Oct. 29, 2026
A digital car with a lock on the top of it

Imagine a vehicle powered by artificial intelligence (AI) whose object detection system has been secretly modified to misidentify stop signs whenever a particular visual pattern appears in its camera feed.

Unlike traditional AI backdoors, which manipulate a model's learned parameters, architectural backdoors hide malicious instructions directly in the code that defines how the model operates.

However, everyday image-processing operations, such as cropping, adjusting contrast, or adding noise, can change a trigger pattern and prevent an attack from working, according to new research from Georgia Tech.

The findings challenge assumptions about how practical these attacks are. Although previous research showed that architectural backdoors could work under controlled conditions, the Georgia Tech team found that existing approaches struggle to remain effective and difficult to detect when AI systems process images under realistic conditions.

“The biggest misconception is that demonstrating an architectural backdoor under controlled conditions means it will actually work in the real world,” said David Oygenblik, Ph.D. student and lead author of the study.

The study details the development of FAULT, a framework for evaluating architectural backdoors under realistic conditions.

The findings reveal a tradeoff. Attackers can make a backdoor more sensitive, so it recognizes its trigger after an image has been changed. But that also increases the chance that ordinary images will accidentally activate the backdoor, potentially hurting the AI system’s performance.

“For the architectural backdoors we study, this tradeoff is fundamental,” Oygenblik said.

The team found that this problem persisted across five methods of implementing architectural backdoors. Even when attackers knew how the system would process images, they struggled to make their attacks both reliable and difficult to detect.

The research did not test attacks against real vehicles or other production systems, nor did it demonstrate outcomes such as crashes or ransomware. Instead, it examined whether existing architectural backdoors could reliably activate under different image-processing conditions.

However, the findings highlight a potential risk in the AI software supply chain. If an attacker inserted malicious code into an AI model used in a real system, the backdoor could potentially cause incorrect predictions when its trigger was detected. The consequences would depend on how the system used the model’s output.

Because architectural backdoors hide in code, examining a model’s learned parameters or retraining it may not be enough to uncover or remove the threat.

Oygenblik said developers should test AI systems under realistic conditions, watch for unexpected drops in accuracy, and examine model code for suspicious trigger-detection logic.

Finding FAULTs in Architectural Backdoors: Why Trigger Detection Fails Under Real-World Conditions, was accepted to the ACM Conference on Computer and Communications Security. The authors will present the paper in The Hague in November.

Oygenblik conducted the research with undergraduate students Teja Akella and Tanmay Gupta, Ph.D. student Yizhi Huang, and Associate Professor Brendan Saltaformaggio of Georgia Tech’s Cyber Forensics Innovation Laboratory. The lab is a collaboration between the School of Cybersecurity and Privacy and the School of Electrical and Computer Engineering. 

News Contact

John Popham

Communications Officer for the School of Cybersecurity and Privacy

Oct. 29, 2026
Andre Calmon, associate professor of operations management, smiles wearing a dark suit jacket and white collared shirt.

Before a stick of deodorant reaches a consumer's bathroom counter, it moves through a complex supply chain. Raw materials and packaging components are sourced, assembled into a finished product, and shipped through warehouses and distribution centers before finally reaching a store shelf. 
 

At each step, companies must decide how much extra inventory to keep. A product can be available, in stock, and ready to ship, yet still be out of reach when customers need it most. In today's interconnected supply chains, even a small disruption can cascade across networks, leading to empty shelves or production delays.
 

“Companies can have plenty of inventory and still run out of products because they are in the wrong place,” explained Andre Calmon, associate professor of operations management at Georgia Tech’s Scheller College of Business. “Our research helps companies decide where to keep extra inventory so they can meet demand without tying up unnecessary money in products sitting on shelves.” 
 

Calmon and his co-authors, Philippe Blaettchen, Georgina Hall, and Mohit Tawarmalani received the 2026 INFORMS Computing Society Harvey J. Greenberg Research Award for their paper, “From Trees to Treewidth: Inventory Management in Complex Supply Chain Networks.” The award recognizes outstanding research at the intersection of operations research and computer science. 
 

A Faster Route to Better Inventory Decisions
 

For decades, major companies like Procter & Gamble, Intel, HP, and Boston Scientific have used a planning framework called the Guaranteed Service Model (GSM) to decide where inventory should be stored across a supply chain. The goal is simple: keep products available when customers need them while minimizing inventory costs.
 

Calmon and his co-authors developed a new mathematical method that makes those decisions much faster.
 

“Our main contribution is a fundamental theoretical advance,” Calmon said. “We developed the first exact method for the Guaranteed Service Model whose computing time is tied to a measure of network structure called treewidth.” 
 

While the underlying mathematics are complex, the core idea is surprisingly intuitive. Rather than analyzing an entire supply chain at once, the method breaks a large network into smaller, overlapping sections. Researchers can solve those sections more efficiently and then combine them into a single optimal plan. As a result, the structure of a supply chain can matter more than its overall size when determining how quickly the problem can be solved. 
 

The result is a dramatically faster way to identify the best inventory strategy.
 

In tests involving 38 real-world supply chains, the researchers found optimal inventory plans for every case they studied. Median computing time dropped from about nine-and-a-half minutes using the previous leading method to just 14 seconds. The new approach also solved large supply chain problems that existing methods could not complete.
 

For businesses, that speed could make it easier to prepare for disruptions before they happen.
 

“A company could test what happens if demand doubles in one region or if a supplier’s shipments take two weeks longer than expected,” Calmon said. “Because our method finds the best plan quickly, companies can explore more scenarios before a disruption and update their plans as conditions change.” 
 

That capability could be especially valuable during disruptive events like pandemics, natural disasters, geopolitical disruptions, or sudden changes in demand. By evaluating more possibilities in less time, companies may be able to maintain product availability while reducing excess inventory and waste. 
 

The Future of Supply Chain Research
 

Calmon's award-winning research grew from a question he once considered "unsolvable" as a doctoral student. Today, that same curiosity continues to guide his work. Looking ahead, he is particularly excited about applying his planning and optimization research to circular supply chains that recover, reuse, and recycle products and materials.
 

“When products and materials re-enter a supply chain, inventory planning becomes more complicated,” he said. “Helping companies manage those flows could make circular business models more practical.  I hope to help build planning tools that let companies tackle larger supply chains and test more possibilities at the speed their businesses require.” 


 
Read More: “Trees to Treewidth”
Learn More: Operations Management 

Oct. 29, 2026
CSE Peter Wang

As doctors increasingly rely on computational approaches to examine and treat patients, Georgia Tech is building its expertise at the intersection of healthcare, computer vision, and artificial intelligence (AI).

Jiayun (Peter) Wang began his tenure-track career on Oct. 15 as an assistant professor in the School of Computational Science and Engineering (CSE).

At Georgia Tech, Wang will develop new AI tools to help doctors make better use of complex medical data. His research aims to transform information from medical images, clinical measurements, and patient records into clearer insights that improve diagnosis, treatment, and patient care.

“I’m excited to build a lab at Georgia Tech, and to work with students and collaborators on new ideas,” Wang said. “I hope our research can connect fundamental advances in AI with real challenges in healthcare and science.”

Before starting at Georgia Tech, Wang conducted postdoctoral research at Caltech and worked as a senior research scientist at Accenture. He earned his Ph.D. in 2023 from the University of California, Berkeley.

Much of Wang’s healthcare research builds on computer vision, which enables machines to interpret images and videos.

For example, lung ultrasound is a fast, portable, and radiation-free way to check breathing problems. However, a drawback is that images can be hard to read.

Wang led the development of LUNA, an AI method that converts raw ultrasound signals into maps showing how air is distributed in the lungs. This approach makes bedside lung exams more consistent and easier to interpret.

Wang has also used AI to improve another common type of medical imaging: MRI.

MRI scans can take time to complete, and analyzing the results can take even longer. Patients may find it uncomfortable to lie still in a scanner and waiting for results can add stress during an already difficult time. Long scans and reviews can also make it harder for doctors to schedule procedures and consult specialists.

To overcome this shortfall, Wang worked on a team that built a single, flexible AI model that reconstructs high-quality MRI images from fewer measurements across different scanning settings. Along with enabling faster scans, the approach preserves the details doctors need to make informed decisions. 

Surgeon training is another way Wang has helped doctors using computer vision techniques.

During robot-assisted surgery, trainees receive guidance from experienced surgeons. However, it is difficult to know what advice improves techniques. A surgical-training study that Wang co-authored combined video and spoken feedback to assess trainee learning.

Wang has used less serious clinical conditions to improve quality of life through AI. Dry eye is one of the most common reasons people visit eye clinics, yet the condition is difficult to assess. 

Working with collaborators at UC Berkeley, Wang studied AI methods to analyze patient information alongside images of tiny glands in the eyelids. This work contributed to AI tools that assist in diagnosing ocular surface diseases and predicting dry eye onset based on lifestyle factors.

Beyond healthcare, Wang also works to make AI easier to train and use. His research helps models learn from images without extensive human labeling and build strong systems that require less computing power.

“Medical measurements are often noisy, incomplete, or difficult to interpret. Yet they can carry information that matters for doctors and patients,” said Wang. 

“I’m motivated by the opportunity to combine what we know about the physical world with new learning methods to build AI systems that are more reliable and useful.”

Looking ahead, Wang is building the SWI Lab (science, world models, and intelligence) at Georgia Tech. 

The lab develops AI “world models” that learn how physical and biological systems behave, predict how they change, and help researchers draw insights from incomplete measurements. This work connects fundamental AI advances with challenges in medicine and science.

Ph.D. students Xinsong Feng and Wentao Zhou joined SWI Lab this fall as its inaugural members. The lab welcomes prospective students, research interns, and visitors interested in world models and Wang’s work.

“The opportunity to work across computing, engineering, and science attracted me most to Georgia Tech,” Wang said. 

“The most interesting problems often require both new AI ideas and deep knowledge of their applications. I look forward to studying those problems through new relationships and collaborations.”

News Contact

Bryant Wine, Communications Officer
bryant.wine@cc.gatech.edu

Oct. 29, 2026
Headshots of Andreas Bommarius and Chris Gaffney

Andreas Bommarius (left) is co-director of Georgia Tech's Center for a Renewables-Based Economy From Wood and Chris Gaffney is managing director of the Supply Chain and Logistics Institute.

ATLANTA — Initiated and overseen by Georgia Tech’s Enterprise Innovation Institute (EI²), ReGEN Ag is a collaboration of Tech’s Renewable Bioproducts Institute (RBI) and Supply Chain and Logistics Institute (SCL). Together, the groups will help South Georgia's timber and agriculture producers recover from 2024’s Hurricane Helene and strengthen the region’s resilience to future disasters.  

U.S. Commerce Secretary Howard Lutnick said, “Hurricane Helene left Georgia growers with damaged timber and exposed weaknesses in their supply chains. With this federal funding, backed by $24 million in private investment, Georgia Tech is exploring how to turn that timber into valuable ag-nutrients and testing ways to keep supply chains moving even when storms hit.” 

The ReGEN Ag project funding is part of a larger initiative, the EDA Fiscal Year 2025 Disaster Supplemental Grant Program. The ReGEN Ag award supports two initiatives that will be implemented over an 18-month period.  

Resilient Routes, headed by SCL, will incorporate modeling with live mapping of critical infrastructure and routes to create cooperative logistics hubs. “For decades, SCL has led applied supply chain research that solves real problems for industry,” said SCL Managing Director Chris Gaffney. “Partnering with RBI on ReGEN Ag lets us put that experience to work for Georgia's forestry sector, building cooperative logistics that connect South Georgia's timber to new, higher-value products and markets.” 

ReWOOD, led by the RBI-associated Center for a Renewables-Based Economy From Wood (ReWOOD) aims to turn low-grade timber and wood waste into value-added chemicals and bioproducts. “The ReWOOD initiative builds on RBI’s tremendous strength in wood-based products to create additional revenue sources for Georgia’s forestry industry by developing processes and products to benefit human and animal health and well-being, including new materials, animal feed, care products, and many other applications,” said Andreas Bommarius, ReWOOD co-director.

Timber and its related industries play a significant role in Georgia’s economy. According to the Georgia Forestry Association, the sector contributes $41.3 billion a year to the state’s economy and supports nearly 144,000 direct and indirect jobs.  

EI² Vice President David Bridges said the ReGEN Ag project complements the unit’s broader commitment to supporting socioeconomic development across the entirety of Georgia.  

“When EI² conducted a listening tour of the state, we heard specifically from timber industry owners and companies in South Georgia about the need for finding and developing value-added products,” Bridges said. “This funding provides a chance to explore some of those pathways, while building resilience and boosting prosperity in the southern part of the state.” 

The award also allows Georgia Tech to work closely with two of its educational partners, South Georgia State College and Wiregrass Georgia Technical College, to build workforce opportunities in forestry, agriculture, biomanufacturing, and supply chain management. 

“EDA’s investment, along with significant private industry and local partner commitments, gives us a real opportunity to help rural communities recover from storms and grow stronger,” said Betsy McGriff, EI² program manager. “We’re grateful to EDA and to the partners across South Georgia who came together to make this possible, and we’re excited to get started.”

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Eve Tolpa
eve.tolpa@innovate.gatech.edu

Oct. 27, 2026
Scientists watched as ants encountered earwax with various properties. Candler Hobbs and David Hu

Scientists watched as ants encountered earwax with various properties. Candler Hobbs and David Hu

Here’s a bit of a riddle for you: What has the consistency of both toothpaste and dough, can absorb twice its weight in water and still remain sticky, was used by medieval artists as a binder in paint and has been hypothesized by physicians to repel insects?

It’s earwax.

earwax magnified

Sheep earwax at various levels of magnification. The matte surface is due to overlapping skin cells; the cracks between help absorb water. David Ancalle and David Hu

The medical term for earwax is cerumen, based on keros, the Greek word for beeswax. People have been pondering earwax since ancient times. Ancient hygiene kits including picks and spoons to remove earwax have been found in archaeological sites worldwide.

Even today, it remains a perpetual question: to remove or not to remove your earwax? The answer: Don’t do it. The general medical belief is that ears are self-cleaning, and earwax brings benefits such as lubricating the ear canal, fighting infection and acting like a sticky net to trap debris before it reaches the eardrum. By poking in there you can accidentally make matters worse, pushing wax deeper or scratching your delicate ear canal.

Cerumen impaction – or blocked earwax – affects about a fifth of the American population, leading to 12 million patient visits per year. Blockages are worsened by daily activities such as wearing earbuds or hearing aids.

Another common problem with ears is wedged debris. For children, much of this is self-inflicted, in the form of items such as Legos or erasers. But for many adults who have something stuck in their ear, the debris is insects, such as ants, beetles, flies or cockroaches.

While rare for the average person, insects in the ear occurred with such frequency over the years that physicians began to wonder whether earwax evolved as a natural barrier for insects. But how would such a barrier work?

To answer this question, my Ph.D. student David Ancalle led experiments with earwax and insects. We teamed up with Yuhang Hu and her student Donjing He, experts in the behavior of soft solids such as hydrogels. As mechanical engineers, we wanted to focus on the physics rather than the chemistry of earwax.

Our research, published in the journal Soft Matter, shows that earwax can, in fact, stop insects without the use of dangerous chemicals.

Watching Ants Encounter Earwax

To do experiments, we needed voluminous amounts of earwax.

Initially we obtained earwax from deceased mammals such as pigs, sheep and rabbits. Eventually we turned to our friends and family. A person has only a grain of rice’s worth of earwax in each ear. Due to a mutation in a single gene, East Asians have dry, flaky earwax, while people of African or European descent have wet, squishy earwax.

To simulate an ant climbing into an ear canal, we used a glass beaker with its rim coated in earwax. Ants, like many insects, are climbing specialists, capable of traveling across many kinds of surfaces. To climb rough surfaces, ants have spikes on their feet, like mountain climbers do, that helps them grasp onto ridges and cracks. For smooth surfaces, ants unfurl wet suction cups, which can support 60 times their body weight on smooth glass.

Watching ants trying to climb earwax showed us how strange a substance it really is. Rough earwax slowed them down, but with their claws they made progress.

Rough earwax was a challenge to climb, but feasible for the ants.

But as soon as ants tried to climb smooth earwax, they fell immediately.

A smooth earwax coating made it impossible for ants to scale this microscope slide.

From these experiments, we concluded that earwax had anti-stick properties when in contact with the insects’ feet.

We next conducted experiments with ants crawling through an earwax-coated tunnel, more similar in size to a human ear canal. As the ants attempted to travel through the tunnel, earwax stuck to them. Once coated, they groomed furiously, unwilling to walk until they had removed as much as possible. Even after several minutes of effort, the ants still had earwax sticking to hard-to-reach places.

Greatly magnified image of ant with yellow circles highlighting bits of stuck debris.

Bits of earwax stick to the ant’s body. David Ancalle and David Hu

So, earwax is a paradoxical material: It is simultaneously slippery, thanks to the lipids it contains, but also sticky. Similar to many materials – including ketchup, paint and shampoo – it thins out when it is physically deformed, what physicists call shear-thinning.

Figuring out earwax’s physical properties

The dual qualities of earwax have to do with its unique composition. Earwax is made up of flakes of skin cells held together with a binder. This blend forms a kind of sponge with microscopic holes. When a body overproduces earwax, it heaps up in boulders that ants can easily climb over.

When the substance is smooth, however, ants rely on their wet suction cups. The holes in the earwax absorb the drops produced by the ants, preventing them from attaching. Absorbency makes earwax fundamentally different from commonly used forms of pest control.

Time-lapse video (one fifth actual speed) of earwax absorbing a 1.0 µL droplet of water.

In your local store, you will find sticky surfaces such as fly paper or chemicals that kill the insects. Some researchers look to nature to find bio-friendly alternatives to using chemicals; for example, plants have properties that prevent insects from climbing.

From this study, we learned that the ear is also a model for insect repellency. Earwax is made of natural materials but has a structure that prevents ants from sticking to it.

The porosity of earwax also gives it unique properties. We showed it can absorb twice its weight in water yet still cling to the walls of the ear. This water-absorbing ability may help prevent water from accumulating on the delicate eardrum and fend off infections such as swimmer’s ear.

Scientists don’t yet understand the complex relationship between earwax, water and bacteria. Some studies show that earwax kills bacteria, but other studies find that earwax harbors bacteria.

Our experiments showed that earwax is most challenging to insects when similar to the thin layer of earwax maintained by a healthy human body. Wax would accumulate more in human ears if not for two self-cleaning mechanisms. The skin in the ear canal grows outward like a conveyor belt. Over 8-10 weeks, this process – called epithelial migration – pushes the oldest earwax out of the ear.

Simultaneously, earwax dries over time, transforming from a gooey state to a brittle one that readily cracks. Every time you talk or chew, your ear canal deforms, and physicians found that this movement helps to dislodge the drier, older earwax.

Artificial earwax, with physical properties that mimicked the real thing, was hard for ants to traverse.

As a test of our concept, a member of our team, chemical engineer Rafeen Rahaman, designed an artificial earwax based on the physical structure rather than the chemistry of the earwax. Ants struggled when they touched it, just like when they interacted with natural earwax.

The cost of harvesting natural earwax and our recipe for artificial earwax are not cheap. We don’t foresee our methods replacing insect repellents produced at large scale. But perhaps the concept might inspire new ways to prevent earwax from coating our earbuds or lead to the manufacture of insect control methods using safe formulations.The Conversation

 

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

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Author:

David Hu, Professor of Mechanical Engineering and Biology, Adjunct Professor of Physics, Georgia Institute of Technology 
 

Media Contact:

Shelley Wunder-Smith
shelley.wunder-smith@research.gatech.edu

Oct. 26, 2026
A blond woman wearing a navy blue blouse smiles for a portrait with a staircase in the background

Susan Napier Thomas, Woodruff Professor in the George W. Woodruff School of Mechanical Engineering and the Parker H. Petit Institute for Bioengineering and Bioscience (IBB), received a Golden Helix Deal of the Year Award from Georgia Life Sciences for the Healthy Ameliorated Lymphatics (HEAL) project. The project was recognized in the federal grant category following an award of up to $21.8 million from the Advanced Research Projects Agency for Health (ARPA-H) through its Groundbreaking Lymphatic Interventions and Drug Exploration (GLIDE) program.

Led by Thomas, HEAL brings together scientists, clinicians, and research institutions across the United States, as well as partners in Finland and Australia, to develop new approaches for treating lymphatic diseases.

Read the full story.

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Tracie Troha | Communications Officer

George W. Woodruff School of Mechanical Engineering

Oct. 22, 2026
Illustration of a blue droplet striking a biological sample and ejecting molecules from the surface.
The impact of a high-energy droplet provides enough force to eject molecules from the sample, illustrating the more active extraction process examined in the study. Image courtesy of Analytical Chemistry.
Illustration of blue liquid droplets interacting with molecules on the surface of a biological sample.
In this illustration, molecules move from the sample into a liquid through favorable molecular interactions, representing the gentler of the two extraction processes examined in the study. Image courtesy of Analytical Chemistry.

Andrei Fedorov and his team have found a way to take some of the guesswork out of mass spectrometry imaging (MSI), potentially giving scientists better tools to explore the molecules that influence our health. 

 

A small sample of human tissue can hold a remarkable amount of information. 

Researchers can use it to search for molecular clues associated with cancer, investigate the chemical landscape of the brain, or determine where a drug travels after it enters the body. Mass spectrometry imaging, or MSI, helps answer those questions by mapping where different molecules are located within tissue. 

But there's a catch. Despite MSI's sophisticated capabilities, finding the right conditions to get useful results can still require a considerable amount of trial and error. Georgia Tech professor Andrei Fedorov and Ph.D. student John Sentmanat are working to change that. 

“Mass spectrometry imaging is probably the most powerful tool to enable new discoveries in cell biology and protein biochemistry. It helps provide a direct insight not only to how the body works but also the underlying cause of disease,” said Fedorov, Associate Chair for Graduate Studies and Rae S. and Frank H. Neely Chair in the George W. Woodruff School of Mechanical Engineering. “But the techniques for using it were evolving in a very empirical manner guided by experience and intuition. It’s like shooting arrows in the dark.” 

In response to this barrier, the team has developed what Fedorov likens to a recipe, a framework designed to help researchers understand how different ingredients and conditions work together to produce the molecular information they want. Published in Analytical Chemistry, the approach could make MSI more predictable and give scientists a better starting point for developing new ways to study the molecular workings of the human body. 

“This paper provides a very simple recipe that everyone can read and use. You don’t need to be a chef, or in this case, be able to write complicated equations,” Fedorov said. “It even allows you to come up with a new recipe using our model as the base.” 

Though the framework was designed to be accessible to researchers across the field, Fedorov and Sentmanat didn’t set out to solve a problem for the scientific community — they were trying to solve one in their own lab.  

Gathering Ingredients 

In 2020, Fedorov, who is also a faculty member in the Parker H. Petit Institute for Bioengineering and Bioscience, was awarded $1.2 million from the National Institutes of Health (NIH) for the development of a new imaging instrument that could change the study of tumor growth, disease progression, and cell function.  

The technology, called BeamMap, has continued to develop with additional support from the National Science Foundation Engineering Research Center for Cell Manufacturing Technologies (CMaT), led in part by Georgia Tech. By bringing together multiple imaging techniques, including MSI, BeamMap allows researchers to study both the physical surface of a biological sample and its molecular makeup. 

While developing the instrument, Fedorov and Sentmanat encountered the same challenge that has made MSI difficult to optimize: Under some conditions, they successfully extracted the molecules they wanted to study. Under others, they damaged a sample without getting the information they needed. Rather than continue relying on trial and error, the team decided they needed a theory that could point them toward the right conditions. 

“As we continued developing the instrument, we realized we needed to develop a theory to identify the optimal operating conditions,” Sentmanat said. “What began as an effort to fine-tune our own technology led to a broader theory that can be applied to a wide range of other approaches.” 

Writing the Recipe 

To build the recipe, the team returned to the basic physics that determines when molecules leave a sample and become available for detection. They distilled those complex interactions into a simpler framework, then compared its predictions with experimental data from previously published studies. When the framework held up across different MSI approaches, the researchers realized the recipe could be useful well beyond their own lab. 

“It's all measurable data that any scientist in this field would be able to use,” Fedorov said. “It’s like having a magic tool that allows you, all of a sudden, not to be blind.” 

Next on the Menu 

Fedorov sees the new framework as one step toward making MSI easier to understand and use. By giving researchers a better starting point for choosing how to operate an MSI system, the recipe could help them spend less time searching for the right conditions and more time using the technology to answer biological questions. 

That could become increasingly important as medicine moves toward understanding disease and treatment at the molecular level.  

“More and more, physicians base their diagnoses on much more specific molecular information,” Fedorov said. “The drugs of the future will not be this little pill that you will be given; they will be your own immune cells. Your diagnostic tools have to match that future.” 

For now, the framework is intended primarily as a tool for researchers. But by helping scientists make better use of technologies like MSI, Fedorov hopes the work can contribute to discoveries that ultimately improve how diseases are understood, detected, and treated. 

Season to Taste 

Like any recipe, the framework leaves room for adjustment. The team deliberately simplified some of the complex physics behind MSI to make the framework practical for other researchers, which means it is designed to point them toward promising conditions rather than predict exactly what will happen in every experiment. 

“The theory provides a recipe that researchers can adapt by adding their own flavors,” Sentmanat said. “Rather than prescribing an exact solution, it offers a framework for identifying the right ingredients and refining the conditions to achieve the best results.” 

Researchers can adapt that starting point to their own instruments, samples, and questions. What began as an effort to improve BeamMap could ultimately help others refine existing MSI technologies or develop new approaches of their own.  

“It’s kind of empowering.” 

News Contact

Ashlie Bowman | Communications Manager

Parker H. Petit Institute for Bioengineering and Bioscience

Oct. 12, 2026
Hurricane Harvey dropped several feet of rain on Texas, stranding trucks, people and their pets. Scott Olson/Getty Images

Hurricane Harvey dropped several feet of rain on Texas, stranding trucks, people and their pets. Scott Olson/Getty Images

How much rain can a hurricane make, and how much do big versus small hurricanes make? – Naarah, age 8, Pittsburgh


Hurricanes can produce enormous amounts of rain, adding up to trillions of gallons of water. But the big risk isn’t their total amount – it’s how much rain falls on each location.

One town in Texas got over 60 inches (152 centimeters) of rain from Hurricane Harvey over five days in 2017. That means empty swimming pools there would have filled with rainwater 5 feet (1.5 meters) deep. Hurricane Lane dumped 58 inches (147 centimeters) of rain on Kahuna Falls, Hawaii, the following year.

So, what causes that much rain, and does the size of the storm matter?

I’m an atmospheric scientist who teaches college students about weather at Georgia Tech. Let’s take a look at where a hurricane’s rainfall comes from and some of the questions about hurricanes and their rainfall that scientists are still trying to answer.

First, What Goes Into Creating a Hurricane?

A few ingredients are needed for a hurricane to form: A tropical thunderstorm complex, lots of moisture in the air, winds that don’t change speed much with height, and warm sea surface waters below.

The water temperature is very important.

A map shows water temperatures very warm in the Gulf.

Sea surface temperatures on Oct. 6, 2026, show the very warm water in the western Gulf of Mexico, where a tropical storm grew into Hurricane Isaias the following day. Water that is over 80 degrees Fahrenheit (26.5 degrees Celsius) can fuel hurricanes. NOAA

The magic number for surface water temperature to be able to fuel a hurricane is at least 80 degrees Fahrenheit (26.5 degrees Celsius). At that temperature and above, the warm ocean water evaporates into the air, changing from liquid to vapor. As it makes that change, it provides energy to fuel the storm and also adds moisture to the atmosphere that eventually falls as rain.

When a hurricane moves over warm water, especially when the warm water extends many feet below the surface, the storm pulls in even more energy and moisture.

What Affects Hurricane Size and Rainfall?

This is one of the many questions about hurricanes that atmospheric scientists are working on.

The average hurricane is about 300 miles (482 kilometers) wide, but hurricanes – or typhoons, as they’re called in the western Pacific of the Northern Hemisphere – can be much bigger or smaller.

Super Typhoon Tip in 1979 grew to half the size of the lower 48 U.S. states. It had a radius of about 690 miles (1,110 kilometers), measured from the center of the storm to its outer edges, meaning it was 1,380 miles (2,222 kilometers) wide. Tropical Storm Marco’s gale-force winds in 2008, in comparison, had a radius of 11.5 miles (18.5 kilometers), meaning it was about 23 miles (37 kilometers) across – not much larger than New York City.

A map shows the difference in size between the largest tropical storm, Tip, which took up half the Lower 48 states, and Marcos, which was about the size of New York City.

How the largest tropical cyclone in recorded history and the smallest compare. It’s quite a difference! NOAA

In 2025, scientists published a study showing that hurricane size appears to be influenced by how warm the ocean water is compared to its usual temperature that time of year. So, if a hurricane moves over water that is much warmer than normal, it tends to grow in size rapidly as well as growing in strength.

A recent hurricane that was very large in size was the extremely destructive Hurricane Helene in 2024. As Helene moved over water near Florida that was much warmer than normal for late September, it picked up lots of moisture that it carried all the way to the mountains of North Carolina. Several days of rain from the storm caused catastrophic flooding there.

A satellite image of a very large Hurricane Helene hitting the Florida Gulf coast.

A satellite image shows Hurricane Helene making landfall on Sept. 26, 2024, near Perry, Fla. The storm carried a lot of water far inland, causing flooding in the North Carolina mountains. NOAA National Environmental Satellite, Data, and Information Service

But don’t count the small hurricanes out.

Just because a hurricane is small, that does not mean that it won’t have a big impact.

Hurricane Andrew in 1992 was destructive yet small. Its strongest winds extended only about 90 miles (150 km) from its center when it reached Miami and the Everglades. But that Category 5 hurricane is still known as one of the strongest hurricanes to hit the United States. It destroyed more than 25,000 homes.

A radar image showing the reach of Andrew's rain bands indicates a small storm.

Hurricane Andrew wasn’t very big when it made landfall on Elliott Key, Fla., near Miami, on Aug. 24, 1992, but it was powerful and extremely destructive. National Hurricane Center

A strong storm isn’t necessary for a lot of rain to fall. Tropical Storm Allison in 2001 is still one of the wettest tropical weather events in U.S. history. It caused over US$4 billion in damage in Texas alone, and it didn’t even develop into a hurricane!

So, What Really Leads to the Highest Rainfall Totals?

Hurricanes that move slowly or nearly stop along their path usually cause the biggest rainfall problems because their rain keeps falling over the same location. The rain may be just as intense in a fast-moving hurricane, but it doesn’t stick around as long.

An example is Hurricane Harvey, which hit southeastern Texas in late August 2017. The storm dropped an estimated 27 trillion gallons of water over the state – that’s enough to fill about 40 million Olympic-size swimming pools.

When Harvey stalled over the Houston area, the storm kept drawing moisture from the warm waters of the Gulf. Over four days, it brought precipitation that totals as high as 60.58 inches (1.54 meters) of rain over Nederland, Texas. The extreme rainfall overran the town’s drainage and transformed Nederland into an island.

A map of rainfall totals over several days shows areas around Houston receiving extremely high amounts of rain.

The Houston area received extraordinarily high precipitation totals from Aug. 25 through Sept. 1, 2017, associated with Hurricane Harvey. National Weather Service, Lake Charles, La., office

If a storm moves fast, like Hurricane Michael did in 2018 when it hit the Florida Panhandle, its winds and storm surge can make a big mess, but its rainfall totals will be lower.

Michael was a Category 5 hurricane – more powerful than Harvey and Helene – but its rainfall totals for locations along its path ranged from 6 to 10 inches (15 to 25 centimeters) because it was moving so fast. That’s still a lot of rain, but not compared to Harvey or to Helene, which dumped 30 inches (76 centimeters) of rain on Busick, N.C., over a few days.

A satellite image showing the eye of the hurricane on the Florida coast.

Hurricane Michael passes over Mexico Beach, Fla., on Oct. 10, 2018. The storm moved quickly, so it brought less rainfall to each location compared to slow-moving Hurricane Harvey in Texas the previous year, but its wind and storm surge caused catastrophic damage. NOAA NESDIS

So, a hurricane can be large or small in size and still have a big impact, depending on how slowly it’s moving.

There is still a lot to learn about hurricanes. Some big questions involve what’s known as rapid intensification. That occurs when a tropical storm’s maximum wind speed increases quickly, by 30 knots (about 34.5 miles per hour or 55 kilometers per hour) or more within 24 hours.

When rapid intensification occurs, as happened with hurricanes Harvey, Michael and Helene before they hit land, the storm can surprise coastal residents and become catastrophic.

Hurricanes of all sizes can be dangerous, so accurate forecasting well before they reach land is crucial to help people prepare for their impacts. One very important area of research that today’s scientists – and future scientists – can work on is improving forecasting so coastal residents know what’s coming and have time to prepare.


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And since curiosity has no age limit – adults, let us know what you’re wondering, too. We won’t be able to answer every question, but we will do our best.The Conversation

 

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

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Author:

Zachary Handlos, Senior Academic Professional and Director of Atmospheric and Oceanic Sciences B.S. Degree Program, Georgia Tech 
 

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Shelley Wunder-Smith
shelley.wunder-smith@research.gatech.edu

Oct. 22, 2026
CSE Wang Prakash NIH R01 Grant
CSE Wang Prakash NIH R01 Grant

Public health systems often face a difficult challenge when time, funding, and resources are limited: who should receive care first? These decisions are complicated further by differences in patient needs, disease progression, and social factors like access to care.

A team of computer scientists and doctors led by Georgia Tech aims to change that. Using a system of artificial intelligence (AI) models, the team can help clinicians deliver the right intervention to the right patient at the right time.

Their system uses Restless Multi-Armed Bandit (RMAB) models. While the name is technical, the idea is simple. The RMAB framework helps decision-makers choose the best actions over time when conditions are complex and constantly changing.

This project will tackle Type 2 Diabetes (T2D), building on previous experience expanding RMAB models to support maternal health care in India. Success on this front could lead to treating other chronic illnesses, like hypertension, heart disease, kidney disease, and mental health.

Recognizing the RMAB system’s potential, the National Institutes of Health (NIH)  awarded the group an R01 grant. The award will fund the project with $1.2 million through 2029.

“Our goal is to address the immense challenge of allocating limited health intervention resources in optimal ways,” said Kai Wang, an assistant professor in the School of Computational Science and Engineering (CSE). 

“By leveraging rich contextual data and accounting for social determinants, we envision RMAB models that empower health centers to customize interventions to individual patient profiles. Ultimately, this advances precision medicine and ensures equitable healthcare access for diverse populations.”

T2D is a diabetes condition where the body resists insulin or doesn’t produce enough to regulate blood sugar levels. Left unmanaged, high blood sugar leads to severe health problems like heart disease and stroke, damage to organs like eyes and kidneys, nerve damage, infections, and ulcers.

T2D is challenging to treat because patients have different ages, genetics, lifestyles, and body composition, all of which affect the progress of the disease. Social factors, like transportation and family support, matter just as much as biology to treat the disease.

For these reasons, patients who get the same medications may get very different results. While treatment plans now incorporate new GLP-1 drugs, they remain expensive and difficult to distribute equitably.

“Diabetes is a great example of a complicated, multi-faceted problem. It is a natural playground for the kind of high-dimensional machine learning algorithms we want to build and deploy,” said School of CSE Professor B. Aditya Prakash.

“As a chronic disease, diabetes also creates opportunities for sequential decision-making. Providers are not making just one decision to treat the condition, but rather a series of decisions over time.”

True to its multi-armed name, the team’s framework applies RMAB models across three different thrusts. 

Contextual RMABs use patient histories, biomarkers, demographics, and other data to predict how each individual will respond to an intervention. These models directly provide personalized, data-driven treatments. 

By incorporating patient-specific information, contextual RMAB models can help healthcare providers prioritize the most timely interventions. The goal is to improve patient outcomes while making more efficient use of limited healthcare resources.

Next, the group wants to understand social life and community influence in public health. Families and social networks shape how people manage chronic health conditions.

Network RMABs study and account for the role of social factors in health interventions. For example, the models can recognize when one patient responds better to treatment than others through benefits of their social circle. 

More broadly, hospitals, clinics, government agencies, and nonprofit organizations often share responsibility for the same population in an area. Sometimes, they even compete for the same medications or funding. 

Multi-agent RMABs are the third glass of models aimed at decentralized health systems to make fairer decisions. These tools examine how to coordinate resources and decisions between stakeholders. This in turn leads to policies that encourage cooperation and equitable resource distribution.

“I feel extremely confident in RMABs because we have already seen these frameworks deliver real-world impact,” Wang said. 

“Prior success has proven the value of data-driven decision-making under resource constraints, giving us a strong blueprint to effectively address the nuances and complexities of diabetes.”

Wang previously built RMAB models to expand maternal healthcare for women in India. Working in conjunction with the non-profit organization ARMMAN, the models used healthcare information to identify and reach the most vulnerable populations. 

By integrating RMAB models into ARMMAN mobile health technology, the partnership has reached over 350,000 mothers. This further led to a 31% improvement in engagement with health information.

Compared to maternal healthcare, diabetes is a more nuanced disease with differing causes, symptoms, and treatments. This requires a team effort spanning institutions and backgrounds.  

Dr. Sara Cromer is an endocrinologist at Massachusetts General Hospital (MGH). Experienced diagnosing and treating diabetes, she has studied how social issues and equity influences the disease. MGH will provide much of the patient data to train the RMAB models.

Associate Professor Jian Li studies reinforcement learning at Stony Brook University. His expertise in applying reinforcement learning for resource allocation plays a key part tailoring models for the T2D problem. Wang and Li have previously worked together on RMAB research.

Prakash is an expert in computational epidemiology at Georgia Tech. He has led substantial contributions to the use of AI for networks to predict outbreaks and track disease spread. This experience has led to collaborations with NIH, the Centers for Disease Control and Prevention (CDC), National Science Foundation, and others.

“This project speaks to the interdisciplinary problem-solving we try to achieve in CSE. Despite having different backgrounds, our interest in applying our strengths to real-world healthcare problems unites us together,” Prakash said.

“These problems require experts from all sides. Without computational scientists, we can’t take advantage of available data. We lean on clinical experts to provide the data and expertise needed to guide our methods and make sense of results. Ultimately, these approaches need to be clinically validated if we want to make real-world progress.”

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Bryant Wine, Communications Officer
bryant.wine@cc.gatech.edu

Oct. 22, 2026
Photo of a man of south Asian descent with short dark hair and a smile. He is wearing a light blue button-down shirt and a red and royal blue striped tie. Behind him is the Atlanta skyline.

Raghupathy “Siva” Sivakumar has been appointed interim executive vice president for Research (EVPR) at the Georgia Institute of Technology, effective Nov. 1.

Sivakumar currently serves as Georgia Tech’s vice president of Commercialization and chief commercialization officer, leading the Office of Commercialization. The office serves as the Institute’s central hub for student entrepreneurship, research translation and commercialization, and venture development and scaling, enabling faculty and students to bring new technologies to market. Sivakumar previously served as the founding director of CREATE-X, a Georgia Tech initiative that fosters entrepreneurial confidence and helps students launch real startups. He has also founded and co-founded multiple technology companies and held technical roles in industry. 

In addition to his administrative leadership, Sivakumar is the Wayne J. Holman Chair and professor in the School of Electrical and Computer Engineering (ECE). His research focuses on wireless networking and mobile computing. 

Sivakumar joined ECE in 2000 as an assistant professor. He earned his bachelor’s degree in computer science from Anna University in Chennai, India, and his master’s and doctoral degrees in computer science from the University of Illinois Urbana-Champaign. He is a Fellow of the Institute of Electrical and Electronics Engineers. 

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