietary supplements, cosmetics, animal feeds, and industrial chemicals — an energy-intensive process leading to greenhouse gas emissions, resource consumption, and pollution.

A landmark new system developed at Georgia Tech could lead to an alternative: a commercially scalable, environmentally sustainable method for amino acid production that is carbon negative, using more carbon than it emits.

The breakthrough builds on a method that the team pioneered in 2024 and solves a key issue – increasing efficiency to an unprecedented 97% and reducing the bioprocess cost by over 40%. It’s the highest reported conversion of CO2 equivalents into amino acids using any synthetic biology system to date.

Published in the journal ACS Synthetic Biology, the study, “Cell-Free-Based Thermophilic Biocatalyst for the Synthesis of Amino Acids From One-Carbon Feedstocks,” was led by Bioengineering Ph.D. student Ray Westenberg and Professor Pamela Peralta-Yahya, who holds joint appointments in the School of Chemistry and Biochemistry and School of Chemical and Biomolecular Engineering. The team also included Shaafique Chowdhury (Ph.D. ChBE 25) and Kimberly Wennerholm (ChBE 23); alongside University of Washington collaborators Ryan Cardiff, then a Ph.D. student and now a Chain Reaction Innovations Fellow at Argonne National Laboratory, and Charles W. H. Matthaei Endowed Professor in Chemical Engineering James M. Carothers; in addition to Pacific Northwest National Laboratory Synthetic Biology Team Leader Alexander S. Beliaev.

"This work shifts the narrative from simply reducing carbon emissions to actually consuming them to create value,” says Peralta-Yahya. “We are taking low-cost carbon sources and building essential ingredients in a truly carbon-negative process that is efficient, effective, and scalable.”

Heat-Loving Organisms

The work builds on the cell-free technology the team used in their earlier study. “Previously, we discovered that a system that uses the machinery of cells, without using actual living cells, could be used to create amino acids from carbon dioxide,” Peralta-Yahya explains. “But to create a commercially viable system, we needed to increase the system’s efficiency and reduce the cost.”

The team discovered that bits of leftover cells were consuming starting materials, and — like a machine with unnecessary gears or parts — this limited the system’s efficiency. To optimize their “machine,” the team would need to remove the extra background machinery.

"Leftover cell parts were using key resources without helping produce the amino acids we were looking for,” says Peralta-Yahya. “We knew that heating the system could be one way to purify it because heat can denature these components.”

The challenge was in how to protect the essential system components from the high temperatures, she adds. “We wondered if introducing enzymes produced by a heat-loving bacterium, Moorella thermoacetica, might protect our system, while still allowing us to denature and remove that inefficient background machinery.”

The results were astounding: after introducing the enzymes, heating and “cleaning” the system, and letting it cool to room temperature, synthesis of the amino acids serine and glycine leaped to 97% yield — nearly three times that of the team’s previous system.

Scaling for Sustainability

To make the system viable for large-scale use, the team also needed to reduce costs. “One of the most costly components in this system is the cofactor tetrahydrofolate (THF),” Peralta-Yahya shares. “Reducing the amount of THF needed to start the process was one way to make the system more inexpensive and ultimately more commercially viable.”

By linking reaction steps so waste from one step fueled the next, the team devised a method to recycle THF within the system that reduces the amount of THF needed by five-fold — lowering bioprocessing costs by 42%.

“This decrease in cost and increase in yield is a critical step forward in creating a method with real potential for use in industry and manufacturing,” Peralta-Yahya says. “This system could pave the way for moving this carbon-negative technology out of the lab and onto the continuous, industrial scale."

Funding: The Advanced Research Project Agency-Energy (ARPA-E); U.S. Department of Energy; and the U.S. Department of Energy, Office of Science, Biological and Environmental Research Program.

DOI: https://doi.org/10.1021/acssynbio.5c00352

Thinking about polymers in that way has led Georgia Tech materials scientists to create new generative artificial intelligence tools that are like Claude or ChatGPT for new materials. 

These are the first foundational models for generative polymer design that have also been validated through physical experiments: users specify the properties they need in a polymer and the model will suggest a chemical structure.

Led by Regents’ Entrepreneur Rampi Ramprasad, the researchers described their latest model this month in the Nature journal npj Artificial Intelligence — including a test material they created and validated in the lab to prove the models work.

Read the full story on the College of Engineering website.

A new mobile app will soon put the ability to monitor a baby’s prenatal heartbeat in the hands of pregnant women who may worry about their baby’s health in between doctor’s visits. 

Set in the heart of Tech Square on the Georgia Tech campus, this year’s event explores how energy systems, materials, technologies, supply chains, and policy must evolve in response to AI’s accelerating impact. As digital infrastructure expands and computation intensifies, the need for reliable, resilient, and sustainable power has never been more urgent. 

“Energy Day reflects Georgia Tech’s strength in connecting world-class research in materials and components with the infrastructure and partnerships needed to translate discovery into scalable energy technologies that serve industry, society, and the future economy,” said Eric Vogel, executive director of the IMS and the Hightower Professor in Materials Science and Engineering. 

Energy Day 2026 also marks an important milestone with the introduction of its first group of corporate sponsors: GE Vernova, Southern Company, Georgia Power, ExxonMobil, Southwire Spark, Gems Setra, and Tektronix. Their support reflects a shared commitment to advancing energy solutions. 

“Tektronix is excited to be part of Energy Day because advancing the future of energy starts with precise measurement and trusted insights,” said Christopher Bohn, president of Tektronix. “From power electronics and high voltage systems to grid scale renewables and AI driven control technologies, the breakthroughs discussed here directly align with the innovations we support through our products and solutions. Collaborating with Georgia Tech allows us to engage early with emerging research and the next generation of engineers—critical collaborators in building a cleaner, smarter, and more resilient energy ecosystem.”

The keynote address will be delivered by Vanessa Z. Chan, a nationally recognized leader at the intersection of innovation, commercialization, and emerging technologies. Chan will provide insights on accelerating technological discovery, emphasizing how AI is transforming energy and materials design. She will discuss how commercialization strategies must rapidly evolve across multidisciplinary energy domains from grid modernization to advanced batteries and clean manufacturing.

Building on the themes introduced in the keynote, the program transitions into a fireside chat with Georgia Tech EVPR Tim Lieuwen featuring Amit Kulkarni and Jim Walsh. Kulkarni is vice president of Product Management and Strategy for the Gas Power business within GE Vernova, where he oversees the world’s largest portfolio of power generation equipment. Walsh, vice president of GE Vernova’s Consulting Services, leads teams providing innovative solutions across the full spectrum of power generation, delivery, and utilization.

Next comes a policy-focused panel that will explore the surge in power demand driven by AI, how the United States is addressing today’s most urgent energy challenges, and the long-term implications of today’s decisions for a sustainable energy future. Bringing together leading voices in U.S. environmental and energy policy, the panel features Joe Aldy of Harvard University and former special assistant to the president for Energy and Environment; Al McGartland of New York University’s Institute for Policy Integrity and former Environmental Protection Agency lead economist and director of the National Center for Environmental Economics; and Kevin Rennert, fellow and director of the Comprehensive Climate Strategies Program at Resources for the Future and former staff member on the U.S. Senate Committee on Energy and Natural Resources.

The second panel focuses on critical materials — the foundation of advanced energy systems and digital technologies. As AI, data centers, and advanced energy technologies drive demand for critical materials, securing them now requires integration and coordination across the entire value chain. Panelists include Rachel Galloway, British consul general in Atlanta; Vijay Murugesan, head of Materials Intelligence and Digital Innovation at Amazon; Colin Spellmeyer, executive strategic sourcing leader at GE Vernova;  Charles Sims, Tennessee Valley Authority Distinguished Professor of Energy and Environmental Policy at the University of Tennessee; and Nortey Yeboah, principal engineer at Southern Company. Together, they will offer perspectives on the policy and economic frameworks shaping the energy supply chain, from developing raw resources to manufacturing the technologies essential to future energy systems.

In the afternoon, participants can dive deeper into specialized topics through three focused technical tracks. 

• “Meeting the Demand for Power” will examine how emerging technologies, advanced nuclear systems, and renewable integration can work together to deliver reliable, resilient electricity.
• “Data Center Infrastructure and Resources” will explore innovations in thermal management technologies, energy-efficient computing, and the broader resource impacts of expanding digital infrastructure.
• “Grid Technologies and Markets” will highlight strategies for strengthening grid capacity, incorporating demand-side management, and optimizing carbon performance as energy systems evolve.

“Meeting the rapidly rising electricity demand driven by AI requires bold ideas, coordinated action, and research that moves at the speed of innovation,” said Yuanzhi Tang, executive director of the SEI. “Energy Day 2026 brings together the people and expertise needed to shape resilient, sustainable energy systems for the future. At Georgia Tech, we see this event as a catalyst for new partnerships, new solutions, and a shared commitment to strengthening the nation’s energy foundation.”

Energy Day 2026 is designed for researchers advancing emerging energy technologies, policymakers navigating shifting regulatory and geopolitical landscapes, industry professionals seeking insight into emerging tools and supply chains, and students preparing to enter one of the most consequential sectors of the decade. It also welcomes anyone interested in AI, sustainability, electrification, and critical materials. 

Join us to explore the future of energy. To learn more and register, visit: Energy Day 2026.

Georgia’s forest industry has long been a pillar of the state’s rural economy. But in recent years, mill closures and shifting markets have put pressure on landowners, workers, and entire communities, particularly in south Georgia. A recently approved $8.9 million Georgia Forestry Innovation Initiative will help chart a new path forward, creating more value from Georgia’s abundant forest resources and expanding opportunities for the people and regions depending on them. 

Read more »

Indoor farms, also known as vertical farms, are popular among agricultural researchers and are expanding across the agricultural industry. Some benefits they have over outdoor farms include:

• Year-round production of food crops
• Less water and land requirements
• Not needing pesticides
• Reducing carbon emissions from shipping
• Reducing food waste

Additionally, some studies indicate that indoor farms produce more nutritious food for urban communities. 

However, these farms are often inaccessible to birds, bees, and other natural pollinators, leaving the pollination process to humans. The tedious process must be completed by hand for each flower to ensure the indoor crop flourishes.

Ai-Ping Hu, a principal research engineer at the Georgia Tech Research Institute (GTRI), has spent years exploring methods to efficiently pollinate flowering plants and food crops in indoor farms to find a way to efficiently pollinate flower plants and food crops in indoor farms.

Hu, Assistant Professor Shreyas Kousik of the George W. Woodruff School of Mechanical Engineering, and a rotating group of student interns have developed a robot prototype that may be up to the task.

The robot can efficiently pollinate plants that have both male and female reproductive parts. These plants only require pollen to be transferred from one part to the other rather than externally from another flower.

Natural pollinators perform this task outdoors, but Hu said indoor farmers often use a paintbrush or electric tootbrush to ensure these flowers are pollinated. 

Knowing the Pose

An early challenge the research team addressed was teaching the robot to identify the “pose” of each flower. Pose refers to a flower’s orientation, shape, and symmetry. Knowing these details ensures precise delivery of the pollen to maximize reproductive success. 

“It’s crucial to know exactly which way the flowers are facing,” Hu said.

“You want to approach the flower from the front because that’s where all the biological structures are. Knowing the pose tells you where the stem is. Our device grasps the stem and shakes it to dislodge the pollen.

“Every flower is going to have its own pose, and you need to know what that is within at least 10 degrees.”

Computer Vision Breakthrough

Harsh Muriki is a robotics master’s student at Georgia Tech’s School of Interactive Computing, who used computer vision to solve the pose problem while interning for Hu and GTRI.

Muriki attached a camera to a FarmBot to capture images of strawberry plants from dozens of angles in a small garden in front of Georgia Tech’s Food Processing Technology Building. The FarmBot is an XYZ-axis robot that waters and sprays pesticides on outdoor gardens, though it is not capable of pollination.

“We reconstruct the images of the flower into a 3D model and use a technique that converts the 3D model into multiple 2D images with depth information,” Muriki said. “This enables us to send them to object detectors.”

Muriki said he used a real-time object detection system called YOLO (You Only Look Once) to classify objects. YOLO is known for identifying and classifying objects in a single pass.

Ved Sengupta, a computer engineering major who interned with Muriki, fine-tuned the algorithms that converted 3D images into 2D.

“This was a crucial part of making robot pollination possible,” Sengupta said. “There is a big gap between 3D and 2D image processing.

“There’s not a lot of data on the internet for 3D object detection, but there’s a ton for 2D. We were able to get great results from the converted images, and I think any sector of technology can take advantage of that.”

Sengupta, Muriki, and Hu co-authored a paper about their work that was accepted to the 2025 International Conference on Robotics and Automation (ICRA) in Atlanta.

Measuring Success

The pollination robot, built in Kousik’s Safe Robotics Lab, is now in the prototype phase. 

Hu said the robot can do more than pollinate. It can also analyze each flower to determine how well it was pollinated and whether the chances for reproduction are high.

“It has an additional capability of microscopic inspection,” Hu said. “It’s the first device we know of that provides visual feedback on how well a flower was pollinated.”

For more information about the robot, visit the Safe Robotics Lab project page.

Georgia Tech’s Qi Tang is building machine learning (ML) models to accelerate nuclear fusion research, making it more affordable and more accurate. Backed by a grant from the U.S. Department of Energy (DOE), Tang’s work brings clean, sustainable energy closer to reality.

Tang has received an Early Career Research Program (ECRP) award from the DOE Office of Science. The grant supports Tang with $875,000 disbursed over five years to craft ML and data processing tools that help scientists analyze massive datasets from nuclear experiments and simulations.

Tang is the first faculty member from Georgia Tech’s College of Computing and School of Computational Science and Engineering (CSE) to receive the ECRP. He is the seventh Georgia Tech researcher to earn the award and the only GT awardee among this year’s 99 recipients.

More than a milestone, the award reflects a shift in how nuclear research is done. Today, progress depends on computing and data science as much as on physics and engineering.

“I am honored and excited to receive the ECRP award through DOE’s Advanced Scientific Computing Research program, an organization I care about deeply,” said Tang, an assistant professor in the School of CSE. 

“I am grateful to my former colleagues at Los Alamos National Laboratory and collaborators at other national laboratories, including Lawrence Livermore, Sandia, and Argonne. I am also thankful for my Ph.D. students at Georgia Tech, whose dedication and creativity make this award possible.”

[Related: New Faculty Applies High-Performance Computing, Scientific Machine Learning Interests to Studies in Plasma Physics]

A problem in nuclear research is that fusion simulations are challenging to understand and use. These simulations generate enormous datasets that are too large to store, move, and analyze efficiently.

In his ECRP proposal to DOE, Tang introduced new ML methods to improve the analysis and storage of particle data.

Tang’s approach balances shrinking data so it is easier to store and transfer while preserving the most important scientific features. His multiscale ML models are informed by physics, so the reduced data still reflects how fusion systems really behave.

With Tang’s research, scientists can run larger, more realistic fusion models and analyze results more quickly. This accelerates progress toward practical fusion energy.

“In contrast to generic black-box-type compression tools, we aim at preserving the intrinsic structures of the particle dataset during the data reduction processes,” Tang said. 

“Taking this approach, we can meet our goal of achieving high-fidelity preservation of critical physics with minimum loss of information.”

Computing is essential in modern research because of the amount of data produced and captured from experiments and simulations. In the era of exascale supercomputers, data movement is a greater bottleneck than actual computation.

DOE operates three of the world’s four exascale supercomputers. These machines can calculate one quintillion (a billion billion) operations per second.

The exascale era began in 2022 with the launch of Frontier at Oak Ridge National Laboratory. Aurora followed in 2023 at Argonne National Laboratory. El Capitan arrived in 2024 at Lawrence Livermore National Laboratory.

With Tang’s data reduction approaches, all of DOE’s supercomputers spend more time on science and less time waiting for data transfers.

“Qi’s work in computational plasma physics and nuclear fusion modeling has been groundbreaking,” said Haesun Park, Regents’ Professor and Chair of the School of CSE. 

“We are proud of Qi and what this award means for him, Georgia Tech, and the Department of Energy toward leveraging computation to solve challenges in science and engineering, such as sustainable energy."

Previous Georgia Tech recipients of DOE Early Career Research Program awards include:

Itamar Kimchi, assistant professor, School of Physics

Sourabh Saha, assistant professor, George W. Woodruff School of Mechanical Engineering

Wenjing Lao, associate professor, School of Mathematics

Ryan Lively, Thomas C. DeLoach Professor, School of Chemical & Biomolecular Engineering

Josh Kacher, associate professor, School of Materials Science and Engineering

Devesh Ranjan, Eugene C. Gwaltney Jr. School Chair and professor, Woodruff School of Mechanical Engineering

Associate Professor Jessica Roberts and Ph.D. student Emily Amspoker of Georgia Tech’s School of Interactive Computing are working with the University of Georgia’s Marine Extension and Georgia Sea Grant in Savannah. Together, they’ve developed a prototype display that uses sonification and texture to convey sea floor habitat information from Gray’s Reef National Marine Sanctuary off the coast of Georgia.

Sonification is the process of translating data points into sound.

The display functions as a map that BLV users can follow to learn about each habitat. It is made from a wooden board with laser-cut patterns engraved into the surface. Each pattern represents information about the four types of habitats found in Gray’s Reef. Each pattern has a distinct sound that corresponds to a legend on the board, which provides an audio description of each habitat.

The four habitats are:

• Flat sand — smooth sandy seafloor with little topographic variation that provides habitat for burrowing organisms such as worms, clams, and sand dollars.
• Rippled sand — sandy bottom shaped into small wave-like ridges by currents and wave action; supports microhabitats of small invertebrates and attracts fish feeding on buried prey.
• Sparse live bottom — areas of exposed hard surfaces with scattered attached organisms like sponges, corals, and algae, offering structure and shelter for reef-associated fish and invertebrates.
• Dense live bottom — hard-bottom reef areas with abundant attached marine life, providing high biodiversity and offering food, and breeding sites for numerous species.

By allowing learners to explore these habitats, the team hopes to emphasize the importance of protecting diverse ocean habitats. 

“Our job was to figure out how we can use sounds and touch to represent each of the four habitat types so our visitors can explore the ocean without being able to see it,” she said.

Roberts said the project is critical to advance understanding of how science and informal learning can be more inclusive to those who have difficulty processing visual data displays.

The Surge That Shattered New Orleans

On Aug. 29, 2005, early reports claimed New Orleans had “dodged the bullet.” But offshore winds funneled water into the city’s canals, triggering multiple catastrophic levee failures. The Lower Ninth Ward, where most fatalities occurred, was devastated as many residents, misled by comparisons to Hurricane Camille, chose not to evacuate. 

“Katrina’s storm surge was exceptional,” says Hermann Fritz, a civil engineering professor at Georgia Tech. “In some areas, we saw water levels over 27 feet — that’s like a three-story building.”

While much attention focused on New Orleans’ levee failures, Fritz points out that the surge’s sheer height and energy would have overwhelmed even more robust defenses in some areas. “Katrina showed us that nature can produce forces beyond our engineering designs,” he says.

A Disaster of Inequality

The storm didn’t strike evenly; it exposed and deepened existing social and economic inequalities. “The disaster hit lower-income Black neighborhoods hardest,” says Allen Hyde, associate professor of history and sociology. He notes how years of segregation, disinvestment, and discriminatory housing policies left these communities uniquely vulnerable. Hyde continues, “Many homes were in low-lying, flood-prone areas, and residents often lacked access to reliable transportation, making evacuation difficult or impossible.”

Georgia’s Changing Landscape: Migration and Impact

Katrina displaced hundreds of thousands and claimed a staggering toll of more than 1,800 lives. Georgia quickly absorbed many evacuees, reshaping its demographics and infrastructure. “Hurricane Katrina led to one of the largest displacements of people due to a natural disaster,” says Shatakshee Dhongde, a professor of economics. “It changed the demographics of Georgia in measurable ways, from school enrollment to the labor market.”

The U.S. Census Bureau tracked this migration, noting spikes in Louisiana-born residents in metro Atlanta. Local school districts enrolled hundreds of new students almost overnight, while housing markets saw increased demand from families looking for permanent homes. The arrival of so many displaced residents didn’t just strain schools and housing — it reshaped the state’s economy. Dhongde notes that evacuees often brought new skills, business ideas, and networks. At the same time, the state and local governments faced the financial burden of expanding social services, healthcare, and housing assistance. 

Dhongde adds, “The impact of a disaster doesn’t stop at the water’s edge. It travels with people, and those effects can last for years.” While the influx strained services, it also enriched Georgia’s cultural and economic fabric.

Hyde notes, “Gentrification made many neighborhoods unaffordable for former residents,” and adds that many Black evacuees didn’t return to New Orleans due to economic barriers and post-Katrina gentrification. Cultural communities scattered across cities like Atlanta, Houston, and Baton Rouge.

Lessons the Levees Still Teach

For Fritz, Katrina remains a wake-up call for coastal preparedness.  “We can’t stop hurricanes,” he says, “but we can improve how we design and maintain our defenses, and how we evacuate people before it’s too late.” He warns that climate change, with its potential to intensify storms, makes those improvements even more urgent.

Dhongde sees a parallel need for social and economic planning. “Disaster preparedness isn’t just about sandbags and levees,” she says. “It’s also about ensuring the communities receiving evacuees have the resources and support systems to integrate them successfully.”

Finally, Hyde stresses the importance of engaging youth and communities in preparedness efforts. “Youth advocacy programs, like those we’re piloting in Georgia, empower young people in marginalized neighborhoods with knowledge and agency to build long-term resilience. Disaster planning must be a community effort, inclusive and forward-looking.”

Now in its second year of supporting the College of Computing, the Squarepoint Foundation continues to expand opportunities, enabling students to focus fully on their studies and pursue activities outside the classroom.  

A selection committee led by Mary Hudachek-Buswell, interim chair of the School of Computing Instruction (SCI), chose this year’s cohort.  

“These students exemplify the curiosity, talent, and determination we strive to cultivate in computer science,” Hudachek-Buswell said. “The Squarepoint Foundation Scholarships will give them the opportunity to focus fully on their studies while pursuing research and projects that have the potential to make a real-world impact.” 

The scholars have demonstrated strong leadership across campus, with all five serving as teaching assistants (TAs) and earning faculty honors. The cohort is also engaged in research and study abroad opportunities. 

Founded in 2021, the Squarepoint Foundation supports STEM education and research while partnering with organizations worldwide to expand opportunity and access.  

“We are proud to continue our partnership with Georgia Tech, as we extend our support to a number of students working towards achieving their academic goals,” said Allison Henry, Squarepoint Foundation manager.  

“The Squarepoint Foundation aims to increase access to education, ensuring that all individuals have the opportunity to pursue the degree of their choice, no matter their circumstances. We wish these talented students the best of luck as they undertake their studies and recognize them for their hard work and dedication to the STEM field."

Meet the Scholars

Maria Cymbalyuk 

Cymbalyuk studies Cybersecurity and Information Internetwork threads, focusing on how technical systems shape who is protected or exposed in digital environments. She’s interested in supporting public defenders and improving access to justice through technology. 

“This scholarship made this semester feel less financially stressful and more like I can focus on building the skills and experiences I care about,” Cymbalyuk said. “I want to use my skills to build tools and do research that supports public interest organizations.” 

Marziah Islam 

The event kicked off with a keynote address from Alex Fitzsimmons, acting undersecretary of the Office of Cybersecurity, Energy Security, and Emergency Response (CESER) at the U.S. Department of Energy. He shared insights into the administration’s work at the intersection of cybersecurity and the rapidly evolving U.S. energy sector. The first panel of the day, “Energy Innovation,” explored leaders’ perspectives on organizational innovation within the industry. With Tech undergraduate Neil Ghosh moderating the panel, Roderick Jackson, Jamie Barber, and Mark Tozzi discussed emerging energy technologies and their potential impact on the industry. 

Later, the Industry Showcase featured representatives from energy companies such as GE Vernova, Cherry Street Energy, Orion, GTA, Kimley Horn, and E4E Solutions, providing valuable networking and career development opportunities for students and professionals. A panel on “Overcoming Growing Pains” followed, with Josh Stallings, vice president of Power Delivery Strategy and Support at Georgia Power; Daniel Molzahn, associate professor in the School of Electrical and Computer Engineering (ECE); and Lisa Berry, GE Vernova’s technical director for Decarbonization and Data Centers for the Americas region. The discussion was moderated by Radhika Sharma, co-president of the Energy Club and a graduate student in ECE, and focused on current challenges facing the rapidly growing energy industry.

One of the standout moments of the conference was the Student Symposium, where 16 student researchers presented their work while competing for $1,000 in prize money sponsored by Cobb EMC. Projects ranged from residential demand management optimization studies to the challenges and viability of hydrogen combustion engines. Erik Barbosa earned first place for his research on a multiscale approach to thermochemical energy storage within buildings. Daksh Adhikari received second place for examining the mitigation of flow boiling instabilities with active flow control, and William Schertzer placed third for work using machine learning and neural networks to model anion exchange membrane degradation. 

The final event of the day, “Scaling Emergent Energy Technologies,” focused on growing the newest energy technologies within the industry. Moderated by Georgia Tech undergraduate James Lovely, the panel included Luke Bockewitz, director of business development at Kinetics; Nian Liu, associate professor and Robert G. Miller Faculty Fellow in the School of Chemical and Biomolecular Engineering; and Thomas Cuthbert, chief technology officer at Emrgy. The conference closed with a keynote speech from James Marlow, president and CEO of Southface Institute, who provided a framework for thinking through innovation and tactical advice for aspiring energy innovators and leaders.

"The level of organization and vision demonstrated by the students was outstanding,” Molzahn said. “By focusing on the evolving energy landscape and inviting experts from across the field, they created an event that sparked important conversations for our campus.” 

“It was an honor to serve as the Energy Club’s 2026 conference chair and work alongside the strong energy community at Georgia Tech,” said Jonathan Acree. “Meaningful innovation in energy depends on collaboration, and it was truly encouraging to see such an interdisciplinary group of talented students, researchers, and industry leaders come together around the shared goal of advancing our energy future.”

The conference also highlighted Georgia Tech’s role as a hub for forward-thinking dialogue on global energy challenges — and the importance of collaboration and innovation in shaping the evolving energy landscape and fostering the next generation of leaders in the field. 

Written by Georgia Tech students: Braden Queen, Orit Endalk, Eli Acree, Radhika Sharma

For many of these patients, care has long meant pain and disfigurement alongside other severe side effects, rather than receiving treatment that addresses the disease itself. This new ARPA-H award marks a potential turning point.

Lead researcher Susan Napier Thomas, Woodruff Professor in the George W. Woodruff School of Mechanical Engineering and the Parker H. Petit Institute of Bioengineering and Bioscience (IBB), has collaborated with her colleague J. Brandon Dixon, Woodruff Professor in the Woodruff School and IBB, for more than a decade on this project. The research partners are driven by the lack of meaningful treatment options available to patients.

“Funding support at this level is unprecedented,” Thomas said. “It finally gives us a chance to move beyond symptom management and toward real treatment. We’re addressing an underserved population with a huge unmet need.” 

A Gap in Care

The lymphatic system helps keep fluid moving through the body and plays a key role in immune health. When it does not function properly, fluid can build up in tissues, causing chronic pain and other long-term complications. Thomas noted that despite its toll on patients, lymphatic disease has lagged decades behind cardiovascular care in both treatment and research investment. 

“We are excited about this groundbreaking project in lymphatic engineering,” said Andrés García, IBB executive director. “By uniting interdisciplinary expertise, this work addresses long-standing challenges in lymphatic disease and moves meaningful solutions closer to the patients who need them most.”

What Comes Next

In the coming years, Thomas, Dixon, and their research partners will work toward an initial human trial, with an early focus on rare lymphatic conditions in children, as well as chronic disease in adults.

“This award reflects Georgia Tech’s growing leadership in using engineering to solve some of healthcare’s biggest challenges,” said Carolyn Seepersad, Eugene C. Gwaltney Jr. School Chair and professor in the Woodruff School. “It reinforces the Institute’s role in advancing innovations that improve patient care and strengthen Georgia’s position as a hub for health technology and biomedical innovation.”

The award was made through ARPA-H’s Groundbreaking Lymphatic Interventions and Drug Exploration (GLIDE) program led by Dr. Kimberley Steele.

This research was funded, in part, by the Advanced Research Projects Agency for Health (ARPA-H) under Agreement No. 1AY2AX000137-01. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. government.

More than 6,200 high school students across Georgia tuned in for Engineers Week 2026. Through a series of online talks, Georgia Tech researchers shared a glimpse of the technologies shaping the future.  

The partnership has driven progress in areas ranging from hypersonics to bioscience, while also deepening institutional ties beyond research. Joint faculty appointments — such as Anirban Mazumdar, who holds roles at both Sandia and the George W. Woodruff School of Mechanical Engineering — demonstrate how closely the organizations work together. The collaboration has also expanded student talent pipelines, providing more avenues for Georgia Tech students to pursue careers at the national lab.

“At its core, this partnership is about people,” said Tim Lieuwen, executive vice president for Research at Georgia Tech. “Sandia and Georgia Tech share a commitment to discovery and developing the talent, creativity, and collaboration our nation needs.”

The renewed MOU, he said, “strengthens connections between our researchers, opens new doors for our students, and builds meaningful career pathways into national service. When our communities work together to address national priorities, we not only accelerate technological advances — we expand opportunities for the people who will shape the future of our nation’s security.”

Under the new MOU, Sandia and Georgia Tech will focus on integrated research across key national security‑aligned areas, including secure artificial intelligence and computing, quantum technologies, critical minerals, advanced manufacturing, energy and grid resilience, and hypersonics. The partnership emphasizes connecting manufacturing, computation, and systems approaches directly to national security applications.

“Together, we have been solving new and unprecedented challenges in science and engineering, and now we have a great opportunity to develop this partnership,” said Dan Sinars, Sandia’s deputy chief research officer. “Our research benefits both national security and national prosperity, and keeps the country at the forefront of the world.”

With this strengthened connection, the partners aim to grow their shared research footprint through increased funding, publications, and faculty-led startups. Over the long term, Georgia Tech intends to become one of Sandia’s top hiring pipelines, ensuring that talent developed through joint research continues into national security careers.

History of the Partnership

The Institute’s collaboration with Sandia began in the mid‑2010s, when the labs selected Georgia Tech as one of its partner institutions. The first MOU, signed in 2015, formalized the relationship and outlined initial technical focus areas. 

In 2018, George White, executive director of strategic partnerships, and Olof Westerstahl,  senior director strategic initiatives in the Office of Corporate Engagement, helped expand the partnership. They launched “Sandia Day,” an event designed to introduce Georgia Tech faculty to Sandia researchers and spark new collaborations. By 2020, the organizations signed a second MOU that expanded the partnership’s technical focus areas to include energy and grid security, materials and nanotechnology, advanced electronics, advanced manufacturing, advanced computing, cyber and information security, bioscience, hypersonics, quantum information science, and engineering sciences.

The results have been substantial. Since 2018, Sandia has sponsored $35 million in research collaborations with Georgia Tech. Researchers from both institutions have co-authored 450 publications since 2016. Research activity continues to accelerate, with $1.6 million in new contracts in the past year alone. As of August 2025, Sandia employs 325 Georgia Tech alumni — a testament to the impact of the growing talent pipeline.

“We view our work with Sandia as the model for engagement with other national labs,” said White. “With the new MOU, we will continue to grow the Sandia partnership. I would like to see our footprint double in scope in the next five years.”

In these contests, a handler directs a trained dog with whistle signals to guide a small group of sheep across a field and sometimes split the flock cleanly into two groups. But sheep do not always cooperate.

Researchers at the Georgia Institute of Technology studied how handler–dog teams manage these unpredictable flocks in sheepdog trials and found principles that extend beyond livestock herding.

In a study published in Science Advances as the cover feature, the researchers applied those insights to computer simulations showing how similar strategies could improve the control of robot swarms, autonomous vehicles, AI agents, and other networked systems where many machines must coordinate their actions despite uncertain conditions.

Group Movement Dynamics

“Birds, bugs, fish, sheep, and many other organisms move in groups because it benefits individuals, including protection from predators,” said Saad Bhamla, an associate professor in Georgia Tech’s School of Chemical and Biomolecular Engineering. “The puzzle is that the ‘group’ is not a single organism. It is built from many individuals, each making local, imperfect decisions.”

When a predator threatens a herd of sheep, individuals near the edge often move toward the center to reduce their own risk, Bhamla explained. “This is ‘selfish herd’ behavior,” he said. “Shepherds exploit that instinct using trained dogs.”

From examining hours of contest footage, the researchers found that controlling small groups of sheep can be harder than managing large ones. A larger group, with more sheep protected in the center, may behave more coherently than a small group as the animals constantly shift between two instincts: “follow the group” and “flee the dog.”

“That switching behavior makes the group unpredictable,” said Tuhin Chakrabortty, a former postdoctoral researcher in the Bhamla Lab who co-led the study.

Looking closely at how dogs and their handlers guide small groups, the researchers found that unpredictability in the flock’s behavior does not always make control harder. “Under the right conditions, that ‘noisy’ behavior might actually be a benefit,” Bhamla said.

Successful Sheep Herding

Sheepdog handlers categorize sheep by how strongly they respond to a dog’s threatening pressure. Some very responsive sheep might panic under too much pressure, while others might ignore mild pressure and require stronger positioning by the dog.

The researchers observed that successful control often followed a two-step pattern. First, the dog subtly influenced the sheep’s orientation while the animals were mostly standing still. Once the flock was aligned in the desired direction, the dog increased pressure to trigger movement. The timing of those actions was critical, because alignment within a small group could disappear quickly as individuals switched between instincts.

“In our simulations, increasing pressure makes the flock reach the desired orientation faster, but how long the flock stays aligned is set mainly by noise,” Chakrabortty said. “In essence, dogs can steer the direction, but they can’t hold that decision indefinitely, so timing matters.”

Funded by Roland Ewubare, a distinguished Nigerian lawyer and corporate executive, the fellowship recognizes emerging scholars whose master’s or doctoral work meaningfully connects with societal engagement and impact. 

The program expands opportunities for graduate researchers committed to addressing real world challenges through innovative, community centered inquiry.

Nimo is a third year Ph.D. student in computer science and a graduate research assistant in the Technologies and International Development Lab led by Michael L. Best, executive director of the Institute for People and Technology and professor in both the Sam Nunn School of International Affairs and the School of Interactive Computing. He is co-advised by Irfan Essa, professor in the School of Interactive Computing.

Nimo’s research explores human centered natural language processing for healthcare, as well as multilingual AI systems in low resource contexts. Nimo develops tools to evaluate and improve the safety, robustness, and global inclusion of language technologies. His broader goal is to build AI systems that are fair, reliable, and effective across diverse languages and cultures, helping ensure that technological advances benefit communities often overlooked in mainstream AI development.

“I’m very grateful to receive this fellowship for societal impact,” Nimo said. “Thank you for this support and believing in the work, and I’m excited to keep building research that translates into real world benefit.”

Nimo earned his B.S. in electrical and computer engineering from Virginia Commonwealth University in Richmond, Virginia, and his M.S. in computer science from the University of Texas at Austin.

Read more about Georgia Tech’s research on preventing pressure injuries »

“It was a hypothesis. I was the experiment, and the hypothesis was proven true.” 

Can an inner-city student who grew up below the poverty line earn a Ph.D. and make a career in research? In theory, yes.  

The barriers are many. But literature suggests that early exposure to STEM and research opportunities can increase the odds for students in need.  

For Kendreze Holland, the idea of making it to college and earning an advanced degree was a hypothesis. Sure, theoretically it could be done — but in his own home, not everyone had even made it past high school.  

Often, the first question on the way to scientific discovery is: What if? What if a student like Holland received the right help at the right time? What if he was guided along the way by mentors who were leaders in their fields? What if he was given the opportunity to develop professional skills and make valuable connections? 

Holland asked himself: What if he could be the one to prove the hypothesis true? 

Introduction 

Holland grew up in northwest Atlanta, one of seven children raised by a single mother. Being one of so many children, most would struggle to stand out. But Holland always sought to be different.  

“My perpetual intention was to be less of a burden to my mother,” he said. “Since my mother’s education limited her abilities to help with my schoolwork, I went above the call of duty to stand out in academics.” 

His mother’s education was cut short in ninth grade so she could raise her first child, Holland’s older sister, and no one in his family had gone to college. In his mind, he had three career paths to choose from: football, hip hop, or retail.  

“Standing at a solid 5 foot 8, the first would have been difficult,” he joked. “And the latter two were not my calling.” 

Just like his mother, the course of his life changed in his ninth-grade year. For Holland, it began an academic journey he never expected.  

In 2012, he was attending B.E.S.T. Academy, an all-boys public school for grades six through 12 focused on business and STEM. Biology class was just another hour waiting to pass for the 15-year-old Holland, until the day two guest speakers from Georgia Tech walked into the room with “some weird apparatuses and mechanical chopsticks.” 

The two guests used the equipment — gel electrophoresis systems and pipettes — to show the boys what research can look like in real life. 

“This experience sparked within me a drive for science, and it was the first time I realized that I wanted to, and could, attain an advanced scientific degree,” Holland said.  

The two speakers were Manu Platt, a professor in the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, and Jerald Dumas, a postdoctoral researcher. Platt and Dumas were there to recruit students for a new program called Project ENGAGES within the Parker H. Petit Institute for Bioengineering and Bioscience (IBB).  

The program was co-founded by Platt and the late Robert M. Nerem, IBB’s founding executive director, to give students like Holland an opportunity to participate in real research projects that would hopefully plant a seed in the next generation of scientists.  

Students come from one of eight partner schools in Atlanta. Once accepted, they are connected to a Georgia Tech graduate student who mentors them and supervises their work, and they get paid to work in their assigned lab for one year.  

Project ENGAGES does more than expose students to STEM concepts and ideas. It equips them with the skills and knowledge to carry out their own independent research projects. They also have opportunities to establish connections with university faculty and industry representatives who can provide career guidance and support. 

Methods 

Though Holland didn’t meet the program’s age requirement in 2012, he applied again the next year and was accepted. During his junior and senior years of high school, he worked in Platt’s lab, where he aided with projects involving proteins, cell cultures, and antibodies.  

“Over the course of those two years, the growth I saw scientifically, professionally, and in maturity, all corroborated my belief that Kendreze was going far, and able to push past whatever goals and obstacles he comes up against,” said Platt, now the director of the Center for Biomedical Engineering Technology Acceleration housed within the National Institute of Biomedical Imaging and Bioengineering.  

Holland's experience sparked a love for science and a career-long connection with Georgia Tech. After high school, he graduated summa cum laude with a degree in chemistry from Georgia State University. As an undergraduate, he stayed connected with Tech and with IBB as a Petit Scholar, a yearlong mentorship program and research experience for top students around Atlanta. 

“I really wanted to stay close to home, and I felt like everything was in my backyard,” he said. “There are many people who come here from other places to Tech because of the great science that is going on. There’s something special about Atlanta, and I’m just getting the best of what I can from it.” 

He credits his time in Project ENGAGES with giving him the confidence and resilience to continue toward his goals. Like many others in the program, he was a first-generation college student with little to no guidance for his academic career. The holistic approach of Project ENGAGES provided professional development opportunities and standardized test preparation to ready him for life in college and beyond. 

“I knew I wanted to go to grad school, but I didn’t know I was going to do all these things,” he said. “Having that one goal sprouted a lot of side quests that just grew into something bigger.” 

After graduating from Georgia State in 2020, Holland was accepted into Georgia Tech’s Bioengineering Graduate Program as a doctoral student. In December 2025, he became the first Project ENGAGES alumnus to successfully defend his dissertation, and he is expected to graduate this spring. 

Lakeita Servance, assistant director of Outreach Initiatives at IBB, was the program manager for Project ENGAGES when Holland was accepted and cheered him on more than 10 years later as he presented his doctoral research. 

“As I sat in that room while he was defending his dissertation and sharing his research with all of us, I still reflected on that boy I saw at 16 years old,” she said. “It was this full circle moment to see him make it all the way back here. The investment we made over a decade ago has paid off in such a large way.” 

Results 

In addition to being the first in his family to go to college and earn an advanced degree, Holland received financial support from the National Science Foundation’s Graduate Research Fellowship Program; was awarded multiple prestigious fellowships, including FORD, GEM, and Herbert P. Haley; landed an internship with 3M Corporate Research Materials Laboratory; and served as a mentor in the Nakatani Research and International Experience for Students. He has published papers, led panel discussions, applied for patents, and presented his research at national conferences.   

“All that stemmed from Project ENGAGES,” he said. “And more importantly, I applied to be a mentor for the ENGAGES program.” 

Holland said some of his most meaningful experiences have come from being able to give back. He has served as a mentor, both formally and informally, to more than half a dozen students, some who come from backgrounds much like his own. 

“I wanted to give back to the program because it poured so much into me. They were able to get me all the way to the Ph.D. level, so I knew that I could use my grind to help other students.” 

Conclusion 

Having proved the hypothesis true, Holland is turning his focus to the future, considering his options in academia and corporate research while he continues to work as a postdoc at Georgia Tech.  

His research in John Blazeck’s lab focuses on cellular engineering using CRISPR gene editing technology to regulate gene profiles, meaning he and other researchers can turn certain genes up and others down to affect the way cells respond. Though he is currently working with yeast cells, he hopes that his research will translate into mammalian cells that could have more clinical applications.  

“In terms of diseases and disorders, you can use it to tune genes to help someone experiencing cancer by helping immune cells or stopping cancer cells from dividing rapidly,” he said. “You can also help other cells to survive longer, and longer cell viability means potentially a patient can survive longer.” 

What began as a presentation in a high school science class has led Holland to a future he never expected. Tequila Harris, professor in the George W. Woodruff School of Mechanical Engineering and co-director of Project ENGAGES, said his story shows others that they can do the same.  

“I believe his achievements will inspire and motivate generations of students to pursue dreams that they may not have known they had. Kendreze Holland has fundamentally shown others that there are multiple pathways to engage in STEM and that opportunities and access to advanced degrees can be attained by those willing to do the work.” 

Holland's story is symbolic of the ultimate goal for Project ENGAGES: to change the lives of talented young people who may never have had the opportunity to succeed.  

“That’s why I was so adamant about getting my Ph.D.,” he said, “to show that one could potentially overcome what they were going through to do something extraordinary.” 

Project ENGAGES is possible thanks to philanthropic support from our generous community: Donate here.

“IPaT is bringing two core competencies to both of these research pillars,” said Maribeth Gandy Coleman, IPaT’s director of research. “First, we’re advocating for and supporting the use of people-centered techniques to inform the research and co-designing the resulting system with all the stakeholders. Second, we’re also making sure we can translate this research into a real return on investment for Children’s. We are ensuring that what we design can be deployed in the hospital, and that it can be integrated with their existing systems and merge as seamlessly as possible with their existing workflows.”

Supporting Data Science, Machine Learning, and Artificial Intelligence (Pillar 1)
Pillar 1 focuses on harnessing artificial intelligence to enable more personalized and predictive pediatric care. The work aims to improve data collection infrastructure, support equitable AI practices, and build a Children’s-Georgia Tech pediatric AI collaboration that integrates advanced AI tools into clinical workflows.

Clinical Deterioration Prediction
One of the flagship projects within Pillar 1 involves developing machine learning models that can detect clinical deterioration in hospitalized children. The goal is to identify when a patient needs urgent escalation to the intensive care unit — faster and more accurately than traditional monitoring.

To achieve this, IPaT research scientists are:

• Extracting and securely transferring electronic health record (EHR) data from Children’s clinical systems.
• Training predictive models using that real‑world data.
• Building the software infrastructure required to deploy these models inside Children’s.
• Integrating model outputs directly into the EHR using Fast Healthcare Interoperability Resources communication protocols. (FHIR is an international standard for the electronic exchange of healthcare information.)

This infrastructure is intentionally designed not just for this single project but as a repeatable, scalable framework for future AI‑enabled clinical tools developed through the Children’s-Georgia Tech partnership.

AI-Enhanced Decision-Making for Hospital Operations
A second emerging project under Pillar 1 aims to address one of healthcare’s most persistent operational challenges: ICU capacity management. Seasonal fluctuations, such as surges in flu or Covid‑19 cases, can create sudden ICU demand surges and staff illnesses, which can make scheduling and staffing decisions challenging.

IPaT is building models that incorporate historical hospital activity, seasonal variation, and real‑time census and staffing levels to predict scheduling needs and help Children’s optimize resource allocation. This research is just beginning, but holds the potential for improving both care delivery and staff well‑being. More importantly, IPaT is applying user-centered design and research techniques along with the engineering work to engage with Children’s people and processes to ensure that these prediction and resource allocation models actually work, and that they will actually be used and useful in the Children’s clinical environment. 

 

Supporting Patient‑Centered Care Delivery (Pillar 2)
Pillar 2 seeks to improve pediatric outcomes by focusing on the “whole child” — physical, psychological, social, and emotional well‑being — while accounting for the needs of families, caregivers, and community environments. Particular emphasis is placed on behavioral health, rural healthcare access, and chronic illness in underserved populations.

IPaT contributes to this work on two fronts:

User Experience and Workflow Research
IPaT’s user experience (UX) researchers conduct interviews, workflow studies, and design evaluations with Children’s clinicians and staff. This human‑centered research helps shape the interfaces, processes, and technologies needed to deliver patient‑centered care in practical, usable ways. These contributions ensure that tools created through the partnership align with the realities of clinical practice.

Data Integration for Behavioral and Social Insights
For Pillar 2 research, IPaT’s secure data enclave enables Children’s EHR data to be transferred, stored, and analyzed in a HIPAA‑compliant environment. Researchers are using this infrastructure to combine clinical data with voluntarily contributed social media information from consenting participants. The aim is to explore indicators of psychological well‑being, behavioral health trends, and early warnings related to self‑harm.

A Secure, Scalable Data Infrastructure to Support Both Pillars
The IPaT secure data enclave provides a protected, secure environment for storing and analyzing sensitive patient information. It serves as the backbone connecting Georgia Tech researchers with Children’s clinical systems. Both Pillar 1 and Pillar 2 research initiatives rely on this Georgia Tech IPaT-managed secure infrastructure to safely enable:

• EHR data transfer and storage.
• Machine learning model development.
• Testing and validation workflows.
• Eventual operational deployment back into Children’s systems.

This secure, scalable architecture is central to the shared goal of translating research into actionable clinical tools.

Accelerating Pediatric Discovery 
Georgia Tech’s partnership with Children’s represents a powerful model for cross‑institutional innovation. By aligning IPaT’s strengths in human‑centered design, machine learning, and secure data systems with Children’s clinical expertise, IPaT is helping to build solutions that move quickly from concept to bedside.

As these projects grow, especially with the ongoing expansion of the clinical deterioration system and the launch of the AI-enhanced operations initiative, IPaT research scientists anticipate even greater opportunities to support Children’s mission and improve pediatric health outcomes.

Thank you to Richard Starr for providing insight about these research projects.

The Department of Energy Office of Science recently released two reports through its Advanced Scientific Computing Research (ASCR) program. The reports were produced by workshops that brought together researchers from universities, national labs, government, and industry to set priorities for scientific computing.

Professor Felix Herrmann served on the organizing committee for the Workshop on Inverse Methods for Complex Systems under Uncertainty. Assistant Professor Peng Chen joined Herrmann as a workshop participant, contributing expertise in data science and machine learning.

Inverse methods work backward from outcomes to find their causes. Scientists use these tools to study complex systems, like designing new materials with targeted properties and using past wildfires to map vulnerable areas and behavior of future fires.

The ASCR report highlighted Herrmann’s work on seismic exploration and monitoring through digital twins. Founded on inverse methods, digital twins upgrade from static models to virtual systems that accurately mirror their physical counterparts. 

Digital twins integrate real-time data sources, including fluid flows, monitoring and control systems, risk assessments, and human decisions. These models also account for uncertainty and address data gaps or limitations. 

The DOE organized the workshop to support the growing role of inverse modeling. The group identified four priority research directions (PRDs) to guide future work. The PRDs are:

• PRD 1: Discovering, exploiting, and preserving structure
• PRD 2: Identifying and overcoming model limitations
• PRD 3: Integrating disparate multimodal and/or dynamic data
• PRD 4: Solving goal-oriented inverse problems for downstream tasks

“A digital twin is a system you can control, like to optimize operations or to minimize risk,” said Herrmann, who holds joint appointments in the Schools of Earth and Atmospheric Sciences, Electrical and Computer Engineering, and Computational Science and Engineering.

“Digital twins give you a principled way to consider uncertainties, which there are a lot in subsurface monitoring. If you inject carbon dioxide too fast, you will will increase the pressure and may fracture the rock. If you inject too slow, then the process may become too costly. Digital twins help us make balanced decisions under uncertainty.”

Supercomputers, algorithms, and artificial intelligence now power modern science. However, these tools consume enormous amounts of energy. This raises concerns about how to sustain computing and scientific research as we know them in the decades ahead.

Professors Rich Vuduc and Hyesoon Kim co-authored the report from the Workshop on Energy-Efficient Computing for Science. At the three-day ASCR workshop, participants identified five key research directions:

• PRD 1: Co-design energy-efficient hardware devices and architectures for important workloads
• PRD 2: Define the algorithmic foundations of energy-efficient scientific computing
• PRD 3: Reconceptualize software ecosystems for energy efficiency
• PRD 4: Enable energy-efficient data management for data centers, instruments, and users
• PRD 5: Develop integrated, scalable energy measurement and modeling capabilities for next-generation computing systems

“I’m cautiously optimistic about the future of energy-efficient computing. The ASCR report says, from a technological point of view, there are things we can do,” said Vuduc.

“The report lays out paths for how we might design better apps, hardware systems, and algorithms that will use less energy. This is recognition that we should think about how architectures and software work together to drive down energy usage for systems.”

The Renewable Bioproducts Institute (RBI) has appointed two additional Strategic Initiative Leaders (SILs) to help shape the next phase of its research and engagement strategy: Joel Kostka and Titiksha Fernandes. 

Tang will lead the strategic vision, interdisciplinary research efforts, and internal and external partnerships at SEI, strengthening connections across Georgia Tech’s Colleges, Interdisciplinary Research Institutes (IRI), the Georgia Tech Research Institute (GTRI), and external partners to advance energy-related initiatives.

Founded in 2004, SEI is one of Georgia Tech’s IRIs and serves as a campuswide hub for energy research, education, and engagement.

Tang is the Georgia Power Professor in the School of Earth and Atmospheric Sciences. Her research and leadership focus on advancing secure, circular, and sustainable energy systems by integrating Earth, environmental, biological, materials, and sustainability sciences and innovations. She previously served as an initiative lead on critical minerals and sustainable resources at SEI as well as the associate director for interdisciplinary research at the Brook Byers Institute for Sustainable Systems.

“Professor Tang brings a strong record of research impact, leadership of complex initiatives, and a collaborative approach that will help elevate Georgia Tech’s energy research enterprise,” said Julia Kubanek, vice president for Interdisciplinary Research at Georgia Tech. “She brings deep expertise in fundamental Earth and environmental science, including water, soil, and energy research, while also leading state and regional partnerships in emerging, applied areas such as critical minerals. Most importantly, she is community-minded with excellent listening and consensus-building skills.”

As executive director, Tang will develop and communicate a unifying vision to advance interdisciplinary energy research and strategic thought leadership at Georgia Tech, integrating expertise across engineering, sciences, computing, business, design, economics, policy, and the humanities.

Tang is also the founding director of the Center for Critical Mineral Solutions and leads a multidisciplinary coalition spanning three University System of Georgia institutions. The coalition connects research, industry, and policy to build Georgia’s critical minerals innovation ecosystem, while driving resource advancement, workforce development, and economic impact.

“I'm honored to serve as the executive director of SEI. Georgia Tech’s energy research and the people behind it have always inspired me. I’m eager to listen, learn, and work alongside our community,” said Tang. “SEI connects research excellence with real-world impact, and I look forward to partnering across campus, industry, government, and communities to translate breakthrough ideas into solutions that strengthen energy security, reliability, and affordability.”

About the Strategic Energy Institute

The Strategic Energy Institute (SEI) serves as a system integrator for more than 1,000 Georgia Tech researchers working across the entire energy value chain. SEI brings together expertise to address complex energy challenges, from commercializing scalable technologies to informing long-term energy strategy and policy. Through research, education, community building, resource development, and thought leadership, SEI mobilizes Georgia Tech’s collective strengths to advance reliable, affordable, and lower-carbon energy solutions for a growing global demand.

The study finds that higher electricity rates and greater shares of college-educated residents are associated with lower household consumption, while larger homes, electric heating, and higher incomes drive usage upward. Notably, electric vehicle (EV) incentive programs correlate with increased household electricity demand—even after controlling for public charging infrastructure—suggesting these programs effectively promote EV adoption and at-home charging. In contrast, energy efficiency (EE) and renewable energy (RE) programs show no clear relationship with consumption in multivariate models. 

Read Full Story and listen to a related podcast on the EPIcenter Newspage

The study finds that EV ownership remains relatively low among households with “median” characteristics — approximately 1% of household vehicles are electric — but increases substantially when households report access to community charging infrastructure. In contrast, single‑vehicle households and households located in states without Tesla dealerships exhibit significantly lower EV ownership rates. When examining adoption intent, the authors find that access to community and workplace charging, trust in the federal government, more liberal political ideology, younger age, and urban residence are consistently associated with higher stated interest in EV adoption. Notably, single‑vehicle households express significantly greater intent to adopt one in the future, despite being less likely to own an EV today. The analysis also reveals that public transit users show elevated EV adoption intent at earlier stages of consideration, suggesting potential complementarities between transit use and personal vehicle electrification.

Read Full Story and listen to a related podcast on the EPIcenter Newspage

Civil Engineering Professor David Frost, the new president of the Geosynthetic Institute, said geosynthetic materials are an important technology for engineers working to design more resilient infrastructure to withstand the increasingly severe natural disasters of the future.

“Geosynthetics are a resilience maker,” Frost said. “Whether to enhance the strength, alter the hydraulic conductivity, limit the deformation or control various rate processes, geosynthetics inherently augment the engineering properties of natural geomaterials.”

Read the rest of the article on the School of Civil and Environmental Engineering website. 

Some labs in the School of Electrical and Computer Engineering (ECE) use liquid nitrogen and liquid helium to chill cryogenic test systems to as low as 4 Kelvins (K), or -452.47 degrees Fahrenheit (F), temperatures that rival the coldest regions of deep space.

At this point, materials and electronic devices stop behaving in familiar ways, which is exactly why ECE researchers use these extreme conditions to explore and develop new semiconductor technologies.

“Electronics are very temperature dependent,” Professor John Cressler said, whose lab houses some of these cryogenic test systems. “Whether you see it or not, every electronic you buy has a tested temperature spec associated with it.”

Current commercially sold devices, including most cell phones, are made to run between 32 F and 85 F. Researchers in ECE test across a far wider range, as they develop technology with extraterrestrial and quantum computing applications in mind.

Other ECE teams work in natural extremes, carrying instruments into polar regions where cold creates challenges that no lab can fully replicate.

Just as cold pushes athletes in different ways, it guides ECE research down its own distinct paths.

Read the full story on the School of Electrical and Computer Engineering's website.

Historically, most engineers believed the former, using heterogeneities, or differences, in materials to make materials stronger and more resilient. However, research from Georgia Tech is showing that, in some cases, heterogeneities make materials weaker and can even accelerate cracks. 

Led by School of Physics Assistant Professor Itamar Kolvin, the study, “Dual Role for Heterogeneity in Dynamic Fracture,” was published in Physical Review Letters this fall. 

While Kolvin’s work is theoretical, the results of the research are widely applicable. “Predicting this type of toughening effect helps engineers decide how much reinforcement to add to a material, and the best way to do so,” he says. “Cracks are complex — they interact with the material, change shape, and respond dynamically. All of this affects the overall toughness, which impacts safety.”

Building Strong Materials

The study found that the key to crack behavior starts at the microscopic level where the material’s microscopic structure influences how it resists cracks running at different speeds.

“Cracks propagate by breaking bonds, and that costs energy,” he explains. “On top of this, materials experience extreme deformations close to where the crack runs, which costs additional energy. In some materials, the amount of this energy cost can depend on the crack’s speed because of microscopic friction between molecules.”

Other materials, like window glass, are mostly indifferent to the crack speed. These materials are made of simple molecules, allowing a crack to propagate slowly or quickly using the same amount of energy. The researchers found that including heterogeneities can help strengthen these materials.

Materials made of more complex molecules, like polymer plastics and gels, on the other hand, are velocity dependent: it takes more energy for a crack to propagate faster. In these materials, heterogeneities are less effective at toughening, and if the crack is fast enough, heterogeneities could help it advance. “That’s something we didn’t expect when we started,” Kolvin says.

Disorder Versus Design

After discovering which types of materials can benefit from heterogeneities, Kolvin wanted to investigate the best way to add them. “Natural materials like rocks are usually very messy and disordered,” he explains, “but in engineering, heterogenous materials tend to be patterned.” For example, imagine a manufactured material: heterogeneities may be added in a grid-like or other patterned way. Now, contrast that with the irregular freckles and inclusions you might see in a rock found in a streambed.

Kolvin’s question was simple: which material was stronger? The results, again, were surprising. The disordered case — similar to what is found in nature — created the toughest material. 

Among the patterned materials the team tested, only one was as tough as the disordered case — and every other pattern tested made the material weaker.

From Lab to Landscape

At Georgia Tech, Kolvin’s lab focuses on the mechanics of materials — both solid and fluid. “We are using our expertise in physics to explore questions across different fields,” he says. “A common concept is treating materials as continua — zooming out from molecular detail to look at how materials deform and flow at the large scale.”

This current research follows suit with applications ranging from investigating the smallest material microstructures to predicting earthquake fractures. “Earthquake faults are highly disordered, and simulating these ruptures is a major challenge, usually requiring supercomputers to solve crack propagation in three dimensions,” Kolvin says. “But with the tools our study has developed, we can simulate similar conditions and large systems using just a desktop computer.”

“This opens the doors for scientists, engineers, physicists, and geologists to explore problems right from their own computer, allowing more researchers access to more tools,” he adds. “And new tools often lead to new discoveries.”

DOI: https://doi.org/10.1103/j4vb-y1ng

Georgia Tech researchers say HPC and artificial intelligence (AI) advances this year are poised to improve how people power their homes, design safer buildings, and travel through cities.

According to Qi Tang, scientists will take progressive steps toward cleaner, sustainable energy through nuclear fusion in 2026. 

“I am very hopeful about the role of advanced computing and AI in making fusion a clean energy source,” said Tang, an assistant professor in the School of Computational Science and Engineering (CSE). 

“Fusion systems involve many interconnected processes happening across different scales. Modern simulations, combined with data-driven methods, allow us to bring these pieces together into a unified picture.”

Tang’s research connects HPC and machine learning with fusion energy and plasma physics. This year, Tang is continuing work on large-scale nuclear fusion models.

Only a few experimental fusion reactors exist worldwide compared to more than 400 nuclear fission reactors. Tang’s work supports a broader effort to turn fusion from a promising idea into a practical energy source.

Nuclear fusion occurs in plasma, the fourth state of matter, where gas is heated to millions of degrees. In this extreme state, electrons are stripped from atoms, creating a hot soup of fast-moving ions and free electrons. In plasma, hydrogen atoms overcome their natural electrical repulsion, collide, and fuse together. This releases energy that can power cities and homes.

Computers interpret extreme temperatures, densities, pressures, and plasma particle motion as massive datasets. Tang works to assimilate these data types from computer models and real-world experiments.

To do this, he and other researchers rely on machine learning approaches to analyze data across models and experiments more quickly and to produce more accurate predictions. Over time, this will allow scientists to test and improve fusion reactor designs toward commercial use. 

Beyond energy and nuclear engineering, Umar Khayaz sees broader impacts for HPC in 2026.

“HPC is the need of the day in every field of engineering sciences, physics, biology, and economics,” said Khayaz, a CSE Ph.D. student in the School of Civil and Environmental Engineering. 

“HPC is important enough to say that we need to employ resources to also solve social problems.”

Khayaz studies dynamic fracture and phase-field modeling. These areas explore how materials break under sudden, rapid loads. 

Like nuclear fusion, Khayaz says dynamic fracture problems are complex and data-intensive. In 2026, he expects to see more computing resources and computational capabilities devoted to understanding these problems and other emerging civil engineering challenges.

CSE Ph.D. student Yiqiao (Ahren) Jin sees a similar relationship between infrastructure and self-driving vehicles. He believes AI will innovate this area in 2026.

At Georgia Tech, Jin develops efficient multimodal AI systems. An autonomous vehicle is a multimodal system that uses camera video, laser sensors, language instructions, and other inputs to navigate city streets under changing scenarios like traffic and weather patterns.

Jin says multimodal research will move beyond performance benchmarks this year. This shift will lead to computer systems that can reason despite uncertainty and explain their decisions. In result, engineers will redefine how they evaluate and deploy autonomous systems in safety-critical settings.

“Many foundational problems in perception, multimodal reasoning, and agent coordination are being actively addressed in 2026. These advances enable a transition from isolated autonomous systems to safer, coordinated autonomous vehicle fleets,” Jin said. 

“As these systems scale, they have the potential to fundamentally improve transportation safety and efficiency.”

The new initiative was sparked by ongoing research at Georgia Tech — and a Yellow Jacket connection: when Postdoctoral Research Fellow Hannah Youngblood’s work on exfoliation glaucoma (XFG) was featured by the BrightFocus Foundation, it caught the attention of Jennifer Rucker, an Alabama resident who was diagnosed with XFG several years ago.