Titan, Msholo, Kelly, and Tara are just like any other African elephants — intelligent creatures that require mental stimulation in their everyday lives.

They would normally get this in their natural habitats while foraging for food and staying alert to predators that might target calves.

However, the four elephants reside at Zoo Atlanta, so they don’t have to worry about these things.

That’s why zoo caretakers are always on the lookout for better ways to help their elephants exercise their brains.

The caretakers at Zoo Atlanta found one when they met Arianna Mastali, a Ph.D. student in Georgia Tech’s School of Interactive Computing. Mastali designed an audio enrichment wall to help stimulate Zoo Atlanta’s elephants.

Many zoos build concrete enrichment walls to foster elephant problem-solving and critical thinking. The walls usually have holes for the elephants to reach through with their trunks as they search for food, treats, or playful objects on the other side.

Mastali enhanced Zoo Atlanta’s enrichment wall by adding an interactive audio component. A nearby speaker system emits distinctive low-frequency tones when an elephant sticks its trunk into a hole.

“They’re intelligent creatures that require a lot of complexity in their habitat,” Mastali said. “We wanted to add to that complexity while giving them more control.”

Experimenting in the Wild

Mastali’s system uses cameras and computer vision to detect when an elephant’s trunk is inside a hole and then sends a signal to the speakers to play a sound.

Mastali is a member of the Georgia Tech Animal Lab, directed by School of IC professor Melody Jackson. The lab often uses sensing technology to enhance animal wellness.

Mastali said she tried incorporating sensing devices into her project several times. She constructed an insert made of PVC pipe and attached a sensor to its base that used infrared beams to detect the elephant’s trunk.

However, she said it was difficult to account for the elephants’ strength. Their trunks would break the insert after a day or two. 

She pivoted toward computer vision to remove the risk of damage and keep the enrichment wall as close to natural as possible. 

“A big lesson we learned was that using existing materials the elephants are already familiar with was the best way to do things, and it simplified our design process,” she said.

Shane Rosse, a student in Georgia Tech’s Online Master of Science in Computer Science (OMSCS) program, assisted Mastali with the computer vision component.

Enhancing Environmental Enrichment

Mastali observed the elephants’ behavior at the wall seven days before and seven days after the installation of the audio enrichment system.

The number of times the elephants approached the wall after installation increased by 176%, and time spent at the wall increased by 71%

“We weren’t sure at first if they would care that much, so it was great to see how much time they spent at the wall, especially our less dominant females,” said Kirby Miller, senior elephant caretaker at Zoo Atlanta. “They seem to like it the most.”

Miller said the elephants used to only approach the wall when they knew there was food behind it. That started to change after the audio enrichment system was installed.

“We would be off somewhere else, and we’d hear the speaker playing the sounds, and we knew there wasn’t any food back there,” Miller said. “Tara had her trunk in one of the holes, just listening to the sound. That let us know they do like it, and they’re very curious about it.”

Miller said because elephants have sharp memories and acute senses of hearing and smell, their habitats must be designed with that in mind.

Zoo Atlanta’s African Savanna elephant habitat was redesigned in 2019. In addition to the enrichment wall, it includes a bathing pond, two waterfalls, and swing boom devices that hold hay for elephants to eat as they would in the wild.

Miller said elephants sheltered at any zoo or conservation would benefit from enrichment devices enhanced by technology.

“I think anything they can participate in that gives them choice and control is great for all zoo elephants,” she said. “It depends on the elephants, but with our elephants, they can hear much higher frequencies than we can. That noise isn’t that loud for us, but for them, they’re feeling that noise, and they can hear much more, which makes it more stimulating for them.”

Generative artificial intelligence (AI) is best known for creating images and text. Now, it is helping industries make better planning decisions.

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.”

Pioneering development teams behind innovative products like the Dyson Supersonic hair dryer and SpaceX’s reusable Falcon 9 rocket rely on complex interdisciplinary collaboration among engineers, designers, and project managers. Karthik Ramachandran, Dunn Family Professor of Operations Management, knows that breakthrough products often don’t emerge from the solitary efforts of a lone genius.  

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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Walton County, Georgia, didn’t ask to become a test case for the artificial intelligence (AI) infrastructure boom. Meta, the company behind Facebook, Instagram, and WhatsApp, made the decision for them.

In 2018, the company broke ground in Social Circle, a small town an hour east of Atlanta with about 5,000 residents, to build one of its largest U.S. data centers. It opened in 2020.

Local officials called it a win. Shane Short, president and CEO of the Development Authority of Walton County, said the plant generates about $10 million annually in property tax revenue and has led to road improvements and expanded broadband.

Electric vehicle maker Rivian followed Meta’s lead and began construction on a plant near Social Circle in September 2025, adding to the area’s rapid industrial growth.

But for residents, the shift from a largely rural, agricultural economy to an energy-intensive industrial one has put new pressure on power and water systems.

“They’re seeing higher water and power bills, worse air quality, and very few jobs in return for this, while large corporations get tax benefits,” said Ahmed Saeed, an assistant professor in Georgia Tech’s School of Computer Science, describing why residents in some communities push back on new data center development.

Saeed and Josiah Hester, associate professor of interactive computing and computer science and director of the Center for Advancing Responsible AI, have spent the past year studying the energy, water, and financial demands associated with these facilities, and how those costs are distributed.

Betting on Demand

AI data centers run on specialized chips that use large amounts of electricity. That power generates heat, which requires energy- and water-intensive cooling.

The state is adding capacity based on expected demand, not current use.

Last year, the Georgia Public Service Commission approved an estimated $16 billion expansion for Georgia Power to support that growth. It is expected to produce about 10 gigawatts of electricity at a given time. That’s enough energy to power about 7.5 million homes for a year.

If that demand materializes, the electricity is used. If it doesn’t, the cost still has to be paid.

Grid Stability

“Those workloads can put a very large demand on the grid all at once, and then remove it just as quickly,” Saeed said. “That sudden change is difficult for the system to handle.”

That volatility is a separate issue.

Even if data center operators pay for the infrastructure they use, large swings in demand can still strain grid operations, especially during peak periods or extreme weather.

What Comes Next

Back in Walton County, the Meta facility is already attracting additional data centers.

Each new site adds power and water infrastructure designed to operate for decades.

The servers inside need to be upgraded every few years.

Saeed and Hester said if Georgia wants to remain an AI and cloud hub, the state needs to set the terms and companies need to meet them.

That starts with disclosure — how much power data centers draw from the grid, how that demand spikes, and how much water they use. It includes clear expectations for how those facilities respond when the grid is under stress, and protections for the communities where they’re built.

The researchers maintain that “build it and hope” is not a strategy.

 

Most smart home devices require power one way or another. You have to plug them in, recharge them, or replace their batteries at some point.

Georgia Tech researchers think they have a better way with small metal tags that can signal when a door or drawer is opened, count reps in the gym, or even track bathroom use for elderly relatives. Their tags are battery-free, quiet, inherently private, and cost only a few cents each. They’re smaller than a penny.

Like other kinds of smart home sensors, the tags are designed to be mounted on a cabinet or doorframe, for example, using a 3D-printed base. A small tab is attached to the corresponding door or drawer. When it’s opened, the tab strikes the metal disk, triggering a brief ultrasonic pulse imperceptible to human ears but detectable by a wearable device that logs the activity.

Read the full story on the College of Engineering website.

How did the earliest life on Earth build complex biological machinery with so few tools? A new study explores how the simplest building blocks of proteins — once limited to just half of today’s amino acids — could still form the sophisticated structures life depends on.

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

In about one out of every 1,000 pregnancies, the neural tube, a key nervous system structure,  fails to close properly. Georgia Tech physicists are now helping explain why this happens, having uncovered the physics that drive neural tube closure in a pregnancy’s earliest stages.

Working with collaborators at University College London (UCL), Georgia Tech researchers used computer models to reveal how, during early development, forces generated by cells physically pull the neural tube closed — like a drawstring. This discovery offers new insight into a critical process that, when disrupted, can result in severe birth defects such as spina bifida.

“Understanding a complex developmental process like neural tube closure requires a highly interdisciplinary approach,” said Shiladitya Banerjee, an associate professor in the School of Physics. “By combining advanced biological imaging with theoretical physics, we were able to uncover the mechanical rules that drive cells to close the tube. My lab builds computational models to uncover the physical rules of living systems. The neural tube is an ideal focus because its formation requires incredible mechanical coordination.”

The researchers presented their findings in Current Biology. 

Closing the Gap

The UCL team studied mouse embryos, which develop similarly to humans, and Georgia Tech researchers used that data to construct their models. From the data, they identified the fundamental physics mechanism that enables neural tube closure in part of the brain. This mechanism, called a “purse string,” is made of actin, a pivotal protein that forms a cell’s skeletal structure. As the purse strings tighten, the tube closes.

“These actin molecules are very important because they give rigidity and shape to cells,” Banerjee said.

“During neural tube closure, actin filaments form a ring around the opening and engage molecular motors — proteins that generate forces inside cells,” he said. “As these motors pull on the actin, they generate tension that tightens the ring and draws the tube closed.”

Stretching to Fit

As the actin ring tightens, cells stretch and elongate, causing them to align and move together in a synchronized pattern, like a school of fish. This coordination allows the cells to move faster and more efficiently, increasing tension and driving a feedback loop that helps seal the neural tube.

The team built a computer model to show how this feedback loop leads to successful neural tube formation. Further research using the model could help explain why the neural tube fails to close.

“Physics-based modeling of cell and tissue mechanics allows us to connect the dots between developmental stages in a way that is both robust and quantitative, simulating experiments that are impossible in biological tissues,” said Gabriel Galea, the study co-author and UCL group leader. “In this case, it allowed us to explain how a cell’s mechanical experience impacts its current and future shapes during a critical step of brain development.”

Beyond neural tube development, the findings highlight the power of physics-based modeling to explain complex biological processes that can’t be observed directly. The researchers say this approach could be applied to other stages of human development where forces, motion, and timing are just as critical.

The computational research at Banerjee Lab is funded by the National Institute of General Medical Sciences

Fernanda Pérez-Verdugo, Eirini Maniou, Gabriel L. Galea, Shiladitya Banerjee, “Mechanosensitive feedback organizes cell shape and motion during hindbrain neuropore morphogenesis,” Current Biology, 2026.

DOI: 10.1016/j.cub.2026.02.068 

 

As wind and solar power expand rapidly worldwide, researchers are confronting a growing challenge: how to effectively integrate them into the power grid.

Wind turbines and solar panels have what economists call zero marginal cost, meaning producing additional units of electricity requires no fuel once installed. At the same time, this renewable energy varies greatly with the weather and can create operational challenges for grid operators.

A new review study from Georgia Tech examines how these characteristics are reshaping electricity markets and grid operations — and why addressing the challenge requires cross-disciplinary collaboration.

The study, published in Renewable and Sustainable Energy Reviews, synthesizes more than a decade of research. It analyzes over 200 studies on the engineering, economic, and policy implications of managing renewable energy sources that are both intermittent and effectively zero-cost to operate.

“Wind and solar are now among the lowest-cost sources of electricity in many parts of the world, but integrating them into the grid isn’t simple,” said Matthew Oliver, associate professor in the School of Economics and lead author of the study. “The wind doesn’t always blow, and the sun isn’t always shining, so output can fluctuate significantly, which complicates grid management.”

He added, “Historically, variation in electricity systems generally came from the demand side, and operators could simply ramp generation up or down. Now, we have variability on both supply and demand sides.”

Analyzing the Data

Looking at the problem, Oliver knew he would need to be familiar with engineering concepts to get at the heart of the issue. He created a research team with Daniel Matisoff, professor in the Jimmy and Rosalynn Carter School of Public Policy; Santiago Grijalva, professor in the School of Electrical and Computer Engineering; and graduate student co-authors Maghfira Ramadhani (economics), Oliver Chapman (public policy), and Amanda West (electrical and computer engineering).

Analyzing over 200 studies published since 2010, the team mapped the complex interactions between electricity market design, grid operations, and renewable technologies.

They also explored the economic implications of large amounts of zero-marginal-cost electricity entering wholesale electricity markets. Because wind and solar have very low operating costs, they can lower prices in wholesale electricity markets. That benefits consumers, but it can also make it harder for flexible conventional plants to earn enough revenue to stay available when renewable output falls.

Collaborating Across Disciplines

The team argues that successfully scaling renewable energy will depend on collaboration across traditionally separate fields.

“Engineering constraints affect how electricity markets work, markets influence investment decisions, and policy shapes how those investments happen,” Oliver said. “When it comes to complex topics like this, you can’t really treat engineering, economics, and policy as separate problems. They’re all part of the same system.”

The researchers found that electricity systems with high shares of renewable energy will require coordinated solutions that combine improved engineering practices, market reforms that value flexibility and reliability, and policies that align private investment with long-term decarbonization goals.

“Our hope is that this paper helps researchers across disciplines communicate more effectively,” Oliver said. “If we want electricity systems with high levels of renewable energy to work reliably, then engineers, economists, and policymakers all have to understand how their decisions affect the others.”

 

Citation: Oliver, Matthew E., et al. “Managing Zero-marginal-cost, intermittent renewable energy: A survey of the engineering, economic, and Policy Challenges.” Renewable and Sustainable Energy Reviews, vol. 226, Jan. 2026. 

DOI: https://doi.org/10.1016/j.rser.2025.116334