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When a bartender checks an ID, they quickly verify a customer’s date of birth and identity before serving them. 

Researchers at Georgia Tech say they have developed a system to help answer that question.

Known as FIRA, the tool analyzes drone crashes to determine whether they were caused by tampered machine-learning (ML) models. The team will present its findings at the 35th USENIX Security Symposium in August. 

The research addresses a growing safety challenge as drones are increasingly used for deliveries, infrastructure inspections, and agriculture.

As drones rely more on machine learning to navigate and make decisions, they also become vulnerable to model poisoning attacks. In these attacks, adversaries manipulate an AI system during its learning phase, embedding hidden triggers that can cause failures under specific conditions.

“Machine learning drones are making more decisions in flight, which makes ML a safety-critical component of these systems,” said Yizhi Huang, Ph.D. student and lead researcher on the project. 

“When something goes wrong, investigators need a way to ask whether the model was responsible, but the model is the part of the system that no one can examine after a crash. FIRA gives investigators a way to investigate these cases by reconstructing what the model was doing during the crash. As more drones run with ML, this kind of forensic capability can help drones be used more effectively and safely.”

When a drone crashes, investigators must determine whether the cause was malicious interference, weather, or mechanical failure. Without reliable forensic tools, accountability is difficult to establish, and safety standards are harder to enforce.

FIRA identifies how drone components interact with machine learning models and monitors those interactions in real time, even with limited bandwidth.

The system functions like a flight recorder, capturing key system activity and reconstructing a timeline after a crash. It then analyzes the model’s behavior to determine whether a malicious trigger was introduced via poisoned ML training data.

In tests across multiple drone platforms and crash scenarios, FIRA identified failure causes and distinguished cyberattacks from environmental or mechanical issues.

The system does not require access to a drone’s source code, making it practical for real-world investigations.

“As commercial drone use expands, tools like FIRA could help improve accountability and trust in AI-powered systems operating in public airspace,” said Huang. 

FIRA: Enabling Automatic Forensic Investigation of Unmanned Aerial Vehicles was led by Georgia Tech’s Cyber Forensics Innovation Lab in cooperation with the Cyber-Physical Security Lab. These labs reside in the School of Cybersecurity and Privacy and the School of Electrical and Computing Engineering. 

Presented annually, the ISCA Influential Paper Award recognizes a standout paper from the ISCA Conference 18-22 years prior. Winning papers are those which have demonstrated “the most impact on the field (in terms of research, development, products or ideas) during the intervening years.”

The awarded paper, Adaptive Insertion Policies for High-Performance Caching, has been highly influential in cache replacement research. As a Ph.D. student at the University of Texas at Austin, Qureshi co-authored the paper with his advisor Yale Patt and collaborators from Intel, including Aamer Jaleel, Simon Steely, and Joel Emer.

“I’ve been working on memory systems for 20 years and if I had to pick one favorite paper, it would be this one,” Qureshi said. “Both because of the simplicity of the solution and for the impact it had.” 

When the paper was published at ISCA 2007, on-chip cache sizes were growing but still managed almost exclusively by the Least Recently Used (LRU) replacement policy.  

The paper showed that more than half of the lines in cache were discarded before they could be used under LRU policy and that this happened because the workloads being run were larger than the cache size. The team came up with a simple solution of modifying the “Insertion Policy”, such that the incoming lines were inserted at the LRU position instead of the MRU position, thus protecting the cache from thrashing patterns. The problem was that some workloads benefited from this policy while others were hurt by it. 

Instead of applying one policy across the whole cache, their solution dynamically tested two small segments against each other, each running a different policy. Whichever policy had fewer cache misses was applied to the entire cache. They named this technique Set Dueling. 

Qureshi clearly remembers the moment he knew this paper would be important. He was up late working on the project when he ran the final simulation with the solution. 

“I was so excited that I just didn’t sleep that night. I knew that the solution would get incorporated and it would change how people think about caching. I could not wait to share the results with my collaborators” he said. 

Set Dueling has since become a common technique for choosing between two competing policies when neither wins on all workloads. With close to 1,000 citations, the paper has greatly influenced research on cache optimization.

This recognition is Qureshi’s third test-of-time honor. In 2024, he received one at the IEEE/ACM International Symposium on Microarchitecture, for his work on Multi-core Cache Partitioning and in 2025, at the IEEE/IFIP International Conference on Dependable Systems and Networks, for his work on DRAM Reliability. 

Qureshi said that his goal in working in computer architecture is to have an industry impact. 

“It’s very gratifying to see that the ideas that we developed a decade or two decades ago are having an impact and being incorporated,” Qureshi said. “As a researcher, these are the best awards you can have because it serves as a validation that the work you’ve done is valuable to both industry and follow-up research.”  

When Robbie van Zyl first started working with drones, it was not for defense. It started with curiosity. 

For communities across the country, this growth hits close to home, and not without controversy. What do data centers actually deliver for the local economies that host them?
 

New research from Daniel Yue, assistant professor of Information Technology Management at Georgia Tech’s Scheller College of Business, and his co-author, Yiyang Zeng, examines how data center openings affect local jobs, wages, business activity, and electricity prices. Their findings suggest that geography plays a decisive role in whether communities see meaningful economic gains.
 

“Enormous amounts of capital are flowing into specific communities, much of it tied to new construction, while rigorous evidence on the local benefits of these facilities has been thin,” said Yue. “Community pushback has organized rapidly across the country. Our paper begins to fill that gap by providing new evidence using detailed, facility-level data paired with county-level economic indicators.”

A Historic Investment Wave

There are more than 2,500 data centers, either active or under construction, across the United States. Individual hyperscale facilities often cost more than $1 billion to construct and can consume as much electricity as a small city. Is it all worth it?

On average, Yue and Zeng found that when a data center opens, the host county sees a measurable lift: Employment rises by about 3.5%, wages by 5%, business establishments by nearly 5%, and household income by about 2%. Building permits also increase sharply, reflecting construction activity tied to new facilities.

These are real, economically meaningful gains. But Yue and Zeng discovered that these gains are much smaller than what might be expected from a large investment. And they’re not evenly distributed.

Why Metro Areas Benefit More

The researchers’ clearest finding is that metropolitan areas capture most of the economic benefits from data centers, while rural areas see far fewer spillover effects. While metro counties saw increases in employment and new business growth, non-metro counties saw no measurable gains.

The reason comes down to what economists call “agglomeration,” or economic density.

Data centers don’t operate in isolation. They rely on construction contractors, engineers, equipment suppliers, professional services, and a skilled workforce. Those connections are far easier to build in places that already have deep labor markets and established business networks.

Metropolitan areas are well-positioned to absorb the indirect spending that data centers generate. High-wage technical employees support restaurants, retail, and local services. Suppliers and contractors can scale up quickly. These spillover effects amplify the impact of the initial investment.

In rural areas, that amplification is much harder to achieve. Facilities tend to employ relatively few permanent workers — often fewer than 100 — and many specialized services are imported from outside the county. As a result, the broader economic ripple never materializes.

That doesn’t mean rural communities see no benefit at all. The research finds a small but real drop in unemployment rates in non-metro counties, and local governments may still gain tax revenue or infrastructure investments. But the sweeping job and wage growth often promised during local recruitment efforts will not likely arrive on its own.

“Location, not facility size, determines whether the local benefits show up,” Yue said.

The Hidden Cost: Electricity Prices

Yue and Zeng’s research also uncovers an important trade-off. Data centers use a lot of electricity. A single large facility can use as much power as roughly 80,000 homes. In areas where the researchers can cleanly measure price effects, electricity prices rise by about 5% after a data center enters.

“When the benefits to communities are small, even downsides like higher electricity prices that strain infrastructure will be felt by locals,” Yue shared.

Who pays for the increased cost of electricity isn’t straightforward. Yue and Zeng’s research suggests communities should ask specific questions about who pays for new infrastructure and how those costs will be distributed. Local utility companies divide costs differently among homeowners, businesses, and large industrial users, including data centers. Because cost-sharing systems vary by state, each data center development is unique.

What Communities Should Consider

As states and cities work to attract or push back against data center construction, Yue and Zeng hope their research will encourage more evidence-based decision-making.

For metro areas with strong labor markets and dense business ecosystems, data centers can deliver meaningful, though not transformative, economic gains. For rural communities, the positive impact is more complicated.

“In 10 years, communities will likely wish they had pressed harder on the quality of the decision itself, including whether the debate was evidence-based, whether their local economy was equipped to capture the gains, and whether the fine print aligned with residents' long-term interests,” Yue said. “It’s vital that communities look past flashy, headline incentive packages and focus on the details: tax abatement structures, electricity tariff arrangements, and who ultimately pays for infrastructure upgrades.”

As data centers continue to dot the American landscape, understanding where they create shared value — and where they don’t — will be critical for community leaders and policymakers alike.
 
Read More: The Local Economic Effects of Data Center Entry

As ISyE’s A. Russell Chandler III Professor, Huo’s theoretical research focuses on explaining why modern deep-learning methods preform so well. He also uses statistics, machine learning, and data science to better to better understand the reliability and fairness of learning systems.

Huo has authored more than 15 refereed journal articles since 2023 and currently has 10 papers under review. His career includes 71 journal articles, 41 conference papers, 10 book chapters, and an edited volume.

“What I find most rewarding is that rigorous theory and useful tools are not in tension – the mathematical questions that fascinate me most often turn out to be the ones that help others make sense of their data,” Huo said. “I’m deeply honored to receive this award and especially grateful to my students and collaborators, who have been at the heart of this work from the very beginning.”

Huo’s first algorithm for distance covariance remains a standard tool for testing statistical dependence and is reproduced in widely used statistical software. He co-directs the Georgia Clinical and Translational Science Alliance's Biostatistics, Epidemiology and Research Design program, which is supported by the National Institutes of Health. He also serves as a co-principal investigator on the $20 million NSF AI Institute for Agent-based Cyber Threat Intelligence and Operation.  

Huo said he is proud of the researchers he has trained. In the most recent recruiting cycle, his doctoral graduates earned tenure-track faculty offers from Georgetown University and the University of Florida. Earlier advisees hold faculty positions at the City University of Hong Kong, Korea Advanced Institute of Science and Technology, and Seoul National University of Science and Technology, while other former students are researchers at companies that include Apple, Citadel, and JP Morgan.

His recently published work includes two 2024 papers in the Journal of Machine Learning Research (JMLR). He and his students established learning guarantees for deep neutral networks, including minimax-optimal convergence rates of neural-network classifiers was accepted in 2026 by IEEE Transactions on Information Theory, and his work on the universal consistency of wide and deep networks appeared at the International Conference on Machine Learning.   

Huo’s work on the fairness of learning systems is reflected in a 2025 Journal of the American Statistical Association paper that characterizes the asymptotic behavior of the adversarial-training estimator, complementing his JMLR work on distributionally robust estimation. At the Conference on National Information Processing Systems (NeurIPS) 2025, Huo and collaborators introduced a kernel-based quantification of the accuracy fairness trade-off in representation learning, along with a new diffusion method for imbalanced text-to-image generation, while a 2026 International Conference on Learning Representations paper advanced policy optimization for large-language-model reasoning.

“I am very honored and excited to receive the NSF CAREER Award,” says Zhu. “This award will support research by my fantastic team of students and post-doctoral researchers and give me the opportunity to carry out education and outreach programs that expand our impact.”

Advancing AI Applications

Since joining Georgia Tech in 2024, Zhu’s research has focused on the mathematical foundations of machine learning and their applications in science and engineering.

With support from the CAREER Award, Zhu and his team will explore the two-way relationship between data and structure in machine learning. They aim to understand how known structures in scientific and engineering problems (e.g., symmetries or physical constraints) can help machine learning models learn more accurately and efficiently from limited data. They will also study how machine learning can uncover hidden structures directly from data, revealing patterns or principles that may not be known in advance.

The project focuses on settings where large amounts of high-quality data are difficult or expensive to obtain, as is often the case in science and engineering. Using mathematical analysis, Zhu and his team will examine how much data is required to accurately learn a model, how structural information can reduce this data requirement, and how much data is needed to reliably identify a model’s underlying structure.

According to Zhu, this approach could reduce the amount of data and time needed to build and test accurate models, leading to more reliable, interpretable, and efficient AI for scientific discovery.

“Such advances align with national AI priorities and help strengthen the mathematical foundations needed for future scientific and engineering applications of AI,” he says.

Expanding AI Literacy

Zhu believes it is important to help students understand AI, noting that they must learn how to interpret and evaluate its outputs rather than accept them uncritically. 

“AI has increasingly become incorporated into many fields of study,” he says. “Institutions must determine how to best integrate it into education while also teaching students how it works. The AI education and outreach components of my project aim to help prepare students for careers at the intersection of mathematics, computing, and science.”

Zhu’s CAREER Award will support educational initiatives at multiple levels, including new graduate and undergraduate courses on machine learning. It will also support a machine learning boot camp for high school students, organized by the School of Mathematics in collaboration with Emory University’s Department of Mathematics. The boot camp seeks to introduce students to foundational ideas in AI and machine learning, with an emphasis on the mathematical principles needed to understand and responsibly use these tools.

Housing and transportation are top priorities for many city mayors, policymakers and public policy researchers—including Brian An, an assistant professor at Georgia Tech’s Jimmy and Rosalynn Carter School of Public Policy. But in February 2021, soon after Winter Storm Uri wreaked havoc in Texas, an eight-hour power outage at his Atlanta home became an epiphany.

“While housing stability had long been on my mind, losing power drove home the importance of another piece of urban infrastructure: reliable access to electricity,” says An, an EPIcenter faculty affiliate and co-director of the Center for Urban Research. “It sparked my interest in studying the intersection of energy and urban policy to help make cities and communities not only socially equitable but also resilient to extreme weather.”

Read Full Story on the EPIcenter News Page

Communities across Georgia and the nation are navigating a range of economic and energy-related pressures. Gas prices, inflation, and the rapid growth of data centers are shaping the cost of goods and services, influencing everyday household financial decisions.

At the same time, ongoing geopolitical tensions are driving fluctuations in global oil markets and fuel prices. The expansion of AI data centers is also increasing demand for land, power, and water resources. And conflicts involving energy infrastructure in parts of the Middle East and Europe have affected supply stability.

Responding to these challenges requires careful analysis of emerging trends, supported by strong research in policy and economics. Faculty Affiliates of Georgia Tech’s Energy Policy and Innovation Center study these complex, interconnected issues affecting energy systems, costs, and access. They analyze emerging trends, evaluate policy options, and identify practical pathways forward.

Read Full Story on the EPIcenter News Page

This battle of the brains has also been playing out over the course of evolution, but not as a simple clash between old and new. 

“There was a theory proposed in the ‘50s that the brain evolved in layers starting with basic bodily functions, to emotions in the reptilian brain, leading up to sophisticated reasoning in humans,” explains Nabil Imam, an assistant professor in the School of Computational Science and Engineering and a faculty member with Georgia Tech’s Institute for Neuroscience, Neurotechnology, and Society (INNS). “This is not how an evolutionary biologist would think about the problem.”

Instead of a “new” brain layered over an “ancient” one — or even a logical brain versus an emotional one — research published in Science Advances reveals that brain evolution may come down to wiring. 

By studying the architecture of both biological and artificial brains, Imam’s team found that brain evolution is a strategic allocation of limited real estate. They propose a computational tug-of-war between two fundamentally different types of internal wiring — ones established even before birth.

This new understanding not only helps resolve a longstanding mystery in brain evolution but could also help us design more efficient AI systems.

The Problem With the “Lizard Brain”

When we refer to our “logical” or “lizard” brains, we’re really talking about different groups of brain regions. The logical brain is known as the neocortex, the brain’s outer layer responsible for vision, perception, reasoning, and other higher-level functions. For the lizard brain, the story gets a bit complicated.

“The limbic system, sometimes called the ‘reptilian brain,’ controls emotion broadly speaking — but it also has other components with distinct functions,” explains Imam. The system has separate regions for memory, smell, and navigation in addition to emotional regulation. “Why do people group all these different regions into one big system? There hasn’t been a good theory for what is common between these different circuits.”

To investigate, Imam’s team looked beyond individual regions to examine how these systems scale across species. Instead of comparing single areas based on function, the team analyzed how the limbic system and the neocortex change together across evolutionary history.

The result was remarkably consistent. When one component of the limbic system was larger, the others were also larger, while the neocortex was consistently smaller. These regions don’t vary independently. “Rather,” says Imam, “it’s a coordinated expansion of these regions across species.”

This reveals something new: The limbic system behaves not as a loose collection of functions, but as a unified network that expands and contracts as a group across evolution.

But what is driving this coordinated push and pull?

Maps Versus Barcodes

Imam argues that it comes down to how these different parts of the brain are wired before birth.

In the neocortex, neural circuits are organized as spatial maps. Areas that process touch in nearby parts of your body, like your index finger and thumb, are physically close to each other in the brain. The same is true for sight and sound. 

Wires in the limbic system, however, are not spatially organized. They function more like a bar code, firing in unique, distributed patterns to represent specific scents or complex memories.

To test whether this was an innate trait or acquired through experience, the team developed AI models for different senses. They found that when they pre-wired an AI with localized, spatial connectivity, the network was naturally very good at processing vision, sound, and touch information. Conversely, distributed, “barcode-style” networks were essential for the AI to excel at scent recognition and memory.

The Evolutionary Tug-of-War

The final piece of the puzzle explains how the size of brain components changes predictably across species. Because resources like space and energy are limited, natural selection chooses which system to prioritize.

The team simulated evolution by creating a multimodal network where the spatial and distributed domains competed for “real estate.” When the environment rewarded smell, all areas of the distributed system expanded and the neocortex shrank. When vision was rewarded, the opposite occurred.

This explains why the nine-banded armadillo, which relies on scent, has a massive limbic system, while the highly visual squirrel monkey is dominated by its neocortex. Across the 182 species studied, the research shows that brain evolution is not about adding new layers of "logic," but about strategically reallocating space between different wiring systems to support survival.

By translating this biological architecture to AI systems, engineers could create machines that learn as efficiently as the human brain, requiring far less data and energy.

“Today's artificial neural networks are trained by vast amounts data — it’s about nurture,” says Imam. “But the brain is not a blank slate that gets trained by experience. It is a mix of nature and nurture, and the nature is that pre-wired architecture.” 

“We could translate that architecture to AI systems to make it more brain-like, or make it learn or function as efficiently as the brain.”

This work was a collaboration with Cornell University and was supported by the National Science Foundation.

DOI: doi.org/10.1126/sciadv.aec6112

The latest rankings build on a record year for Georgia Tech commercialization. In 2025, the Institute reported advancing hundreds of technologies toward the marketplace, while achieving record levels of invention disclosures, issued patents, and licensed technologies. Those milestones underscore Georgia Tech’s expanding role in transforming research discoveries into products, companies, and partnerships that create economic and societal value.

“The strong performance reflects a commercialization strategy focused not only on protecting IP, but also on helping researchers translate discoveries into practical applications,” said Raghupathy “Siva” Sivakumar, chief commercialization officer at Georgia Tech. “Whether through licensing technologies, launching startups, or partnering with industry, we are building pathways that help researchers transform discoveries into real-world solutions.” 

From advanced health technologies to environmental monitoring tools and next-generation aerospace ventures, Georgia Tech innovations are reaching users and markets in increasingly diverse ways. One example is Kinemo, a startup developed through Georgia Tech research that is helping people with limited mobility regain independence through wearable assistive technology. Founded by researchers from the College of Engineering, Kinemo uses physiological sensing and small intentional movements to enable users to control digital devices. The company works closely with clinicians and patients at Shepherd Center to refine the technology and expand accessibility for individuals living with spinal cord injuries and mobility limitations.

Another example is Skopii, a startup launched from research in the lab of environmental engineering professor Ameet Pinto. The company is commercializing portable imaging and artificial intelligence technology that enables users to rapidly analyze microorganisms in water and environmental systems, eliminating the need for lengthy laboratory testing. The technology has the potential to improve decision-making for water utilities, food production systems, and environmental monitoring efforts.

As research institutions face increasing pressure to demonstrate impact beyond publications and laboratory discoveries, Georgia Tech continues to show how world-class research can translate into technologies, startups, jobs, and solutions that improve lives. The latest AUTM rankings provide another measure of that success, highlighting an innovation ecosystem that consistently moves ideas from the lab to the marketplace.

Startups such as Kinemo and Skopii illustrate the broader commercialization approach reflected in Georgia Tech's AUTM rankings. 

T1D is caused by the autoimmune destruction of insulin-producing pancreatic beta cells, requiring individuals to manage their blood glucose levels through lifelong insulin therapy. Transplanting pancreatic islets has long been investigated as a potential curative treatment, but long-lasting success in extrahepatic sites has been limited—particularly when islets are transplanted beneath the skin—due to poor blood vessel formation, immune rejection, and cellular stress following transplantation.

Vlahos’ research addresses these limitations by combining synthetic biology and tissue engineering in a new way: engineering cells to actively reshape their local environment after transplantation to make it more hospitable for the graft. Rather than relying solely on biomaterials or porous structures to support transplanted cells, the project focuses on programming the cells themselves to sense stress and respond dynamically.

Read the full story.

Artificial intelligence isn't just changing the way we think about human intelligence — it's changing the way we study the mind. "In recent years," says School of Psychology Assistant Professor Ratan Murty, "it has become very apparent to us that we have new ways to study the brain." 

Those new methods were top of mind as over 100 researchers from across Atlanta gathered for this year’s Computational Cognition Conference (CoCo Con). Hosted by Georgia Tech’s Center of Excellence in Computational Cognition (CoCo) on March 13, the conference allowed researchers from Georgia Tech and beyond working at the intersection of the mind and advanced computing to gain insights into both human cognition and artificial intelligence. 

CoCo itself is “a hub for research, education, and community” housed within the Georgia Tech School of Psychology says Robert Wilson, associate professor in Psychology and director of CoCo. Launched in 2023, the center is home to more than 50 affiliate faculty and 100 trainees across Georgia Tech, Atlanta, and beyond using computational methods to study the mind. Through chalk talks, educational programs, and conferences like CoCo Con, the center aims to rethink how we approach the study of the mind. 

The conference featured multidisciplinary talks spanning the full breadth of computation cognition — from exploration and avoidance in anxiety, what makes music memorable, the theory of mind in humans and machines, dynamic drift diffusion modeling, and the structure of memory for narratives — overall highlighting the interdisciplinary nature of the field. 

Additionally, the day featured a robust poster session highlighting work by the eight inaugural CoCo Pilot Grant recipients as well as other postdoctoral scholars, graduate students, and undergraduates doing computational cognition research at Atlanta-based institutions. In total, there were 20 posters ranging from the physics of cognition to naturalistic decision making and beyond.  

A running theme throughout the conference was the growing influence of cutting-edge technologies like artificial intelligence — and how researchers are preparing for the ethical, social and practical challenges that they bring. “The next operating system won’t run on your phone,” said DeBrae Kennedy-Mayo, senior academic professional in the Scheller College of Business and one of the conference speakers. “It will run on your brain.” With such rapidly advancing technology and the growing reach of computational cognition research, institutions like CoCo are looking to rethink the current practices of studying brain data in a modern light.  

Ending the conference was a panel discussion with researchers from across psychology, anthropology, and related fields to reflect on the future of brain research. Together, they explored what it means to do computational cognition research through the central question: What should be in the CoCo canon? 

The discussion emphasized that understanding what we are studying — and how we study it — is particularly important in an interdisciplinary field. While the narrative or canon of a field is defined by shared knowledge, skills, and history, computational cognition blurs those boundaries. Ultimately, as posed by Emory Department Chair and Professor of Anthropology Dietrich Stout, the field is “an interdisciplinary space trying to become a disciplinary space” within the vast array of sciences, technology, mathematics, and engineering.  

With or without a defined canon, the day underscored the importance of computational cognition for understanding not just how the mind works, but the future of cutting-edge technologies that shape how we approach the study of the mind. 

Through a major investment from the U.S. Department of Energy (DOE), Georgia Tech is helping accelerate the development of domestic critical minerals from unconventional and secondary resources. The $7.5 million award positions the Institute to advance supply chain solutions that span resource discovery, processing, recycling, and circular materials management.

Selected by DOE’s Office of Critical Minerals and Energy Innovation, Georgia Tech will lead the Critical Minerals in the Atlantic Seaboard Plain (CM-MAP) project. The regional effort builds on DOE’s Carbon Ore, Rare Earth, and Critical Minerals (CORE-CM) initiative and will examine potential resources across the Atlantic coastal plain.

The CM-MAP project will focus on sedimentary deposits, including kaolin, bauxite, heavy mineral sands, and phosphates, as well as legacy mining residues, coal combustion byproducts, and other unconventional and secondary resources that could support future recycling and circular economy opportunities.

Drawing on existing infrastructure, regional assets, industry bases, and scientific expertise, CM-MAP will establish a regional innovation ecosystem that supports domestic critical mineral production, recycling, and advanced manufacturing, while fostering new economic opportunities throughout the Southeast.

“This is a powerful example of how Georgia Tech brings together leading research capabilities and partnerships from industry, government, nonprofits, and national labs to address complex national challenges,” said Tim Lieuwen, executive vice president for Research. “By identifying and domestically sourcing critical minerals, we are helping secure essential supply chains, while enabling the next generation of energy and materials technologies.”

The DOE award builds on a growing network of research, industry, regional, and international partnerships led by Georgia Tech to translate scientific discovery into real-world supply chain solutions, including:

• Research leadership — Founded in 2024, Georgia Tech’s Center for Critical Mineral Solutions serves as a hub for interdisciplinary research and technology development across the Institute.
• Regional partnerships —Through the Georgia Partnership for Essential Materials, a flagship regional collaboration platform, Georgia Tech, the University of Georgia, Georgia State University, and the Georgia Mining Association convene stakeholders from across the critical minerals sector. The partnership brings together industry, nonprofit organizations, regional economic development agencies, national labs, universities, and technical colleges to connect, collaborate, and stay engaged in the latest developments.
• International engagement — A U.K.-U.S. working group extends partnerships across the Southeastern United States and Southwest United Kingdom, connecting researchers, industry leaders, and government agencies working to strengthen global supply chains.
• Georgia Critical Mineral Supply Chain Manufacturing Demonstration Center — Supported through congressional funding, the center is developing capabilities and regional supply chain demonstrations that connect resource development, materials processing, recycling, and advanced manufacturing.

As part of CM-MAP, researchers will analyze materials collected from natural deposits and industrial sites throughout the Southeast to identify their critical mineral content. The resulting large datasets will be combined with artificial intelligence and machine learning approaches to better understand and predict where resources exist, optimize extraction pathways, and inform future recovery and recycling strategies.

“This project brings together a highly collaborative team from Georgia Tech, national labs, industry partners, and research institutions across the region,” said Yuanzhi Tang, the principal investigator and Georgia Power Professor in the School of Earth and Atmospheric Sciences, with a courtesy appointment in the School of Civil and Environmental Engineering.

Tang is also the founding director of the Center for Critical Mineral Solutions and executive director of the Strategic Energy Institute. 

“Through this award, we are working to build secure and resilient critical materials supply chains, from resource discovery and characterization to processing, recovery, recycling, and advanced manufacturing, while also developing the skilled workforce needed to support these emerging industries,” Tang said. “Our vision is to create a regional innovation ecosystem that embraces both unconventional resources and circular economy approaches to maximize the value of materials already in use.”

Learn more about critical materials research and workforce development efforts at Georgia Tech by visiting the Center for Critical Mineral Solutions webpage.   

Gong Chen, assistant professor in the School of Mathematics, has received a $450,000 National Science Foundation Faculty Early Career Development (CAREER) Award to study the long-term behavior of solitons — coherent, particle-like waves observed across physics and mathematics.

While most waves spread out as they disperse (for example, ripples on water), a soliton is different: it can keep a coherent shape while moving, and in some cases, it can interact with other waves and still emerge recognizable afterward.

“Solitons are important because they appear across many areas of science, including fluid dynamics, optics, plasma physics, field theory, and models from mathematical physics,” says Chen. “From a mathematical point of view, they are a beautiful testing ground for understanding nonlinear behavior.”

Predicting Waves

According to Chen, a guiding idea in the field is that complex nonlinear waves may eventually resolve into a collection of stable solitons alongside dispersive radiation — the more diffuse portion of the wave that spreads out and weakens. Chen’s research focuses on how these waves behave over long periods of time, especially when multiple solitons interact.

“If we start with a complicated nonlinear wave, can we predict what it will look like far in the future?” asks Chen. “I want to understand not just whether a wave is stable, but how it evolves: how stability can fail, how energy is exchanged, and how complicated wave motion eventually organizes itself.” 

His work examines multi-soliton systems and more complex wave structures, including topological solitons, where long-range interactions and internal fluctuations make the mathematics more challenging. 

Chen is developing new mathematical frameworks tailored to these moving and interacting waves, including tools from spectral theory and nonlinear scattering. These approaches allow researchers to analyze wave behavior with new precision in settings where existing methods are limited.

A key part of this work involves nonlinear dispersive equations, which capture the competing effects that shape wave systems. 

“A complicated wave may contain several solitons, some radiation that spreads away, and small oscillations trapped near the solitons,” explains Chen. “Nonlinear dispersive equations allow us to ask precise questions: Which part of the wave persists? Which part disperses? How much energy is released during a collision? Does the system eventually simplify into solitons plus radiation?”

Although the work is theoretical, it strengthens the foundation for models used widely in science and engineering.

“A better theoretical understanding of solitons and dispersive waves improves the reliability of these models,” Chen says. “It helps us know when coherent structures should persist, when they should radiate energy, and when instability or collision effects may change the outcome.”

Beyond Research: Teaching and Impact

Chen plans to use the CAREER Award to integrate research and education. He is organizing a summer school focused on dispersive waves and developing new courses, including a second-level course in partial differential equations that emphasizes connections to physical phenomena.

“The CAREER Award provides the stability and long-term support needed to pursue a coherent research program rather than isolated projects,” says Chen. “For me, the award is especially meaningful because the research and education components are closely connected.”

Chen aims to help students connect mathematical theory with real-world phenomena. 

For Chen, that connection is what makes the field compelling.

“One point I would emphasize is that soliton dynamics is a place where abstract mathematics meets very intuitive physical pictures,” says Chen. “Understanding when that particle-like behavior persists, when it breaks down, and what remains afterward is both mathematically deep and scientifically natural.”

The NSF Faculty Early Career Development Program is a five-year grant designed to help promising researchers establish a foundation for a lifetime of leadership in their field. Known as CAREER awards, the grants are NSF’s most prestigious funding for early-career faculty.

The grant will support building a robotics and AI ecosystem for dexterous and mobile manipulation, enabling robots to move through environments, interact with objects, and adapt to changing conditions.

Ye Zhao, LIDAR director and associate professor in the George W. Woodruff School of Mechanical Engineering, leads the project, with Anqi Wu, assistant professor in the School of Computational Science and Engineering, serving as co-principal investigator.

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

FIFA President Gianni Infantino likened the scale of each game to that of a Super Bowl. The success of a tournament that large will rely heavily on technology, affecting everything from the players on the pitch, all the way to viewers at home.

On top of the state-of-the-art technology used at many large events, this World Cup will also see the debut of new technology. At the center of much of it will be electrical and computer engineering.

Experts from the Georgia Tech School of Electrical and Computer Engineering (ECE) weigh in on how the field is enabling the technology behind the world’s largest sporting event.

Read Full Story on the ECE News Page

Researchers at Georgia Tech say they have developed a system to help answer that question.

Known as FIRA, the tool analyzes drone crashes to determine whether they were caused by poisoned machine-learning (ML) models. The team will present its findings at the 35th USENIX Security Symposium in August. 

The research addresses a growing safety challenge as drones are increasingly used for deliveries, infrastructure inspections, and agriculture.

As drones rely more on machine learning to navigate and make decisions, they also become vulnerable to model poisoning attacks. In these attacks, adversaries manipulate an AI system during its learning phase, embedding hidden triggers that can cause failures under specific conditions.

“Machine learning drones are making more decisions in flight, which makes ML a safety-critical component of these systems,” said Yizhi Huang, Ph.D. student and lead researcher on the project. 

“When something goes wrong, investigators need a way to ask whether the model was responsible, but the model is the part of the system that no one can examine after a crash. FIRA gives investigators a way to investigate these cases by reconstructing what the model was doing during the crash. As more drones run with ML, this kind of forensic capability can help drones be used more effectively and safely.”

When a drone crashes, investigators must determine whether the cause was malicious interference, weather, or mechanical failure. Without reliable forensic tools, accountability is difficult to establish, and safety standards are harder to enforce.

FIRA identifies how drone components interact with machine learning models and monitors those interactions in real time, even with limited bandwidth.

The system functions like a flight recorder, capturing key system activity and reconstructing a timeline after a crash. It then analyzes the model’s behavior to determine whether a malicious trigger was introduced via poisoned ML training data.

In tests across multiple drone platforms and crash scenarios, FIRA identified failure causes and distinguished cyberattacks from environmental or mechanical issues.

The system does not require access to a drone’s source code, making it practical for real-world investigations.

“As commercial drone use expands, tools like FIRA could help improve accountability and trust in AI-powered systems operating in public airspace,” said Huang. 

FIRA: Enabling Automatic Forensic Investigation of Unmanned Aerial Vehicles was led by Georgia Tech’s Cyber Forensics Innovation Lab in cooperation with the Cyber-Physical Security Lab. These labs reside in the School of Cybersecurity and Privacy and the School of Electrical and Computing Engineering. 

School of Electrical and Computer Engineering (ECE) Assistant Professor Amirali Aghazadeh and Ph.D. student Daniel Saeedi have developed AstroAgents, an AI system that analyzes mass spectrometry data — detailed chemical compositions from meteorites and Earth soil samples — to generate novel hypotheses about the origins of life on the planet. 

What sets AstroAgents apart is its use of agentic AI. Unlike traditional AI systems that perform fixed tasks, this agentic system is designed to pursue a scientific goal. It draws from astrobiology literature, interprets complex data, and proposes original ideas that researchers can investigate further. 

Their paper, recently featured in the journal Nature, is opening new possibilities for how scientists explore questions that have remained unanswered for decades. 

In a special Q&A, Aghazadeh and Saeedi explain how AstroAgents analyzes space chemistry, what it’s revealing about the possible origins of life on Earth, and what they hope to explore next.

READ THE Q&A

Logically this makes sense and has been backed up by past security research. However, researchers at Georgia Tech have found hidden paths in cyber-physical systems that attackers can use to disrupt or even destroy them.

To find these hidden paths before an attacker does, the researchers built a testing tool called ICSFlux. This new tool leans on the physics used by the industrial process and maps out the system to find new threats that were once thought impossible. 

ICSFlux was deployed across 11 different programmable logic controllers in six industrial sectors, including chemical manufacturing, water treatment, power grids, aircraft, desalination, and waste processing. The process uncovered twenty genuine safety violations. 

In one case drawn from a chemical-plant simulation, an attack path uncovered by the tool drove a reactor past its safe pressure limit and into a simulated explosion. By using nothing but valid operator commands, the team took the reactor from a completely normal and stable state to critical territory. 

Because the method relies only on the physics of a process and not on the details of any one controller, the same tool worked across all six sectors without being rebuilt, and it reduced the search space by roughly 50%.

Burak Sahin, a Ph.D. student at Georgia Tech and the study's lead author, found that by sending a series of perfectly normal, fully authorized commands, intruders can slowly nudge a physical process toward a dangerous state. 

“These systems are usually judged safe as long as nobody hacks into them,'' Sahin said. “What we found is that an attacker who can send everyday commands, the same ones a normal operator sends, can patiently steer the process toward a failure. No single command looks wrong, which is exactly why the usual defenses miss it.''

Most existing tools assume an attacker can rewire the controller or change the software inside it. In the real world, those controllers are locked down and cannot be touched. ICSFlux takes the opposite and more realistic view. It treats the controller as a sealed box that cannot be opened and works only with the commands an operator is normally allowed to send.

Rather than measuring how much of a controller's software it has exercised, the usual yardstick for this kind of testing, ICSFlux measures how close the physical system is getting to an unsafe limit and steers its testing in that direction.

“Two different sensor readings can run through the exact same code and still send a reactor in completely different directions,'' Sahin said. “Looking only at the software tells you nothing about whether the physical system is safe. We had to follow the physics, not the code.''

One of the study's most important takeaways emerged when the researchers tightened the safety margins to see whether caution alone would help. Even when every command stayed within approved limits, the way the controller reacted to a steady stream of small adjustments could still cause pressure to overshoot and the reactor to fail. In other words, staying inside the rules was not always enough.

All of the team's experiments were carried out on secured, controlled test beds. The work was conducted with Georgia Tech's Cyber-Physical Systems Security Lab, whose research spans the security of cyber-physical systems from industrial programmable logic controllers to marine, automotive, and drone platforms. Georgia Tech's Cyber Forensics Innovation Laboratory, a team of researchers who work together to further the investigation of advanced cyber crimes and the analysis and prevention of next-generation malware attacks, also contributed to the paper. 

The labs are a collaboration between the School of Cybersecurity and Privacy and the School of Electrical and Computer Engineering. 

Fuzzing the Physical Space: Physics-Aware Testing of Black-Box Industrial Control Systems' was accepted to the 2026 IEEE Symposium on Security and Privacy. In addition to Sahin, the team includes Ph.D. students David Oygenblik, Mingxuan Yao, and Yizhi Huang as well as Associate Professors Brendan Saltaformaggio, and Saman Zonouz.

“It’s a tremendous honor to be recognized alongside such an inspiring group of scientists,” Manafzadeh says. “I’ve always been motivated by big, fundamental questions, and it’s exciting to see that kind of curiosity-driven research celebrated.”

Manafzadeh will join Georgia Tech in August 2026 as an assistant professor in the School of Biological Sciences. Her research investigates how joints function and how they evolved, using advanced technology to create animations of moving skeletons with sub-millimeter precision.

“My research is aimed at understanding how joints work and where they come from,” she explains. “Physicians can repair ACL injuries and perform hip replacements, but we still don’t fully understand joint mechanics at a fundamental level.” Because joints are a shared feature of virtually all vertebrates, she adds, nearly all movement — from slithering to sprinting to soaring — depends on them.

Manafzadeh first applied these methods to pterodactyls, “reanimating” the extinct animals to study how they flew. Now, her research could also open doors to personalized surgical treatments for people and new designs for bio-inspired robots.

For most, that heart-pounding uncertainty ends the moment the foot finds solid ground. But for many individuals living with conditions like stroke or spinal cord injury (SCI), that sense of disconnect is a permanent reality.

“These conditions of course have a huge effect on our ability to move around and be independent — but the other side of it is the sensory feedback that we lose,” says Matthew Flavin, an assistant professor in the School of Electrical and Computer Engineering. Most rehabilitation treatments primarily focus on restoring movement, but “even if you have motor control, if you can’t feel when your foot's touching the ground it can be really hard for you to move around safely.” 

In a new study published in Proceedings of the National Academy of Sciences, Flavin and an interdisciplinary team of researchers introduce a way to bridge this gap: a wearable “sensory substitution” system that translates foot pressure into high-tech patterns of heat and vibration they can feel elsewhere. 

The system uses high-resolution pressure-sensing insoles designed by the team, which are placed inside a user's shoes to record how their weight shifts in real-time. This data is streamed via Bluetooth to a flexible, skin-conformable array of haptic receivers worn on the forearms, a part of the body that often retains sensation in SCI. The receivers give quick pressure feedback through vibration, while also alerting the user to longer-term pressure “hotspots” through heat. 

“One of the limitations of a lot of approaches in haptics is that you're having to map a missing sense onto a completely different sense,” says Flavin. “We’re keeping the type of information that we're missing, which is the distribution of pressure, and we're just basically putting it on a different part of their body.”

Rerouting the lost sensation was key to making the device intuitive to learn. Participants were able to correctly identify the “feel” of the ground through their arms with high accuracy within a mere two-hour session. When tested with a small group of participants with stroke or SCI, the wearable significantly improved standing balance and led to steadier walking.

“What’s encouraging about these early results is that participants appeared to use the feedback in ways that supported balance and walking,” says John Rogers, a materials science and engineering professor at Northwestern University who collaborated on this study. “Our study suggests that providing pressure information through another part of the body could be a practical path for helping people compensate for lost sensation.” 

While vibration provides immediate feedback for walking and balance, the team views the thermal feedback as a tool for long-term health. Heat is a slower, low-frequency signal that could alert patients to pressure hotspots, potentially preventing diabetic foot ulcers or pressure injuries for those who are bedridden or use wheelchairs.

The small, lightweight system is completely untethered, making it suitable for use during daily activities in and outside the clinic. It’s also highly adaptable to different injury types, which is ideal for conditions as variable as stroke, SCI, and diabetic neuropathy. Placement of the haptic receivers can be adjusted based on where a patient has the most sensation, and the sensitivity of the insoles can be tailored to each patient. 

As a member of several of Georgia Tech’s Interdisciplinary Research Institutes — the Institute for Neuroscience, Neurotechnology, and Society, the Institute for Robotics and Intelligent Machines, and the Parker H. Petit Institute for Bioengineering and Biosciences — Flavin credits the project’s success to an interdisciplinary effort and deep engagement with clinicians and patients.

“This reinforces the importance of really engaging with your stakeholders very early on,” says Flavin. “If you're not continually refining that concept with those stakeholders, you quickly find that they might be looking for something that your device isn't delivering.”

With new funding from the National Science Foundation (NSF), the team is now working to make the technology even smaller and more reconfigurable, moving closer to a standard wearable for daily clinical use.

DOI: https://doi.org/10.1073/pnas.2536577123

The continued recognition highlights Georgia Tech’s research leadership in advancing energy solutions across technology, science, policy, and economics and in delivering technically advanced solutions that is scalable, secure, and sustainable for the future.

“The scale and integration of our energy ecosystem is among Georgia Tech’s great strengths,” said Executive Vice President for Research Tim Lieuwen. “A defining part of that ecosystem is the Strategic Energy Institute (SEI), our interdisciplinary research institute that brings together the talents of researchers from across disciplines to accelerate energy innovation and deliver real-world solutions.”

SEI integrates energy activities at Georgia Tech by connecting more than 1,000 researchers across the entire energy value chain and enabling collaboration with industry, government, communities, and nonprofits. SEI is deeply engaged in building community, developing resources, promoting thought leadership, and marshaling the full resources of Georgia Tech around tackling the tough energy and environmental problems and opportunities society faces.

“Georgia Tech’s energy leadership is built on the depth of our research and the breadth of our collaborations,” said Yuanzhi Tang, SEI’s executive director. “By connecting expertise across the full energy value chain, we are advancing solutions that enhance affordability, reliability, security, and sustainability.” 

U.S. News & World Report evaluates the academic research performance of universities in 51 subject areas using indicators such as publications, citations, and global and regional research reputation. Georgia Tech was assessed among 292 institutions in the U.S. and continues its strong standing in the rankings, claiming the No. 32 spot overall in the nation and No. 9 among public universities.

The seed grant program, administered by the Brook Byers Institute for Sustainable Systems (BBISS), reaches faculty members from a diverse array of disciplines due to the generous support provided by broad-based partnerships in addition to the funds provided by the Sustainability Next committee. This year’s partners are the School of Civil and Environmental Engineering, the College of Design, BBISS, the Renewable Bioproducts Institute, the Georgia Tech Research Institute, and the Institute for Data Engineering and Science.

The goal of the program is to nurture promising research areas for future large-scale collaborative sustainability research, research translation, and/or high-impact outreach; to provide mid-career faculty with leadership and community-building opportunities; and to broaden and strengthen the Georgia Tech sustainability community as a whole. The call for proposals was modeled after the Office of the Executive Vice President for Research’s Moving Teams Forward and Forming Teams programs.

This year’s seed grant awards align with the four main thematic areas in which BBISS aims to enhance Georgia Tech’s research to address some of our most pressing sustainability challenges:

• AI and Sustainability, and the Sustainability of AI Infrastructure.
• Climate Science, Technology, and Solutions.
• Healthy Environments and Sustainable Resource Use.
• Resilience and Regeneration.

The 2026 Sustainability Next Seed Grant awards are:

Forming Teams:

• Actualize Shallow Geothermal Systems for Decentralized Heating. Principal Investigator (PI): Sheng Dai.
• Building Community University Research Capacity for PFAS Testing and Treatment. PI: Ruth C. Yow. Co-Principal Investigators (Co-PIs): Joe Bozeman, Yongsheng Chen, and Ahmed Ibrahim Yunus.
• A Global Sustainability Analysis of Places “Urbanizing from Within.” PI: Gregory Randolph. Co‑PIs: Sabina Dewan, Yiyi He, John Taylor, and Celine Vacchiani‑Marcuzzo.
• Creating a Refusal Taxonomy to Explore Alternate Computing Practices. PI: Richmond Wong. Co‑PIs: Heidi Biggs and Carl DiSalvo.
• Demystifying Data Centers: Examining Georgia Tech’s Coda HPCC in the Context of Sustainability and Resilience. PI: Scott Duncan. Co-PIs: Jung-Ho Lewe and David Solano Sarmiento.
• Physical Transport of Sunlight‑Exposed Dissolved Organic Carbon in the New Arctic. PI: Lilian Dove. Co‑PI: Jennifer Bowen.

Moving Teams Forward:

• Agentic AI Digital Twins for Hurricane Resilience in Coastal Georgia. PI: Ali Sarhadi.
• CLEAR‑SE: Co‑Creating a Center‑Scale Network for Advancing Collaborative, Long‑Term Action Research on Community‑Led Resilience and Disaster Risk Reduction in the Southeast. PI: Sofía Pérez‑Guzmán. Co‑PI: Jennifer Hirsch.
• Data Center Effects on Communities in Georgia’s Black Belt. PI: Cindy Kaiying Lin. Co‑PIs: Joe Bozeman, Anthony Harding, Allen Hyde, Nicole Kennard, Jung-Ho Lewe, and Ahmed Saeed.
• Reimagining Southern Forests: Enabling Cost‑Effective Sustainable Production of High‑Value Climate‑Ready Southern Pines. PI: Caitlin Petro. Co‑PIs: Lucas Clay, Ulrika Egertsdotter, and Joel Kostka.
• Human‑Technology Collaborations: Towards Sustainable and Inclusive Food Systems. PI: Rosemarie Santa Gonzalez. Co‑PIs: Ashutosh Dhekne, Sylivia Janicki, Nicole Kennard, Yaman Sangar, and Abigale Stangl.
• Guiding Transportation with Community Action through Research, Education, and Service (GT‑CARES). PI: Rounaq Basu. Co-PIs: Sofía Pérez‑Guzmán, Jennifer Hirsch, and Scott Moffat.
• Instability‑Resolved Ocean Mixing for Climate Prediction and Climate Solutions. PI: Suhas S. Jain. Co‑PIs: Mohammad Mohaghar, and Donald Webster.
• Buildings Next: Forming a Transdisciplinary Consortium for Sustainable Building Innovation. PI: Paula Gomez. Co‑PI: Allison Bridges.
• Paper and Natural Dye Living Exhibition. PI: Anna Doll. Co‑PI: Virginia Howell.

Lieuwen, the executive vice president for Research and Regents’ Professor in the Daniel Guggenheim School of Aerospace Engineering, received the ASME medal in 2025 in recognition of his pioneering contributions to combustion, clean energy, and the science of resilient energy systems. It is the first ASME Medal ever awarded to a Georgia Tech faculty member or graduate, marking a milestone both for Lieuwen and the Institute.

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

Scientists have captured the most detailed structural images to date of a specific type of protein’s DNA repair process. The research could reveal ways to inhibit the effects of the BRCA1 and BRCA2 gene mutations that heighten the risk for breast, ovarian, and other cancers.

“This work lets us see, step by step, one mechanism by which cancer cells could manage to repair their DNA when BRCA genes mutate and fail,” says study co-author Vicki Wysocki, who is chair of the Georgia Tech School of Chemistry and Biochemistry. “By capturing this process in detail, this study opens the door to understanding how those cancerous cells survive and how treatments might disrupt that mechanism.”

Designated as a Breakthrough Article, the study Mechanism of single-strand annealing from native mass spectrometry and cryo-EM structures of RAD52 homolog Mgm101 was recently published in Nucleic Acids Research.

In addition to Wysocki, who is a professor in the School of Chemistry and Biochemistry and a professor emerita at Ohio State University, the Georgia Tech research team included co-first author Zihao Qi, a Ph.D. candidate in the Wysocki Lab.

They were joined by Ohio State researchers co-first author Carter Wheat and senior author Charles Bell, who is a professor of biological chemistry and pharmacology in the College of Medicine. Additional authors include Metro High School student Miqdad Hussain and CAS researcher Katerina Zakharova.

When BRCA Fails

Normally, BRCA genes help prevent cancer by acting as tumor suppressors — producing proteins that help repair broken DNA. When cancer cells lack the tumor-suppression function of normal BRCA genes, research has shown that a protein called RAD52 performs DNA repair.

Since RAD52 allows cancer cells to survive and replicate without tumor suppression, researchers have wondered if blocking it would kill the cancerous cells. Blocking RAD52, however, requires fully understanding its repair activities, which have been difficult to capture with even the most sophisticated techniques. 

DNA strands break every day in cells, which is why proteins exist to fix the breaks and keep cellular processes running smoothly, the team says. But because repairs must happen quickly and human proteins are often more complex than their ancestral counterparts, even the most advanced imaging equipment can’t capture every step in the process.

In order to understand RAD52 better, the research team turned to its ancestral protein, Mgm101, to observe several key steps in its DNA repair process.

A Clearer Image

The team decided to leverage multiple types of imaging. Wysocki’s lab at Georgia Tech conducted native mass spectrometry and mass photometry, using light to measure masses of protein-DNA complexes. The results showed that the ancestral protein Mgm101 assembled from a single copy of itself into a large multi-unit ring composed of 19 copies of the protein.

“This ring is essentially a template,” Wysocki explains. “The first strand of DNA can come down, and then the second strand comes on and starts being annealed to the first strand.” Annealing occurs when two single strands of DNA come together to form the characteristic double helix structure.

The findings were supported by what Bell’s lab determined using cryogenic electron microscopy, observing structures floating in solution and frozen in a thin layer of ice.

“RAD52 high-resolution structures have been determined with single-stranded DNA, but not with the two DNAs that it’s trying to anneal,” Bell says. “Its job is to bind single-stranded DNA and anneal it to its complement sequence. It’s been captured structurally, but only in a few states relevant to the reaction.”

“Here, we have more of the states along the full pathway from substrate, to intermediate and product. And the duplex intermediate is a conformation that’s never been seen before.”

Previously, researchers were unsure if this DNA repair process used one protein ring or two rings working together, the team says. Their findings show that just one ring is used — and that this is likely consistent across different species.

Paths to Treatment

Next, the team plans to try capturing the same phases of the DNA repair process with RAD52 from humans. A clearer understanding of how this family of proteins binds to DNA strands and coaxes them back together after a break provides insights for drug targets that could halt the process in cancer cells empowered by mutated BRCA genes, they say.

“It’s still a proposed mechanism: Just because we see these snapshots of the process doesn’t mean we know all the details, but we do have the best snapshots for any protein that does this single-strand annealing,” says Bell. “This focuses our strategies for drug development.”

DOI: https://doi.org/10.1093/nar/gkag320

Funding: This work was supported by the U.S. National Science Foundation and the National Institutes of Health. The cryo-EM data were collected at Ohio State’s Center for Electron Microscopy and Analysis and processed using the Ohio Supercomputer Center.

It was Ferguson’s own first manufacturing industry job at Glidden Paint in high school that tipped a row of dominoes, clearing his way out of poverty. Following next in the Hall County native’s favor was his receiving the Pell Grant and HOPE Grant, which led to his associate’s degree and first job in education.

Since then, Ferguson has spent the better part of three decades advancing workforce preparation and education access in Georgia, first as chief information officer for the Technical College System of Georgia, and now through his current roles at Tech.

“Access to higher education changed the trajectory of my life. The question now is how we build systems that create those same opportunities for others — whether someone starts their career right out of high school, earns credentials while working, or returns later to pursue advanced technical education or engineering. We need to create flexible pathways that develop talent at every stage of life.”

Steven Ferguson

Forged in Manufacturing

Ferguson was born into a family of “makers,” who got by on odd jobs and money from their small bait and tackle shop on Lake Lanier and later peddling a variety of goods. At a young age, Ferguson learned salesmanship and picked up the tinkering spirit.

“My dad was always entrepreneurial, and I think you might even consider us manufacturers, always making fishing equipment or other things,” said Ferguson. “From a very young age, I was out making jig heads, tying flies, and bagging hooks or sinkers. It was definitely in my blood.”

When he was in 10th grade, a teacher nominated Ferguson for a new youth apprenticeship program. That opportunity ultimately led to his role as an information technology apprentice at Glidden Paint, which became Ferguson’s first job in the manufacturing industry. The job was a perfect fit for Ferguson, who enjoyed learning more about the manufacturing process and the practical outlet for his computing knowledge.

He continued working there until he began studying computer science at North Georgia College and State University. Later, he transferred to Gainesville College (GC) to participate in a joint enrollment program designed to lead to eventual enrollment for a bachelor’s degree at Tech.

However, before Ferguson completed his time at GC, he had an associate’s degree and, more importantly, a job offer. GC wanted him to train others for careers in information technology.

Read Full Story on the Enrollment Management News Page

That principle is especially relevant today across the artificial intelligence (AI) landscape. As systems scale, they increasingly become harder to measure, compare, and fix, particularly within proprietary environments. 

A team led by Georgia Tech, working with collaborators across industry, has developed a new approached called Chakra to bring greater clarity to complex AI systems. 

“Imagine a room where everyone is trying to collaborate, but each person speaks a different language,” said Tushar Krishna, an associate professor in the School of Electrical and Computer Engineering, who is leading the effort. “That’s a bit like today’s AI ecosystem. The internet worked because it was built on shared practices and protocols. In AI, we’re still building that kind of common foundation.” 

The work, which Krishna leads through the nonprofit MLCommons, was released alongside a paper at the 2026 Conference on Machine Learning and Systems(MLSys) in Bellevue, Wash.

His project was selected through a competitive, peer-reviewed process involving scientific experts, state and local resource managers, and coastal community stakeholders as part of a biennial research competition sponsored by the University of Georgia’s Marine Extension and Georgia Sea Grant, supported by the NOAA National Sea Grant College Program.

“I am excited to continue to contribute to Sea Grant’s critical mission of research that strengthens the resilience of coastal ecosystems,” says Kostka.

Strengthening Coastal Marsh Restoration

Kostka’s team will examine how locally sourced Spartina plants and beneficial root-associated microbes can improve marsh restoration outcomes. The research will evaluate plant growth, stress tolerance, and field performance to identify practical, scalable strategies for supporting coastal ecosystems.

Selected projects are intended to “advance the understanding, management, and strategic use of Georgia’s coastal and marine resources,” while producing results that address coastal management needs and remain accessible to communities, according to an article from Marine Extension and Georgia Sea Grant. In addition to faculty from Georgia Tech, the 2026 research awards will support investigators from Georgia Southern University and Savannah State University.

As an energy economist, I am often asked about what contributes to gas prices and what different policies can do to affect them.

The price of a retail gallon of gas is the sum of four things: the cost of crude oil, refining, distribution and marketing, and taxes.

In nationwide figures from January 2026, crude oil accounted for about 51% of the pump price, refining roughly 20%, distribution and marketing about 11% and taxes about 18%. That mix shifts with conditions: When crude oil prices spike, that can drive more than 60% of the price; when the price drops, taxes and logistics are larger shares of the cost.

Crude Oil is the Biggest Ingredient

Because the price of crude oil is the largest element, most of the price at the pump is derived from the global oil market.

Usually, big swings in crude prices come mainly from shifts in global demand and expectations – not from supply disruptions, according to widely cited research in 2009 by the economist Lutz Kilian.

But what is happening in early 2026 with the war in Iran is one of the exceptions: a classic supply shock. Severe disruptions to shipping through the Strait of Hormuz and attacks on Middle East oil infrastructure have taken millions of barrels a day off the global market.

Most drivers generally can’t quickly reduce how much they drive or how much gas they use when prices rise, so gasoline demand doesn’t change much in the short run. That means a jump in crude costs tends to result in people paying more rather than driving less.

Refining, Regulations, and the California Puzzle

Refining turns crude into gasoline at industrial scale. The U.S. doesn’t have a single gasoline market, though. Roughly a quarter of U.S. gasoline is a cleaner-burning blend of petroleum-derived chemicals called “reformulated gasoline,” which is required in urban areas across 17 states and the District of Columbia to reduce smog.

California uses an even stricter formulation that few out-of-state refineries make. California is also geographically isolated: No pipelines bring gasoline in from other U.S. refining regions.

California’s gasoline prices have long run above the national average, explained in part by higher state taxes and stricter environmental rules. But since a refinery fire in Torrance, California, in 2015 reduced production capacity, the state’s prices have been about 20 to 30 cents a gallon higher than what those factors would indicate.

Energy economist and University of California, Berkeley, professor Severin Borenstein has called this the “mystery gasoline surcharge” and attributes it to the fact that there isn’t as much competition between refineries or gas stations in California as in other states. California’s own Division of Petroleum Market Oversight says the surcharge cost the state’s drivers about $59 billion from 2015 to 2024. It’s not exactly clear who is getting that money, but it could be gas stations themselves or refineries, through complex contracts with gas stations.

Getting the Gas Into Your Car

The distribution and marketing category covers the costs of everything involved in getting the gasoline from the refinery gate to your tank.

Gasoline moves by pipeline, ship, rail and truck to wholesale terminals, and then by local delivery truck to service stations.

At the retailer’s end, the key factors are station rent and labor, the cost to buy gasoline in bulk to be able to sell it, credit card fees of as much as 6 to 10 cents a gallon at current prices, and franchise fees paid to the national brand, such as Sunoco or ExxonMobil, for permission to put their branding on the gas station.

Most gas station operators net only a few cents per gallon on fuel itself – which is why many gas stations are really convenience stores with pumps out front. Borenstein and some of his collaborators have also documented that retail gas prices rise quickly when wholesale costs climb but fall slowly when wholesale costs drop.

The Question of Gas Tax Holidays

The federal government charges a tax on fuel, of 18.4 cents a gallon for gasoline and 24.3 cents a gallon for diesel. States charge their own taxes, ranging from 70.9 cents a gallon for gas in California to 8.95 cents in Alaska.

When gas prices rise, many politicians start talking about temporarily suspending their state’s gas tax. That does reduce prices, but not as much as politicians – or consumers – might hope. Research on past gas tax holidays has found that consumers get about 79% of the reduction in gas taxes. That means oil companies and fuel retailers keep about one-fifth of the tax cut for themselves rather than passing that savings to the public.

Gas tax holidays also reduce funding for what the taxes are designed to pay for, typically roads and bridges. That pushes road and bridge upkeep costs onto future drivers and general taxpayers.

There is an additional problem, too: Taxes on gasoline are supposed to charge drivers for some of the costs their driving imposes on everyone else – carbon emissions, local air pollution, congestion and crashes. But Borenstein has found that U.S. fuel tax levels are already far below the true cost to society. Removing the tax on drivers effectively raises the costs for everyone else.

The Jones Act: A Small Number That Adds Up

The 1920 Jones Act is a federal law that requires cargo moving between U.S. ports to travel on vessels built and registered in the U.S., owned by U.S. citizens, and crewed primarily by U.S. citizens and permanent residents. Of the world’s 7,500 oil tankers, only 54 meet this requirement. Only 43 of these can transport refined fuels such as gasoline.

So, despite significant refining capacity on the Gulf Coast, some U.S. gasoline is exported overseas even as the Northeast imports fuel, in part reflecting the relatively high cost of moving fuel between U.S. ports.

Economists Ryan Kellogg and Rich Sweeney estimate that the law raises East Coast gasoline prices by about a penny and a half per gallon on average, costing drivers roughly $770 million a year. In light of the war’s effect on gas prices, the Trump administration has temporarily suspended the Jones Act requirements – an action more commonly taken when hurricanes knock out Gulf Coast refineries and pipeline networks.

What Moves the Number

The result of all these factors is that the price that drivers see at the pump mostly reflects the global price of crude, plus a stack of domestic costs, only some of which are inefficient.

Tax holidays give a partial, short-lived rebate. Jones Act waivers trim pennies, though permanent repeal may cause more fundamental changes, such as reduced rail and truck transport of all goods, which could lower costs, emissions and infrastructure damage associated with cargo transportation. Harmonizing fuel blends across states and seasons may lower prices somewhat, but likely at the expense of increased emissions.

Ultimately, the best protection against oil price shocks is a more efficient gas-burning vehicle, or one that doesn’t burn gasoline at all. In the meantime, the best I can offer as an economist is clarity about what that $4.30 actually buys.

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

Fiona Ryan, a Ph.D. student in Georgia Tech’s School of Interactive Computing, is pioneering research that tracks and predicts user gaze from a first-person perspective in 3D environments.

Currently, most AR devices react to where users look, playing catch-up. Ryan’s method could give these devices a heads-up and make the user experience more seamless.

“It allows an AR system to anticipate what the person will interact with next and where they’re going to look next so it can proactively render the experience,” she said.

Ryan is the lead author of the paper Forecasting 3D Scanpaths in Egocentric Video, which she will present next week at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) in Denver.

While there is existing research on predicting user gaze from 2D still images, her work is the first to address the issue through a 3D framework.

“Because we live in a 3D world and people are dynamically moving around from multiple points of view, we need to predict gaze in 3D rather than 2D,” she said. “What we’re seeing is a path of the person’s attention in 3D through space. Our paper is the first to attempt to model this.”

Ryan conducted most of the research while interning at Meta, where she used data from Meta’s Aria Digital Twin dataset. The dataset contains first-person video footage of users interacting with objects in an apartment.

“We chose that dataset because it has a high-fidelity 3D reconstruction of a full environment, which helps us get a ground-truth 3D gaze,” she said. “We can trace eye movement and see how it intersects with the environment.”

A video demonstration of Ryan’s work shows her software tracking a user’s path toward a table with a cup on it. Once the user picks up the cup, the software correctly predicts the direction the user will turn next.

“When we look at a scene, we don’t take in everything in full detail all at once,” she said. “We fixate on certain areas, and our gaze is a sequence of fixations, which might depend on what we’re trying to do. If we want to pick up a cup, we might look toward that and then the next step would be looking at where we’re going to put it down.”

Ryan said the software can predict, on average, up to three seconds into the future — and as far as 10 seconds in some cases. That’s enough time for the AR system to proactively render a more enhanced environment.

“We’re not looking that far into the future right now, but it would be interesting to explore longer forecasting windows,” she said. “I think potential futures would diverge pretty quickly, so we’re trying to explore what can reasonably be predicted from a short segment of a person looking and moving through space.”

Ryan said her paper served as a proof-of-concept, and that there is still much future work to be done. She already has some ideas.

“I think future models can include different scenarios to help narrow down possibilities. Sometimes a person’s gaze stays on one thing for a long time. If we know what someone is trying to do, we’ll have a better idea of the likely path their attention might go.”

There could also be future implications for her work in robotics research.

“It could potentially be used for training algorithms for robots to emulate active human perception. If we can understand what a person looks at as they perform a task, we could use that to facilitate a robot learning to do that same task.” 

Fiona Ryan, a Ph.D. student in Georgia Tech’s School of Interactive Computing, is pioneering research that tracks and predicts user gaze from a first-person perspective in 3D environments.

Currently, most AR devices react to where users look, playing catch-up. Ryan’s method could give these devices a heads-up and make the user experience more seamless.

“It allows an AR system to anticipate what the person will interact with next and where they’re going to look next so it can proactively render the experience,” she said.

Ryan is the lead author of the paper Forecasting 3D Scanpaths in Egocentric Video, which she will present next week at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) in Denver.

While there is existing research on predicting user gaze from 2D still images, her work is the first to address the issue through a 3D framework.

“Because we live in a 3D world and people are dynamically moving around from multiple points of view, we need to predict gaze in 3D rather than 2D,” she said. “What we’re seeing is a path of the person’s attention in 3D through space. Our paper is the first to attempt to model this.”

Ryan conducted most of the research while interning at Meta, where she used data from Meta’s Aria Digital Twin dataset. The dataset contains first-person video footage of users interacting with objects in an apartment.

“We chose that dataset because it has a high-fidelity 3D reconstruction of a full environment, which helps us get a ground-truth 3D gaze,” she said. “We can trace eye movement and see how it intersects with the environment.”

A video demonstration of Ryan’s work shows her software tracking a user’s path toward a table with a cup on it. Once the user picks up the cup, the software correctly predicts the direction the user will turn next.

“When we look at a scene, we don’t take in everything in full detail all at once,” she said. “We fixate on certain areas, and our gaze is a sequence of fixations, which might depend on what we’re trying to do. If we want to pick up a cup, we might look toward that and then the next step would be looking at where we’re going to put it down.”

Ryan said the software can predict, on average, up to three seconds into the future — and as far as 10 seconds in some cases. That’s enough time for the AR system to proactively render a more enhanced environment.

“We’re not looking that far into the future right now, but it would be interesting to explore longer forecasting windows,” she said. “I think potential futures would diverge pretty quickly, so we’re trying to explore what can reasonably be predicted from a short segment of a person looking and moving through space.”

Ryan said her paper served as a proof-of-concept, and that there is still much future work to be done. She already has some ideas.

“I think future models can include different scenarios to help narrow down possibilities. Sometimes a person’s gaze stays on one thing for a long time. If we know what someone is trying to do, we’ll have a better idea of the likely path their attention might go.”

There could also be future implications for her work in robotics research.

“It could potentially be used for training algorithms for robots to emulate active human perception. If we can understand what a person looks at as they perform a task, we could use that to facilitate a robot learning to do that same task.” 

With more than 150 attendees from industry, academia, and research organizations, the event’s high-level engagement underscored the urgency of critical issues facing the energy sector today, including the surging electricity demand, resiliency of the grid, and evolving supply chains, as well as the value of a dedicated space for candid, solutions-oriented dialogue.

“INTERSECT 2026 demonstrated the power of bringing together leaders who are actively shaping the future of energy,” said Laura Taylor, director of EPIcenter. “What began as a forum to explore emerging ideas has grown into a critical platform for aligning perspectives and advancing actionable solutions across the Southeast.”

This year’s program focused on real-world implementation challenges, including managing large-scale load growth and coordinating infrastructure investments to meet demand reliably and affordably. Panels featuring leaders from utilities, global energy corporations, and research organizations emphasized the importance of aligning strategy across sectors to ensure that the Southeast remains competitive and resilient.

Chris Womack, chairman, president, and CEO of Southern Company, delivered the keynote address, highlighting the unprecedented scale of current energy demands. 

“Meeting this moment requires us to think differently — serving growth while ensuring reliability, resilience, and long-term value for our customers and communities,” said Womack.

Launched in 2017, the inaugural INTERSECT conference marked the launch of EPIcenter itself and established Georgia Tech’s commitment to connecting research, industry insight, and policy development. It focused on the need to bridge the gap between rapidly advancing technologies and slower-moving regulatory and market frameworks, a theme that continues to shape its mission today. 

As INTERSECT 2026 concluded, participants pointed to a shared takeaway: With its industrial base, growing population, and integrated energy systems, the Southeast is uniquely positioned to lead in the next phase of the energy transition. With AI-driven power demand and grid infrastructure playing a significant role going forward, it is imperative to bring together the right voices to shape policies and strategies that will connect ideas to action.

The highly competitive Laboratory Placement program is a paid opportunity offered through the U.S. Department of Energy’s Science Undergraduate Laboratory Internships. It provides students from a wide range of disciplines an opportunity to contribute to cutting-edge research at leading facilities, including Argonne National Laboratory, Ames National Laboratory, Lawrence Berkeley National Laboratory, National Laboratory of the Rockies, Oak Ridge National Laboratory, Princeton Plasma Physics Laboratory, and Savannah River National Laboratory.

The program’s 2026 cohort includes 16 Georgia Tech students from disciplines such as artificial intelligence, materials science, aerospace engineering, nuclear engineering, chemical engineering, mechanical engineering, and physics. Their research placements reflect the interdisciplinary nature of today’s scientific challenges, with projects covering bioinformatics, high-energy and condensed matter physics, accelerator science, environmental management, and advanced materials.

Many of the internships are closely aligned with national energy priorities, with students working in research areas including nuclear energy, hydrogen and chemical systems, materials for energy applications, plasma and fusion sciences, and complex engineered systems.

“Georgia Tech’s deep engagement with the national laboratory system creates unparalleled opportunities for our students to contribute to the future of energy,” said Yuanzhi Tang, executive director of the Strategic Energy Institute. “By connecting interdisciplinary talent with world-class research environments, we are not only advancing discovery but also shaping the next generation of leaders who will drive secure, sustainable, and resilient energy systems.”

Working alongside national lab scientists, students will not only gain access to world-class facilities but benefit from mentorship and professional networks, while contributing to research critical to national security, economic competitiveness, and a more sustainable energy future. 

“These internships demonstrate the strength of Georgia Tech’s relationships across the federal research ecosystem,” said Robert Knotts, executive director of Federal Relations in the Office of Institute Relations. “They provide a direct pathway for students to engage in public service through mission-driven research at national laboratories — while strengthening connections that are vital to advancing national priorities in energy, security, and innovation.”

Traditional sleep monitoring systems often rely on bulky equipment and nasal cannulas — small tubes inserted into the nostrils to measure airflow. While effective, these systems can be uncomfortable, intrusive, and difficult to tolerate overnight, limiting their use for long-term monitoring at home.

Now, researchers led by W. Hong Yeo, Peterson Professor in Pediatric Research at the George W. Woodruff School of Mechanical Engineering, have developed a soft, wireless nasal patch that could offer a more comfortable alternative for monitoring breathing during sleep.

The technology, described in a recent study published in Proceedings of the National Academy of Sciences (PNAS), uses ultrathin, skin-like wearable electronics to detect subtle movements of the nose caused by breathing without tubes, wires, or direct airflow measurements.

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

“Her outstanding research accomplishments and contributions to the School and Georgia Tech led to this selection,” said Professor Christopher W. Jones, the John F. Brock III School Chair in ChBE@GT.

The $20,000 in discretionary funding from this one-year fellowship will support Jamali’s research activities focused on developing new tools for in situ liquid-phase transmission electron microscopy, stochastic thermodynamics, and nanoscience-based platforms.

The Spencers established the endowment from which the term fellowship funding comes in 2017. This endowment will eventually lead to the establishment of a professorship in ChBE@GT.

“Bob Spencer is a successful alumnus who has remained connected to our chemical engineering program,” according to Jones. “His family’s gift will allow ChBE@GT to support an early career professor at a critical stage of their development—the crucial years just before their promotion and tenure review. We are grateful for their support and generosity.”

Read Full Story on the ChBE Newspage

This concept of “lab tool as lab assistant” is the premise of a recent paper in npj | Computational Materials titled “Thinking Microscopes: Agentic AI and the Future of Electron Microscopy,” by Vida Jamali, assistant professor the School of Chemical and Biomolecular Engineering; Amirali Aghazadeh, assistant professor in the School of Electrical and Computer Engineering; and Josh Kacher, associate professor in the School of Materials Science and Engineering. 

In the paper, the team introduces the concept of “thinking electron microscopes,” in which agentic AI systems are directly integrated with the instrument. This allows microscopes to move beyond their conventional role as characterization tools and toward functioning as co-scientists for human users.

Drawing on advances in specialized large language models, or LLMs, that demonstrate their ability to collaborate, reason over data, and integrate prior knowledge, the team envisions specialized LLM-based agents assigned to specific roles and areas of knowledge expertise. By explicitly incorporating domain knowledge into specialized agents and distributing information across multiple agents with focused expertise, the approach enables parallel evaluation of competing hypotheses, clearer separation of roles — such as planning, simulation, and critique — and more transparent and robust reasoning.

Within the experimental pipeline, these agents can analyze materials’ properties, physical data, chemical processes, and other relevant parameters. They could also collaborate with an agent that specializes in experimental design, refining iterative closed-loop experimentation, and real-time scientific discovery.

Although the research focuses on AI collaboration, the team notes that human researchers must retain accountability for the accuracy and integrity of both the experimental process and the results reported. This oversight begins with advocating for greater open access to research materials in all formats, building community-driven data repositories, and adopting standardization in how experimental parameters and metadata are reported. Equally important, researchers should be willing to report data from failed experiments as well as successful outcomes. Finally, organizations should work together to standardize secure APIs that enable shared, remote access to infrastructure across distances.

We see this as a step toward scientific instruments that do more than acquire data; systems that can reason over experiments, adapt measurements, and participate in the scientific discovery process alongside researchers. - Vida Jamali, assistant professor the School of Chemical and Biomolecular Engineering

The team is already developing these systems by connecting cloud-based, agentic infrastructures to microscopes at the Institute for Matter and Systems at Georgia Tech. With the addition of agentic AI, the goal is to accelerate discovery and engineering of new nanoscale materials for energy and quantum applications, as well as advance capabilities in cryo-electron microscopy and structural biology. These tools can optimize data collection, link real-time microscope observations with structural models of proteins, and dynamically adjust and prioritize experiments. The team sees this work as the first step toward the next generation of “thinking” electron microscopes, as well as an advancement in scientific discovery across domains. 

 - Christa M. Ernst

This research is supported by the Institute for Data Engineering and Science and the Institute for Matter and Systems

Original Publication
Jamali, V., Aghazadeh, A. & Kacher, J. Thinking microscopes: agentic AI and the future of electron microscopy. npj Computational Materials 12, 149 (2026). https://doi.org/10.1038/s41524-026-02077-y

The Shurl and Kay Curci Foundation funds science-based projects with an emphasis on advancing a healthy and sustainable future for humans, focusing on early-stage research with far-reaching and lasting implications.

“This is a special program that supports junior faculty with particular creativity,” says School of Biological Sciences Chair Todd Streelman. “The best part for me is that representatives from the Curci Foundation visit our campus and conduct in-person interviews, showing they value both the projects and the young scientists.”

Nathan McDonald: Understanding Synapses and Engineering their Repair

The McDonald Lab studies the fundamental biology of synapses, the tiny structures that allow neurons to communicate. Their research focuses on understanding how the nervous system and brain develop, specifically how hundreds of billions of neurons form and connect through trillions of synapses – and how they continue to change throughout adult life. 

“What’s exciting about the grant is that it allows us to apply that knowledge and explore whether and how we might control synapse formation,” explains McDonald.

The McDonald Lab will examine whether the molecular processes neurons use to build synapses during early development can be reactivated later in life.

If successful, the new research could have implications for aging and neurodegenerative conditions in which synapses are lost, potentially revealing ways to repair specific synapses and restore their function.

“Many researchers are interested in repairing or regenerating synapses. Most approaches so far have focused on pharmaceuticals – using drugs to influence synaptic strength. What makes our approach unique is that we are trying to leverage the developmental machinery that neurons already have,” he explains.

McDonald and his team are working with Caenorhabditis elegans, a microscopic roundworm widely used in neuroscience research. The organism offers a simplified, tractable system for examining how synapses are built, dismantled, and potentially rebuilt.

“If we can demonstrate proof of concept in a simple nervous system, that opens the door to scaling these approaches to more complex models,” explains McDonald.

He plans to use the Curci funds to support students and staff as they explore these new methods for engineering synapse formation.

“The work has the potential to be developed into something more translational and applicable to disease,” says McDonald. “These sources of funding are incredibly important for launching new research directions.”

Farzaneh Najafi: Exploring Sleep and the Cerebellum’s Role in Cognitive Health

The Farzaneh Najafi Lab examines predictive processing, how the brain makes and learns predictions about the world. Najafi’s research focuses on deepening understanding of how sleep supports learning and cognitive health across the lifespan. 

Najafi’s Curci-funded research will examine how the brain uses sleep to reorganize itself after learning, with a particular focus on the cerebellum, a region that contains nearly 80 percent of the brain’s neurons. By identifying changes in cerebellar activity during sleep, her work has the potential to improve early detection of neurological disorders.

“We know that sleep stabilizes memories in areas like the cortex and hippocampus, but we know very little about what sleep does in the cerebellum,” says Najafi. “This grant allows us to bring sleep, cerebellar circuitry, and learning together.”

Najafi and her team will combine behavioral experiments with high-resolution imaging to study how cerebellar circuits and synapses change across wake and sleep.

“We’re looking at cerebellar activity during sleep at the circuit and synapse level to see how learning-related changes unfold,” explains Najafi.

In some cerebellar disorders, sleep disturbances can appear five to 10 years before motor symptoms begin. By identifying early changes in cerebellar activity during sleep, Najafi’s research could help pinpoint neurological disease at a stage when intervention may still be possible.

Curci funding will allow Najafi’s lab to collect foundational data needed to establish the first mechanistic links between sleep, cerebellar activity, and long-term brain health.

“Many traditional funding mechanisms are hesitant to support these kinds of higher‑risk directions, especially early on, but this award makes it possible to pursue a new and promising line of inquiry,” says Najafi.

The NSF CAREER Award supports early-career faculty who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization. The award provides $662,045 over five years to support Sanders’ project, Patterning Hard Interlocking Particles to Achieve Soft Materials and Structures.

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

Between orchestra concerts, magic shows, and yo-yo exhibitions, Li thrives in the limelight. In fact, not much rattles his nerves considering the five years of pressure he endured studying computational neuroscience at Tech.

Before he returns to New York City to continue building brain-interface technologies at Meta, we caught up with Li to learn how he keeps such a cool head at Georgia Tech and beyond.   

Graduate: Chengrui Li

Research Interests: Computational neuroscience, eye-tracking experiments and data analysis, statistical machine learning

Education: Ph.D. in Computational Science and Engineering (CSE)

Faculty Advisor: School of CSE Assistant Professor Anqi Wu

What persuaded you to attend graduate school at Georgia Tech?

My undergraduate was at Sichuan University in China. We knew that the most cutting-edge technology and research were in the United States, so I participated in an undergraduate exchange program at the University of Tennessee, Knoxville, during my third year. 

I wanted to pursue a Ph.D. in neuroscience while also becoming very proficient in math and computer science (CS). This led me to apply to the CSE Ph.D. program over others. Georgia Tech’s CS ranking is very high, and the CSE program is very interdisciplinary, which matched my expectations super well. I did attain a solid education in math and CS at Georgia Tech. I also advanced my interest in neuroscience and its application by studying mathematical models and algorithms.

What research project from Georgia Tech are you most proud of?

My variational importance sampling paper is a favorite. That one was based heavily on statistical inference. I spent many hours working through complicated derivation calculations, often half-awake and half-asleep after several late nights. 

This paper confirmed to me, though, that innovative research requires both hard work and inspiration, and that this endeavor can be rewarding. The paper was selected as a top 5% spotlight paper at ICLR 2024, a world-leading conference on artificial intelligence research.

Could you share more about your role as a research scientist at Meta?

I have been working on Meta’s electromyography (EMG) neural band. This next-generation human-computer interaction device connects with and navigates Meta’s AI glasses.

With the neural band, you can use finger gestures to control the display content you see through the glasses, like swiping your thumb to scroll the screen, or writing on your lap as if you had a pen in your hand to send WhatsApp messages.

How did your Georgia Tech education prepare you for this role?

By pursuing my Ph.D., I am more proficient in critical thinking, math, coding, and presentation. During my interview, I demonstrated these skills and provided my publication records. This helped me land an internship, enabled my success in that role, and led to a full-time position. Additionally, my background in computational neuroscience best matched the work on the EMG neural band team at a big tech company.

What advice would you give someone interested in graduate school?

First, be clear whether a bachelor’s or master’s degree meets your work needs, or if you are truly interested in a scientific research topic. This interest should be based on your own passion, not the current trends. Interest is an important factor in deciding to pursue a Ph.D. because you have to like the topic and like it for a long time. A Ph.D. will require you to dive deep into a subject you must be genuinely curious about.

Second, we are in a new era with rapid advances in information technology. Time is an invaluable resource and is shaped by technology. You have to think more about your time, consider where and how you spend it, and embrace ways to use it more efficiently. 

Can you tell us more about your hobbies and how you keep up with them?

I started learning violin when I was five years old, and magic tricks when I was 11. The brain is a supercomputer suitable for functional computation. Our brain is an interface between the objective and subjective, where computation plays a core role in integrating these exact mechanics into interpretations of the world. This realization was one of the important factors that inspired me to pursue my Ph.D. research in computational neuroscience.

Another comparison I’ve learned after playing violin for 23 years is that the cochlea in our inner ear is a fast Fourier Transformer that simultaneously computes the aesthetic of music for us. Performing magic tricks for 17 years taught me that all the occurrences of seemingly low-probability magic phenomena are achieved by either letting it be a certain event or exhausting all possibilities.

I also have other hobbies, like yo-yo balls. I enjoy performing all these skills in front of audiences. Performing brings me satisfaction when I see excitement and happiness from the people I entertain. I am very grateful to my parents for their cultivation and encouragement in doing things that bring me fulfillment. They taught me to be curious and explore my interests, to enjoy pastimes, and instilled the habit to not give up my passions. These were not secondary things that distracted me from coursework or Ph.D. research, but rather complementary parts of my life that bring out the best in me.

What is your favorite Georgia Tech memory?

I have a lot. For my research, I debated frequently with Anqi Wu, my advisor. These often went late into the night to defend my stances. These challenged my beliefs and made me a stronger scholar, for which I am grateful to Anqi for her time and patience.  

I also enjoyed performing in the Georgia Tech symphony orchestra with our great conductor, Chaowen Ting. I was involved with the Georgia Tech Chinese Students and Scholars Association, where I showcased magic and yo-yo performances at organization events.

Jain is one of 15 scientists selected this year for “their promise to change their fields by solving nature’s puzzles in a broad range of fields and develop next-generation technologies that can reveal biological function,” according to a Searle Scholars Program press release. 

“We are honored to be part of the Searle Scholars Program,” Jain says. “For a young lab with ambitious goals, this kind of recognition means everything. It gives us the confidence and resources to pursue high-risk, high-reward questions that could one day make a real difference for people affected by neurodevelopmental disorders.”

Jain received his Ph.D. in molecular and cellular biology from the University of Arizona and completed his postdoctoral work at the University of California, Los Angeles. He joined Georgia Tech in 2024.

That is clearest on the surface, where Shah studied how public statements by businesses and financial institutions shape market behavior. At a deeper level, though, his success was buoyed by support from professors and his mentorship of younger students.

Shah’s ability to connect and invest in others led him to partner with Georgia Tech colleagues and start a financial technology business. He returns to campus this week to officially graduate from Tech, giving us a chance to catch up about his grad school experience and life as an entrepreneur.

Graduate: Agam Shah

Research Interests: Quantitative and computational finance, artificial intelligence, natural language processing, large language models (LLMs)

Education: Ph.D. in Machine Learning, home unit in the School of Computational Science and Engineering (CSE)

Faculty Advisors: Scheller College of Business Professor Sudheer Chava and School of CSE Associate Professor Chao Zhang

What persuaded you to attend graduate school at Georgia Tech?

Georgia Tech’s dedicated College of Computing strongly appealed to me. I was particularly drawn to the interdisciplinary nature of its machine learning Ph.D. program and the School of Computational Science and Engineering, both of which align well with my research interests. 

What research project(s) from Georgia Tech are you most proud of and why?

I am proud of all 20-plus research papers I have had the opportunity to contribute to at Georgia Tech. However, if I had to choose one, it would be my work on Federal Open Market Committee (FOMC) text analysis, which was also highlighted in the news. 

This work is not only well-cited in academic literature, but the language model developed in the paper is also actively used by economists at many of the world’s top central banks, including researchers at the FOMC and the Bank of England. It is also used by leading financial institutions such as BlackRock and Daiwa Securities. Since its release, the model has achieved over 100,000 downloads on Hugging Face. 

What can you tell us more about your startup, ZettaQuant?

 ZettaQuant aims to solve one of the biggest challenges in using LLMs and agents: working effectively with massive underlying datasets. We serve as a layer between raw data and LLMs, helping distill billions of tokens into the relevant context that models can use. 

As a deep-tech startup, we are actively engaging with industry practitioners to better understand how to design and engineer our system to integrate seamlessly with their evolving AI workflows. Given the complexity of the problem we are tackling, particularly in advancing document intelligence systems, we are currently very focused on research and foundational development. 

How did your Georgia Tech education prepare you for starting ZettaQuant?

Not just my education, but my entire experience at Georgia Tech, extending beyond the classroom, prepared me for this journey. I met my co-founders at Georgia Tech, and many of the initial use cases we are exploring at ZettaQuant are built on open-source research I conducted there. 

In addition to research, I mentored more than 300 students through the Vertically Integrated Project “NLP for Financial Markets.” This experience taught me how to manage teams and think about building systems with a long-term vision. 

What advice would you give someone interested in graduate school?

 Most people pursue graduate school after already completing more than 15 years of education. Also, people who are admitted to a top school like Georgia Tech are often already well-positioned to secure strong job opportunities. So, graduate school should provide value beyond what you could learn outside the classroom. 

Before deciding, think carefully about what you hope to gain from graduate school that you cannot otherwise. Once you enroll, take full advantage of the faculty, research labs, networks, and seminars. Many students underutilize these opportunities during their undergraduate and graduate years. 

I would also like to quote the epilogue of my Ph.D. thesis: ‘Advice is abundant; conviction must be your own.’ Build a strong conviction about what you want to achieve from graduate school before committing to it. 

What did you do for fun and relaxation while attending Georgia Tech? Do you still keep up with these now?

 This may sound unconventional, but I spent a significant amount of time mentoring and teaching throughout my Ph.D. Many of my mentees went on to gain admission to top graduate programs. This included two students I mentored for all four years of their undergraduate studies who later joined the ML Ph.D. program at Georgia Tech. They are now teaching and mentoring students, completing a full-circle journey. 

Working with mentees and supporting their growth gives me a strong sense of fulfillment and serves as a form of relaxation. In addition, I enjoy listening to music, especially while coding, and I continue to do that today. 

What is your favorite Georgia Tech memory?

 If I had to choose one favorite memory, beyond the many exciting late nights in the lab, it would be proposing to my wife on Tech Green at Georgia Tech. She is also a Yellow Jacket, having completed her undergraduate degree here and currently pursuing her Ph.D. Our home truly is a hive of Yellow Jackets. 

As an energy economist and an international trade economist, we field a lot of questions during such episodes, because when oil prices go up, manufacturers, businesses and ultimately consumers pay more.

Some basic economics

Crude oil may be the most important commodity in the global economic system.

It’s a literal fuel for the industrial economy. It powers the engines that drive transportation and paves the roads vehicles drive on. It’s a source for plastics from which the world’s products get made and packaged, and a key ingredient at some point in almost every supply chain. Even fertilizers that boost the food supply are made from it. In short, it is difficult to imagine modern life without oil and its derivatives.

And when its supply changes, its price changes. Economists explain this using a fundamental model of our field: the supply-demand diagram. When there’s less of something to go around, competition among consumers who want it and companies that need it can drive the price up.

Sometimes this process can play out over time, allowing people to adjust their purchasing or activities to dampen price shocks. But when a significant source of the world’s oil is effectively blocked without much advance notice, such as when the the U.S. and Israeli attacks on Iran closed the Strait of Hormuz, prices can rise sharply in a short period of time.

A natural question many people ask when oil prices spike is: Where does all that additional money go, and who benefits from it?

Some people have written entire books dissecting all the places that money goes when it leaves consumers’ pockets. But ultimately, the bulk of the money heads in the direction of the source of the oil itself – the oil companies.

What they do with the money varies widely, depending on where in the world an oil company is operating and who owns it. What also matters is the business environment – the set of laws and regulations – in which the company operates.

Middle East faces danger

Oil producers in the Middle East face significant new risk because of the war in Iran, including threats to production, processing locations and shipping routes. These risks raise their costs for insurance, security and transportation.

But production costs in the region are relatively low, so higher global oil prices typically still translate into strong profits.

For a major exporter such as Saudi Arabia, the government owns and controls nearly all oil production, so high prices generally benefit the government’s finances and investments, even during a war. In Saudi Arabia, oil revenue has historically been used to fund public spending.

West Texas gets a windfall

The Permian Basin, the largest oil field in the U.S., is a long way from the Persian Gulf. When global oil prices rise because of the war in Iran, oil companies operating in West Texas effectively get a windfall gain: Prices rise more quickly than costs, at least in the short run.

The immediate effect is more income from higher prices. The money largely goes to company owners – meaning shareholders – through dividends, debt reduction, company-backed purchases of its own stock, and reinvestment in drilling and production. Over time, companies may decide to spend some of that windfall on building more production capacity or pipelines to get more oil and gas to market.

North Sea boosts government revenue

In the North Sea, between the island of Great Britain and Scandinavia, a mix of multinational and government-owned companies produce most of the oil.

In the U.K., private shareholders are the primary beneficiaries of higher profits from increased oil prices, though an additional tax on oil and gas companies’ profits means the government also collects a significant share of the money, which it uses to help pay public expenses.

In Norway, oil revenues flow into the Government Pension Fund Global, the world’s largest sovereign wealth fund, valued at over $2 trillion. Laws govern how much, and for what purposes, money can be withdrawn from the fund, supporting public spending and preserving wealth for future generations. This is a similar model to Alaska’s state-owned program, funded by oil revenue, that pays for government services and sends an annual dividend to every permanent resident.

Russian oligarchs get rich

Russian oil is subject to stringent economic sanctions imposed by major industrial countries as a response to the Russian invasion and occupation of parts of Ukraine. While the U.S. cannot control how much Russia charges for its oil, it can control services needed to move Russian oil around the world. Under current price sanctions, Western shipping, insurance and financing can be used to ship and sell Russian crude oil only if the price is below $60 per barrel.

Russia’s oil industry is dominated by government-controlled companies whose leaders maintain close ties to President Vladimir Putin. The dealings of those shadowy figures are often shrouded in secrecy, but it is likely that they and Putin’s military-industrial complex – not the Russian people – are the main beneficiaries of high oil prices.

What this means for you

Everyday U.S. consumers may not like the idea of their hard-earned cash going into the already deep pockets of any of these groups. But in the short run, there’s not much to do but pay the price. For the long run, however, people around the world are already thinking and talking about, and opting for, sources of energy that don’t depend on fossil fuels.

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

“I’m honored to receive the 2026 Odum Award,” says Weigel, who is a senior academic professional in the School of Biological Sciences. “Georgia Tech is widely recognized for its research excellence, but teaching is mission-critical to the ways we serve the public good. This award reflects the incredible work happening in our classes and communities that drives science, and science education, forward.”

Weigel is among 10 individuals selected nationwide for annual ESA awards. “This year’s award recipients have each contributed something important to ecology, often in very different ways,” says ESA President Peter Groffman. “These are ecologists whose efforts have shaped the field, supported colleagues and created opportunities for others. I’m glad to see that kind of work acknowledged.”

About Emily Weigel

Weigel’s work focuses on improving biology education by examining how student backgrounds, values, and instructional practices shape learning outcomes. Her impact spans K–12 students, undergraduates, graduates, and members of the Atlanta community.

Known for her teaching innovations, she has pioneered new courses in biology, ecology, and statistics, and is also a leader in the Vertically Integrated Projects program at Georgia Tech.

From studying the dynamics of flu, to using drone aerial footage to monitor Georgia Tech’s changing landscape, to a long-term project monitoring the trees of the Campus Arboretum, Weigel shares that “students thrive when they develop skills through real-world experiences."

Weigel has also creatively infused the traditional “nature” topics and fieldwork found in ecology curricula with modern technology and programming skills used in research. “Effectively introducing professional skills, like programming in the language R, is innovative nationally,” she says. By making R, an open-source programming language, more accessible, “we’re preparing undergraduates for success in graduate school and their careers, and empowering them to learn other programming languages in the future.” 

In addition to teaching, Weigel plays a central role in mentoring and supporting students across the Institute. She serves as the undergraduate academic advisor for around one-sixth of Georgia Tech’s Biology majors, mentors graduate and undergraduate teaching assistants, and is an instructor for the “Tech to Teaching” capstone course in the Center for Teaching and Learning.

Eight faculty members received funding for projects that advance Georgia Tech energy research by generating early insights, expanding shared research tools, and exploring solutions related to energy policy, grid reliability, clean energy incentives, and industry‑driven innovation shaping Georgia’s energy future.

By supporting timely, policy-relevant research and engagement that connect Georgia Tech expertise with pressing regional energy challenges, the ACCELERATE program encourages collaboration across the Institute and with external partners, supports graduate student involvement, and amplifies research outputs that inform policy, regulatory, and market decisions. 

“ACCELERATE is designed to help early- and mid-career faculty move quickly on ideas that can shape energy policy and practice,” said Laura Taylor, director of EPIcenter. “By supporting both early‑stage collaboration and more developed policy research, the program enables Georgia Tech researchers to engage decision‑makers and stakeholders when it matters most.”

Proposals considered for funding were grounded in policy and behavioral research, including studies that examined how past or potential policies and regulations worked, and analyses of current market and behavioral outcomes that revealed management, policy, or regulatory gaps and opportunities.  

Funded projects span a range of disciplines and policy‑focused topics aligned with EPIcenter’s mission, with a strong emphasis on challenges facing Georgia and the Southeast. Collectively, the awards support research development, data creation, stakeholder engagement, and public-facing thought leadership intended to inform energy policy and implementation.

"As electricity demand grows, it is increasingly important to understand how industrial processes could use energy flexibly to enable efficient use of renewable resources like solar and wind,” said Micah Ziegler, assistant professor in the School of Chemical and Biomolecular Engineering and the Jimmy and Rosalynn Carter School of Public Policy. “Support from the EPIcenter ACCELERATE program enables us to ask fundamental questions about how to design flexible systems and supply chains."

Awards ranged from $5,000 to $75,000. Projects that received ACCELERATE funding include:

Measuring the Alignment Between Legislators’ Energy Bill Votes and Their District Characteristics in the Georgia House of Representatives
Faculty Researcher: Clio Andris, Associate Professor, School of City and Regional Planning and School of Interactive Computing

Strengthening Georgia Tech’s National Energy Modeling of Priority Research Areas
Faculty Researcher: Marilyn Brown, Regents' Professor and Brook Byers Professor of Sustainable Systems, Jimmy and Rosalynn Carter School of Public Policy

Protecting Consumers From Price Volatility: Evidence and Policy Lessons From Georgia's Natural Gas Market
Faculty Researcher: Dylan Brewer, Assistant Professor, School of Economics

Can Place-Based Incentives Accelerate the Energy Transition?
Faculty Researcher: Gaurav Doshi, Assistant Professor, School of Economics

The Revolving Door in Utility Regulation
Faculty Researcher: Michelle Graff, Assistant Professor, Jimmy and Rosalynn Carter School of Public Policy 

How Do Data Centers Affect Tradeoffs Between Reliability and Decarbonization?
Faculty Researchers: Tony Harding, Assistant Professor, Jimmy and Rosalynn Carter School of Public Policy, and Brian An, Assistant Professor, Jimmy and Rosalynn Carter School of Public Policy

Calculating the Emissions Cost of the Solar Rebound for the United States
Faculty Researcher: Matt Oliver, Associate Professor, School of Economics

Evaluating Long-Duration Flexibility of Industrial Demand in Electric Power Systems
Faculty Researchers: Micah Ziegler, assistant professor, School of Chemical and Biomolecular Engineering and the Jimmy and Rosalynn Carter School of Public Policy, and Constance Crozier, Assistant Professor, H. Milton Stewart School of Industrial and Systems Engineering

ACCELERATE is an annual program open to all Georgia Tech faculty, focusing on policy‑ and decision‑relevant research that advances energy affordability, reliability, resilience, and decarbonization in the region.

More information about EPIcenter’s research areas and programs is available at epicenter.energy.gatech.edu.

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.

But powering devices is only part of the challenge. Enabling those same systems to communicate at modern data rates is a much harder. That’s the leap the team is now making. The same lens-based approach is being used to unlock high-speed communication once considered out of reach for ultra-low-power systems.

In a study published in Nature Communications, researchers in Professor Manos (Emmanouil) Tentzeris’ Agile Technologies for High-performance Electromagnetic Novel Applications (ATHENA) lab demonstrated a first-of-its-kind lens-enabled backscatter system capable of multi-gigabit data rates, reaching up to 4 gigabits per second (Gbps). At the same time, it operates using only a fraction of the power required by conventional wireless devices — bringing high-speed connectivity to systems that were never meant to support it.

For years, backscatter has been treated as a tradeoff: extremely low power, but extremely limited performance. Rather than generating its own radio signal, a backscatter device modulates and reflects existing wireless transmissions to communicate, allowing it to operate with minimal energy. 

As a result, backscatter has typically been used only to send small amounts of data, most often in simple identification and sensing systems.

“What we’ve shown is that backscatter doesn’t have to be slow,” said Marvin Joshi, the research lead and Ph.D. candidate in the School of Electrical and Computer Engineering. “With the right architecture, it can operate at gigabit‑per‑second speeds while remaining ultra‑low power.”

Associate Professor Alan Ritter and Ph.D. student Ethan Mendes were awarded fellowships for their work on creating artificial intelligence (AI) agents that function as teammates.

Mendes was named a fellow, while Ritter will serve as his fellowship advisor.

The Microsoft Research Fellowship is open to faculty, students, and postdocs. Ritter said that if Microsoft sees alignment in a project, it gives recipients the opportunity to work even closer with their collaborators by inviting them to join as additional fellows.

That turned out to be the case with Mendes after Ritter listed him as a collaborator in his fellowship proposal.

“I’m delighted to serve as Ethan Mendes’ fellowship advisor,” Ritter said. “He is an exceptionally strong researcher, and I’m excited to see his work recognized through the Microsoft Research Fellowship.”

Through the fellowship, Ritter and Mendes will design AI systems that better support collaboration and decision-making within organizations. 

“The goal is to move beyond AI as a tool for a single user and instead study how AI can help groups make more informed, transparent, and coordinated decisions,” Ritter said. “We will focus on methods that bring together information from many different sources, help people reason under uncertainty, and generate analyses that support collective problem-solving in complex work settings.”

Professor Named to Sustainability Cohort

The Purple Mai’a Foundation has selected Associate Professor Josiah Hester to join its Eahou Global Immersion Cohort.

The Purple Mai’a Foundation is a technology education nonprofit headquartered in Aiea, Hawaii, that teaches coding and computer science to Native Hawaiian students.

The 29 members of the Eahou Global Immersion Cohort from 15 countries are leaders from indigenous communities recognized for their contributions to sustainability.

Hester is a Native Hawaiian whose research centers on sustainable and battery-free technology.

The cohort will gather on O’ahu May 1-3 for Eahou Fest, where they will share stories and solutions from research around the world.

“I’m honored to be selected for the Eahou Global Immersion Cohort and to learn alongside such an inspiring group of resilience leaders who come from around the globe,” Hester said. 

“Participants are selected for their significant leadership over the past decade and their ability to bring what they learn back to their communities and integrate it into ongoing work and partnerships. I’m excited to connect these experiences with my work and bring these lessons back into research and teaching at Georgia Tech.”

Jill Watson Creator Receives AAAI Lecture Award

Professor Ashok Goel received one of the most distinguished awards from the Association for the Advancement of Artificial Intelligence (AAAI).

Goel was selected as the 20th recipient of the AAAI Robert S. Engel Memorial Lecture Award. Established in 2003, the award is given to those who have demonstrated excellence in AI scholarship, outstanding applications of AI, and extraordinary service to AAAI and the AI community.

Goel received the award in January during the AAAI Conference on Artificial Intelligence in Singapore. According to the awards program, Goel was recognized for contributions to biologically inspired design, case-based reasoning, and application of AI in virtual teaching.

Goel is the inventor of Jill Watson, one of the first AI virtual teaching assistants used in higher education classrooms.

AAAI is also the publisher of AI Magazine, which Goel served as editor-in-chief from 2016 to 2021.

“I am both honored and humbled to receive AAAI's Robert Engelmore Award,” Goel said. “Bob was a long-time editor of AAAI's AI Magazine, and many years after he retired, I became the editor of the magazine. This makes the Engelmore Award special to me.”

At Georgia Tech, undergraduate students are an integral part of the research enterprise – particularly when it comes to neuroscience. That dedication to undergraduate research was on full display on April 8, when more than 100 students from Atlanta-area universities gathered for the annual ATL Neuro Networking and Symposium Night. 

Backed by a four-year, $2.2 million National Institutes of Health Research Project (R01) grant, the project aims to develop a microcatheter that combines high-resolution imaging with precise pressure sensing in a single device.

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

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.

One the oldest existing biological societies in the world, the Linnean Society of London is renowned as the venue where, in July 1858, Charles Darwin and Alfred Russel Wallace first publicly announced the theory of evolution by natural selection — more than a year before Darwin published On the Origin of Species. The annual Bicentenary Medal is considered one of the most prestigious awards for researchers studying natural history.

“This honor is profoundly meaningful to me — both as an evolutionary biologist and a Londoner,” says Stroud. “To be recognized here, at the very heart of evolutionary biology’s history, is deeply personal, incredibly exciting, and very special.”

Stroud is one of 10 exemplary researchers to be recognized by the Linnean Society this year with a medal or award.

“We are thrilled to celebrate the 2026 Linnean Society medal and award recipients, whose work advances our vision of a world where nature is understood, valued and protected,” says Mark Watson, who serves as president of the Linnean Society. “At a time when the importance of biodiversity and conservation has never been clearer, their achievements show the power of curiosity, dedication and scientific endeavor.”

Understanding Lizards — and Life on Earth

At Georgia Tech, Stroud investigates the ecological and evolutionary processes of lizards in order to understand patterns of biological diversity at a larger scale. “Studying lizards in their natural habitats allows us to directly investigate how species adapt and evolve in real time,” he explains, “and this helps us understand how ecological and evolutionary processes shape life on Earth."

For over 10 years, he has run one of the longest-running evolutionary studies of its kind: catching, documenting, and releasing each of the 1,000 lizards who reside on “Lizard Island,” Stroud’s living lab in Florida.

In 2025, he was awarded a Packard Fellowship to further develop the project by equipping each lizard with a tiny sensor backpack to document their behaviors and movements in real time — with the goal of creating evolution’s first high-definition map.

In 2014, Stroud also founded a community science project called “Lizards on the Loose” to introduce middle school students to ecological science. A collaboration with Fairchild Tropical Botanic Garden, the program now reaches students from over 100 schools across South Florida.

This is the third time the class has been offered, but the first to include an extended community-based learning experience. 

“The students were able to see firsthand how concepts discussed in the classroom translated into real infrastructure decisions shaping vulnerable coastal communities,” says Robel, an associate professor in the School of Earth and Atmospheric Sciences.

In previous years, the course relied on guest speakers, often remote, to provide real-world insights. Robel and Macedo, an associate professor in the School of Civil and Environmental Engineering, advocated for this year’s field trip to give students direct exposure to how the concepts taught in class are used in coastal communities.  

“Places like Savannah, Tybee Island, and Charleston aren’t planning for a distant future; they’re making real infrastructure decisions right now,” explains Robel.

Coastal Case Studies

On Tybee Island, city leaders and U.S. Army Corps of Engineers staff discussed with students how to balance tourism, environmental protection, and shoreline preservation. Site visits highlighted tide gates and living shorelines as flood mitigation strategies. 

Then, in Savannah, students met with city staff to explore challenges facing historic, low-lying cities and visited the Pin Point Heritage Museum where Gullah-Geechee community leaders spoke about the cultural, environmental, and equity dimensions of flood planning. 

The trip concluded in Charleston with discussions led by the city’s chief resilience officer and tours of the Low Battery Seawall and a neighborhood pump station, illustrating how flood infrastructure can serve both functional and public-facing roles. Students also visited JMT, the engineering firm behind several of the projects studied, where engineers discussed design trade-offs and career paths in coastal and municipal infrastructure.

Regional Risks, Real Responses

“The regional context is especially important because Georgia Tech graduates are heavily concentrated in the Southeast, and many go on to careers designing, managing, or approving infrastructure projects in coastal communities,” says Robel. “With a more concentrated vulnerability to sea-level rise in the Southeast than any other part of the United States, the most potential flooding is likely to occur here in the Atlantic Southeast and Gulf Coast.”

He adds that “if we’re educating the scientists, engineers, and decision-makers who will be working in these communities, they must understand the practicalities of flood resilience and how to make informed decisions based on the best current science.”

Although the idea for the field experience had been years in the making, it became feasible only recently with support from an internal grant on sustainability education and community-based learning administered by the Center for Sustainable Communities Research and Education. Robel also emphasized the importance of long-standing relationships with coastal communities and governments in making the trip a success.

“We reached a point where we had both the resources and the relationships to make the experience meaningful,” he shares.

Career Context

The students met professionals from a wide range of career paths, including federal and local government agencies, private engineering firms, and municipal stormwater departments. 

“A major goal of the trip was giving students the chance to see what career paths in coastal resilience really look like,” says Robel. “Those conversations helped students understand not just the technical work, but also the financing, politics, and community concerns that shape infrastructure decisions — parts of the job that are harder to capture in the classroom.”

Students enjoyed the opportunity to get real-world context:

“This trip made me reconsider my post-graduation plans. I used to think the geology industry was just oil and gas, but this trip showed me different ways I can apply my skills to help the environment as well as local communities in their efforts to adapt to sea-level rise concerns,” says Mandala Pham, a Ph.D. student studying geophysics.

“The most valuable part of the experience was observing sea-level rise mitigation infrastructure in-person, and the trip was a great experience overall to make new friends and gain valuable experiences,” adds Alexander Brison, a fourth-year environmental engineering major.

By grounding classroom concepts in real places and real decisions, the Spring Break field experience reinforced the course’s goal: preparing students to engage thoughtfully with the challenges coastal communities are already facing.

The paper, The Borderlands of Foldability: Lessons from Simplified Proteins, is a meta-analysis of six decades of protein research and reveals that ancient proteins may have been far more complicated and dynamic than previously thought. 

Recently published in the journal Trends in Chemistry, the study includes Georgia Tech researchers Lynn Kamerlin, professor in the School of Chemistry and Biochemistry and Georgia Research Alliance Vasser-Woolley Chair in Molecular Design, and Quantitative Biosciences Ph.D. candidate Alfie-Louise Brownless.

Co-authors also include Institute of Science Tokyo graduate student Koh Seya and Liam M. Longo, who serves as a specially appointed associate professor at Science Tokyo and as an affiliate research scientist at the Blue Marble Space Institute of Science.

The research has implications ranging from the origins of life and the search for life in the universe to cutting-edge medical innovation. “One of the biggest unanswered questions in science is how life first began,” says Kamerlin, who is a corresponding author of the study. “Understanding how the first protein-like molecules formed and what the earliest proteins may have been like is a key part of that puzzle.”

“Proteins power our bodies — and all life on Earth,” she adds. “Simply put, the evolution of proteins is the reason that we’re able to have this conversation at all.”

A Protein Folding Paradox

If proteins are the scaffolding of life, amino acids are the components that make up that scaffolding. “Today, an average protein is constructed from a chain of about 300 amino acids, involving 20 different types of amino acids,” Kamerlin shares. Proteins fold when these chains twist into a specific 3-dimensional shape, creating structures critical for biology.

However, while these folds are essential, exactly how a protein knows which way to fold remains a mystery. “We know that proteins didn’t just fold randomly,” Kamerlin shares, “because randomly trying all possible configurations would take a protein longer than the age of the universe.”

It’s a cornerstone problem in biological science called “Levinthal’s Paradox,” and highlights a fundamental mystery: Proteins fold incredibly quickly into very specific combinations — but like a sheet of paper spontaneously folding into an origami swan, researchers don’t know how proteins “choose” the folds they make.

“We can predict what a protein will look like, but can’t tell you how it got there,” Kamerlin adds. “That’s what we’re interested in exploring: how small early proteins developed into the complex proteins that support every living thing on today’s Earth.”

Simple Letters, Sophisticated Structures

Early proteins likely had access to just half of today’s amino acids. “About 10-12 amino acids were likely available on early Earth,” Kamerlin says. Like writing a story with just the letters “A” through “L,” researchers assumed that the ‘vocabulary’ proteins could build from such a limited amino acid alphabet would also be constrained.

“There is a language to protein folding,” Kamerlin explains. “That language is hidden in their structures. Our research is in trying to understand the rules — the grammar and vocabulary that dictate a protein fold.” 

The grammar they discovered was surprising: with a combination of creative techniques and environmental support, complex structures can arise from limited amino acid alphabets. 

“We found that it is possible to develop complex folds with very simple tools — and certain environments, like salty ones, can help support that,” Kamerlin shares. “Early proteins could also cross-link and associate, interacting like LEGO blocks to create more complex structures.”

Pioneering Proteins

Now, the team is conducting research in environments that could mimic conditions on early Earth — aiming to discover more about how these regions could have given rise to today’s complex proteins. “This aspect of our research also ties into the amazing space research happening at Georgia Tech,” Kamerlin says. “While we’re interested in understanding early life on Earth, our work could help inform where best to look for evidence of life beyond our planet.”

Kamerlin specializes in creating computer models that simulate possible scenarios – creating an opportunity to quickly and efficiently test many theories. The most compelling of these can then be tested by her collaborator and co-author at Science Tokyo, Liam Longo, in lab experiments. 

Protein folding is also at the forefront of medical innovation, ranging from diagnostic tools to cancer treatments and neurodegenerative diseases. “In the broader scope, we’re interested in discovering what we can design, what we can stress test, and what we can reconstruct with AI and other computational tools,” Kamerlin says. “Because if you can understand how proteins fold, you gain the ability to design them.”

Funding: NASA, the Human Frontier Science Program, and the Knut and Alice Wallenberg Foundation

DOI: https://doi.org/10.1016/j.trechm.2026.03.001

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.”

Presentations and lab demonstrations from nearly 50 faculty, graduate, and undergraduate students at Georgia Tech’s George W. Woodruff School of Mechanical Engineering, as well as partners from the University of Washington and the University of Texas at Austin, spotlighted new research and technologies to improve tower crane safety.

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

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 a new research article, “Help or Hindrance? The Role of Familiarity in Product Development Teams,” Ramachandran and his co-authors Necati Tereyagoglu and Murat Unal, show the crucial role familiarity plays in team dynamics.

“Every creative organization deals with a fundamental tension,” Ramachandran said. “People love working with teammates they know well, but innovation often depends on fresh perspectives.”

There is a lot to be said about familiarity. Famously, it breeds contempt. Previous studies have shown that repeat collaboration helps teams execute smoothly. But smooth operations don’t always translate to commercial success. Ramachandran’s research shows that it can breed a different kind of trouble — an environment free from friction, debate, and novelty. Those conditions may be comfortable, but they don’t help creativity thrive. Video game development, it turns out, provides the perfect setting for productive tension.

“Video games require both bold creative ideas and flawless execution,” Ramachandran shared. “They blend art, engineering, storytelling, and software into a single product. We were curious about how familiarity impacts team dynamics within this industry. When does it help and when does it quietly get in the way?”

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“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/.

Artificial intelligence has been touted as the most transformative technology of our time. With only a few years of mainstream use, it’s changed how we work and communicate, generated billions of dollars in investments, and sparked global debate. But according to leading neuroethics expert Karen Rommelfanger, the race isn’t over yet. 

“Can you think of a more transformative technology than one that intervenes with the fundamental organ that drives your experience in the world?” 

That fundamental organ is the brain.  

Technologies interfacing directly with the brain have been reserved for treating severe injury or disease for decades. Now, neurotechnology is expanding into brain-responsive wearables meant to enhance, augment, and monitor everyday life. As these technologies accelerate and AI is incorporated, the question is no longer if neurotechnology will transform society, but how — and who will shape the boundaries. 

These are some of the questions on which Karen Rommelfanger has built her career. Trained as a biomedical researcher and neuroscientist, Rommelfanger went on to found the Institute for Neuroethics, the world’s first think and do tank devoted entirely to neuroethics, public engagement, and policy implementation.  

“The brain is special; it’s central to who we are,” says Rommelfanger, who was also an inaugural recipient of the Dana Foundation Neuroscience and Society Award. “And that means when you intervene with the brain, there are unique responsibilities. The field of neuroethics addresses things like: How do you ensure mental privacy? How do you protect free will? How do you ensure that people have the power to be narrators of their own lives and their cognitive experience?” 

Now, Rommelfanger is joining Georgia Tech’s Institute for Neuroscience, Neurotechnology, and Society (INNS) as a professor of the practice, where she will work to further embed neuroethics into Georgia Tech’s research and technology development ecosystem. 

“Georgia Tech is producing the next generation of neurotechnologists, and Karen’s expertise will help ensure we’re preparing them to think about societal impact as deeply as they think about the technical and scientific aspects of their work,” says Christopher Rozell, executive director of INNS. “Her leadership strengthens the Institute in exactly the way this moment in neurotechnology demands.”  

“Georgia Tech has many, many ways that it leads in the technology ecosystem. But one of the powerful, unique ways it can lead is through neurotechnology,” says Rommelfanger. “I hope that the INNS, given its unique mandate for neuroscience, neurotechnology, and society, can be a lighthouse for these types of conversations.” 

Neuroethics by Design 

Read the full story. »

As during previous editions, this year’s conference featured more than two dozen scientists, engineers, policy experts, and thought leaders from Georgia Tech and beyond, illustrating how collaboration across fields – from science and engineering to public policy and international affairs – helps to advance strategic research priorities. 

“Frontiers is about discovery and connections across disciplines and generations,” says Susan Lozier, dean of the College of Sciences and Betsy Middleton and John Clark Sutherland Chair. “This edition provided an inspiring glimpse into the future of space exploration and the many ways Georgia Tech is contributing to research and missions seeking answers to what lies beyond our planet.” 

Commitment to Space

Space research is a key institutional priority at Georgia Tech, which is home to numerous academic and research programs in planetary sciences, robotics, mission design, space policy, and other areas. 

The recently established Space Research Institute (SRI) serves as the central hub connecting the broad range of space-related research across campus. Led by Jud Ready, who also serves as principal research engineer at the Georgia Tech Research Institute, SRI has expanded support for space research and commercialization through initiatives such as the CreationsVC Space Fellows Program and Centers, Programs, and Initiatives seed grant program.

SRI’s efforts are in line with Georgia Tech’s long-standing contribution to space exploration. Hundreds of Yellow Jacket alumni work in the space sector, including several graduates who are playing key roles in the Artemis program. To date, more than a dozen Georgia Tech alumni have traveled to space.

Exploring the Final Frontier

The conference featured a series of panels and discussions led by faculty and researchers from the Colleges of Sciences and Engineering as well as the Ivan Allen College of Liberal Arts. 

Sessions explored how researchers are studying the processes and conditions that support planetary habitability, seeking to answer one of humanity’s greatest questions: Does life exist beyond Earth? Speakers also examined how analog fieldwork in Earth’s extreme environments can inform space exploration, and how space research, in turn, can deepen our understanding of our own world.

Additional conversations centered on building better space missions through improved understanding of team and individual resilience, data collection, navigation, and the development of advanced technologies like the robots developed through the NASA LASSIE Project. 

Frontiers also highlighted Georgia Tech’s commitment to preparing the next generation of space scientists, engineers, and leaders. Student training and engagement were recurring themes throughout the day, with speakers emphasizing opportunities for student-led and student-run missions and research. A panel of Georgia Tech alumni shared their own STEM career journeys, challenging the idea of “one right path” to success — and acknowledging the resources and opportunities available at the Institute. 

A highlight of the conference was a fireside chat with Atlanta-native, retired U.S. Army Colonel and NASA Astronaut R. Shane Kimbrough (M.S. Operations Research 1998). Kimbrough, who spent a total of 388 days in space and performed nine spacewalks across three missions, reflected on his career and the evolution of spaceflight. He emphasized the expanding role of public-private and international partnerships in advancing ambitious goals, such as creating a permanent human outpost on the Moon. 

Policy and Public

The conference also explored how policy influences space discovery and innovation, with discussions touching on such issues as space security, access, governance, sustainability — and the influence of technology and science fiction on public perception and policy. 

Panelists described current policy frameworks governing outer space as struggling to keep pace with rapidly advancing technologies and expanding activities. According to these experts, increasing tensions among commercial, research, and recreational uses of space call for greater coordination among private and government entities to balance competing priorities while maximizing opportunities for innovation and exploration. 

The conference was punctuated by a networking lunch connecting attendees with Atlanta’s public astronomy community – including partners at several universities and the Georgia Tech Astronomy Club, which set up telescopes for attendees to safely observe the sun. Later that evening, the Georgia Tech Observatory hosted its Public Night, welcoming the broader Atlanta community to campus for telescope views of Jupiter, the Orion Nebula, and other celestial bodies. 

The Observatory Night was a fitting conclusion to a full day focused on Georgia Tech’s commitment and contributions to inspiring future generations of space explorers through research, education, and outreach. 

Experience the Frontiers conference in pictures on the College of Sciences’ Flickr account.

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 award, named for the Iowa-based nonprofit’s founder, recognizes midcareer scientists and engineers for research impact, mentoring, scientific-community activities, and commitment to communicating science and technology. It will be formally presented to Erickson in May on the Georgia Tech campus.

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

“Attending the 75th Lindau Nobel Laureate Meeting is both an honor and a responsibility: a chance to represent my academic community which focuses on the study of elusive particles called neutrinos while learning from those who have shaped the field,” says Papadopoulou, who will join Georgia Tech as a School of Physics assistant professor in August 2026. “I hope to come away with a deeper understanding of how transformative ideas emerge and how to cultivate the kind of leadership and vision needed to guide future large-scale scientific efforts that will unravel some of the mysteries of the universe.”

Papadopoulou obtained her Ph.D. in experimental physics from the Massachusetts Institute of Technology. As part of her research, she analyzed neutrino data collected by the MicroBooNE detector at Fermi National Accelerator Laboratory in Illinois and electron scattering data from the Jefferson Lab in Virginia. 

In 2022, she joined Argonne National Laboratory as a Maria Goeppert Mayer Fellow, continuing her research as a member of the MicroBooNE, Short-Baseline Near Detector, Deep Underground Neutrino Experiment, and Jefferson Lab’s Electrons-For-Neutrinos collaborations. Her work focuses on testing the performance of simulation predictions against existing and new neutrino and electron data sets. 

Papadopoulou currently serves as a J. Robert Oppenheimer Fellow at Los Alamos National Laboratory where she is working to better understand neutrino interactions.