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.”
The Georgia Tech team’s dielectric lens — similar in spirit to an optical lens — focuses incoming millimeter-wave energy onto an array of tiny antenna elements, enabling both wireless energy capture and high‑speed backscatter communication within the same system.
The system reshapes and reflects existing wireless signals, with each element modulating the reflected signal to enable high-speed data transmission without requiring a traditional transmitter.
At millimeter-wave frequencies, used by 5G and future 6G systems, there is plenty of available bandwidth, but signals at these frequencies are highly directional and sensitive to alignment.
In practice, that means even small misalignment can break the link. This has been a major limitation for real-world deployment. The lens overcomes that constraint by enabling high gain and wide angular coverage simultaneously, without the need for active beam steering.
“Think of it like a camera lens for wireless signals,” Tentzeris said, who is a Ed and Pat Joy Chair Professor in ECE. “It captures energy coming from many different directions and focuses it efficiently onto the device.”
The result is a system that can communicate over a ±55-degree field of view, maintaining strong performance even when the device and the reader are not perfectly aligned.
In controlled experiments, the researchers achieved data rates of up to four Gbps, with sustained gigabit communication at distances of up to 20 meters, using high-order modulation schemes like those used in modern cellular networks.
For a system that doesn’t generate its own signal, those numbers are unexpectedly efficient. The system operates at just 0.08 picojoules per bit — approaching million-fold improvements compared to conventional wireless radios.
“To put that in perspective,” Tentzeris said, “a typical wireless transmitter burns milliwatts of power. This system operates at essentially near-zero power while pushing the data rates 1,000 times higher than what traditional backscatter could do.”
Taken together, the results point to a fundamentally different class of wireless system, according to Tentzeris, one that combines high data rates with ultra-low power in a way that hasn’t been demonstrated before.
Based on standard wireless modeling, the team estimates the technology could support Gbps communication over distances of kilometers when paired with existing 5G millimeter-wave infrastructure, extending high-speed, ultra-low-power links far beyond what has been achievable with backscatter systems.
“That combination is exactly what future wireless networks are moving toward. This capability aligns naturally with next‑generation 6G systems,” said Tentzeris, pointing to the growing importance of Integrated Sensing and Communication (ISAC) and Joint Communication and Sensing (JCAS) frameworks that require simultaneous communication, sensing, and localization.
But speed and efficiency are only part of the story. Because the devices are low-cost, lightweight, and printable, they could be deployed at massive scale on buildings, roads, vehicles, drones, or wearable systems.
In a smart city, thousands of these tags could continuously exchange information about traffic, air quality, or structural health without ever needing batteries. That means dense, always-on sensing and communication without worrying about power or upkeep.
In disaster zones, temporary high-speed networks could be set up almost instantly, without cables or power infrastructure.
“Imagine an ambulance transmitting high-resolution medical images in real time, or first responders building a live digital map of a disaster area,” Joshi said. “You get fiber-like performance, but completely wireless and energy-efficient.”
The architecture also lends itself to intelligent optimization, where AI-based control can be enabled to dynamically enhance signal capture and system efficiency, further expanding performance in large-scale deployments.
“This is really about adding intelligence to anything, anywhere,” Tentzeris said. “When communication becomes this fast, efficient, and scalable, entirely new applications become possible.”
With the core architecture now demonstrated, the ATHENA Lab team is shifting focus from proof‑of‑concept to deployment. That means moving out of the lab and into real-world environments. The next phase includes testing the system outdoors, integrating it onto drones and mobile platforms, and exploring flatter, more compact lens designs that could be easier to mount on real-world infrastructure.
“We’re thinking about how this fits into the broader wireless ecosystem,” Joshi said. “We’ve shown what’s possible. Now the question is how far we can push it in the real world."
Professor Emmanouil “Manos” Tentzeris and Ph.D. student Marvin Joshi hold a lens‑enabled backscatter system that could support battery‑free wireless communication across future smart city infrastructure.
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.
“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:
“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.”
]]>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.”
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.
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.”
]]>The elephant enrichment wall at Zoo Atlanta recently received an upgrade thanks to a Georgia Tech Ph.D. student. Arianna Mastali designed an audio enrichment system that uses computer vision to detect when an elephant sticks its trunk into the enrichment wall as it searches for food. The system then sends a signal to play a unique tone from a nearby speaker that corresponds to each hole. So far, Mastali has found that elephant wall interactions have increased by 176%, and the elephants are visiting the wall even when there isn't food behind it.
]]>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.”
To test Diffusion-DFL, the team ran experiments based on real-world settings, including:
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.”
]]>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.
]]>Published today in the journal Nature Communications, the paper “Trivalent Titanium in High-Titanium Lunar Ilmenite” confirms titanium in a reduced, trivalent state in a black, metal-rich lunar mineral called ilmenite. It’s a state only possible in low-oxygen environments, conditions researchers refer to as “reducing.”
“Models have suggested that these reducing conditions may have varied at different locations and times across the surface of the Moon,” says lead author Advik Vira, a graduate student in the School of Physics who recently earned his doctoral degree. “We hope our microscopy technique can be a valuable step in mapping and understanding the Moon’s 4.5-billion-year history.”
The team anticipates that their technique could be used on many of the lunar samples collected more than 50 years ago by the Apollo missions in addition to the Apollo Next Generation Samples — a group of lunar samples that have been stored under pristine conditions — and new samples from the planned Artemis missions, with Artemis II slated for launch this spring. The technique might also be applicable to samples collected from the far side of the Moon and returned in 2024 by the Chang’e-6 mission.
“The Moon holds clues not only to its own past, but also to the earliest eras of Earth’s evolution — history that has long since been erased from our planet,” Vira says. “This study is a step toward understanding the history of both and a reminder that there is still so much left to learn from the lunar rocks we’ve brought back to Earth.”
The School of Physics research team included corresponding authors Vira and Professor Phillip First; in addition to graduate student Roshan Trivedi; undergraduate students Gabriella Dotson, Keyes Eames, Dean Kim, and Emma Livernois; and Professor Zhigang Jiang, along with Institute for Matter and Systems Materials Characterization Facility Senior Research Scientist Mengkun Tian; School of Chemistry and Biochemistry Senior Research Scientist Brant Jones and Thom Orlando, Regents' Professor in the School of Chemistry and Biochemistry with a joint appointment in the School of Physics.
The Georgia Tech team was joined by Addis Energy Senior Geochemist Katherine Burgess; Macalester College Assistant Professor of Geology Emily First; along with Lawrence Berkeley National Laboratory Research Scientist Harrison Lisabeth, Senior Scientist Nobumichi Tamura, and Postdoctoral Fellow Tyler Farr, who recently earned a Ph.D. from Georgia Tech’s George W. Woodruff School of Mechanical Engineering.
The investigation began with a dark gray rock called a lunar basalt. Formed when ancient magma erupted on the Moon’s surface, minerals crystallized as it cooled — preserving key information in their structures. Billions of years later, the rock was brought to Earth by the 1972 Apollo 17 mission, where a small piece is now stored at Georgia Tech’s Center for Lunar Environment and Volatile Exploration Research (CLEVER), a NASA Solar System Exploration Research Virtual Institute (SSERVI) center led by Orlando.
As a NASA virtual institute, CLEVER supports researchers exploring lunar conditions and developing tools for the upcoming crewed Artemis missions, and provided the lunar samples for this research. The SSERVI also plays a critical role in training the next generation of planetary researchers: both Vira and Farr earned their Ph.D.s while on the CLEVER team.
“At CLEVER, we are very interested in understanding the impacts of space weathering,” Vira says. “We implemented modern sample preparation and advanced microscopy techniques to image samples at the atomic level, and were curious to apply it more broadly to the collection of Apollo rocks in the Orlando Lab. This sample caught our attention.”
“When we imaged an ilmenite crystal from the lunar basalt, what struck us first was how uniform and perfect the crystal structure was,” he recalls. “We found no defects from space weathering and instead saw an undamaged, pristine crystal — undisturbed for 3.8 billion years.”
To investigate further, the team analyzed small chips of the rock with Burgess, a member of the RISE2 SSERVI team and then a geologist at the U.S. Naval Research Laboratory. Using state-of-the-art electron microscopy and spectroscopy techniques, Vira determined the oxidation state of the elements in the ilmenite present.
In spectroscopy measurements, each element leaves a distinct ‘signature,’ Vira explains. “When we brought our results back to Georgia Tech’s Materials Characterization Facility, Mengkun (Tian) noticed something unusual: the signature showed titanium might be present in the trivalent state.”
The presence of trivalent titanium had long been suspected in this lunar mineral. The team was intrigued.
With funding from Georgia Tech’s Center for Space Technology and Research (CSTAR), Vira returned to the U.S. Naval Research Laboratory to analyze additional samples. The results confirmed that more titanium was present than the mineral’s formula (FeTiO₃) predicts — indicating a portion of the titanium present was trivalent.
“That led me to place our measurements in terms of the broader geological context,” Vira shares. Working with First, Vira explored how ilmenite with trivalent titanium could help reconstruct the nature of ancient magmas from the Moon, especially the chemical availability of oxygen.
“Because its location on the Moon was noted during the Apollo mission, we know exactly where this rock is from, and we can determine how old the rock is,” he explains. “When coupled with our trivalent titanium measurements, we can use that information to estimate the reducing conditions for this specific region at the specific time our rock formed.”
If the upcoming Artemis missions return samples suitable for the team’s technique, these rocks could provide a new window into ancient lunar geology. The research also highlights that many lunar samples already on Earth could be reexamined to look for trivalent titanium.
“There is still so much to learn from the lunar samples we have already brought to Earth,” Vira says. “It’s a testament to the long-term value of each sample return mission. As technology continues to advance, this type of work will continue to give us critical insights into our planet and our place in the universe for years to come.”
DOI: 10.1038/s41467-026-69770-w
Funding: This work was directly supported by the NASA SSERVI under CLEVER. Researchers were also supported by the NASA RISE2 SSERVI and the Heising-Simons Foundation. Funding for collaborations between the U.S. Naval Research Laboratory and Georgia Tech for the investigation of lunar minerals was provided by the Georgia Tech Center for Space Technology and Research. Sample preparation was performed at the Georgia Tech Institute for Matter and Systems, which is supported by the National Science Foundation. This work utilized the resources of the Advanced Light Source, a user facility supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, and was supported in part by previous breakthroughs obtained through the Laboratory Direct.
]]>Selena Langner
College of Sciences
Georgia Institute of Technology
The Georgia Scientific Computing Symposium (GSCS) held its annual meeting on February 21 in Athens. Since 2009, the event has hosted researchers from across the Peach State to showcase homegrown advances in scientific computing.
The symposium highlighted Georgia’s reputation as a computing innovation hub. People from around the world come to Georgia universities to lead computing research. By advancing science, engineering, medicine, and technology, their work improves communities at home and abroad.
Faculty and students from Georgia Tech, UGA, Georgia State University, and Emory University presented at the symposium. Georgia Tech participants came from the colleges of Computing, Engineering, and Sciences.
This year’s organizers agreed to meet in Atlanta for the 2027 symposium. Georgia Tech’s School of Computational Science and Engineering (CSE) will host the 19th GSCS.
“From healthcare to computer chip design, scientific computing underpins many of the technological advances we see in our lives,” said Professor Edmond Chow, associate chair of the School of CSE.
“Scientific computing provides the mathematical models, simulations, and data‑driven tools that make modern innovation possible. It allows people to analyze complex systems, test ideas virtually before building them, and make faster, more accurate decisions across nearly every sector of society.”
Professor Haomin Zhou and Assistant Professor Helen Xu delivered two of the symposium’s five plenary talks.
Zhou presented a new method for solving the Schrödinger equation, a landmark equation in quantum mechanics. Drawing inspiration from the mathematics used in generative artificial intelligence models, his approach develops an algorithm that more effectively simulates waves, particle motion, and other physical systems.
Xu focused on improving how computers move and organize data during complex calculations. Her work uses “cache-friendly” layouts that help computers access data more efficiently, boosting performance for scientific and engineering applications.
“Speaking at GSCS was a great opportunity,” Xu said. “The symposium fostered connections within the scientific computing community and gave us a chance to share exciting research.”
The symposium showcased student work through a poster blitz and a poster session. During the blitz, 36 students each had one minute to introduce their research to the full audience. They then shared more details about their research during the poster session.
The student projects showed the range of fields supported by scientific computing. The session also provided attendees with an opportunity to connect and expand their professional networks, helping grow the field’s future impact.
“As an aerospace engineer by training and aspiring computational scientist, GSCS gave me the platform to network with other researchers in the field while showcasing my own research,” said M.S. student Kashvi Mundra.
“I was able to connect with scientists across different disciplines whose work intersects with my own in unexpected ways. Those conversations pushed my thinking beyond my own lab's perspective, helping me see my work on physics-informed machine learning for inverse problems in a broader scientific computing context.”
Georgia Tech students who presented posters included:
Abir Haque (CSE), Massively Parallel Random Phase Approximation Correlation Energy via Lanczos Quadrature
Antonio Varagnolo (CSE), Physics-Enhanced Deep Surrogates for the Phonon Boltzmann Transport Equation
Ben Burns (CSE), Infinite-Dimensional Stein Variational Inference with Derivative-Informed Neural Operators
Ben Wilfong (CSE), Shocks without Shock Capturing; Compressible Flow at 1 quadrillion Degrees of Freedom without Loss of Accuracy
Daniel Vickers (CSE), Highly-Parallel Fluid-Solid Interactions for Compressible Flows
Eric Fowler (CSE), High-Performance Tensor Contractions in Computational Chemistry
Haoran Yan (Math), Understanding Denoising Autoencoders through the Manifold Hypothesis: A Geometric Perspective
Kashvi Mundra (CSE), Autoregressive Multifidelity Neural Surrogate Modeling under Scarce Data Regimes
Sebastián Gutiérrez Hernández (Math/CSE), PDPO: Parametric Density Path Optimization
Vivian Zhang (AE), Multifidelity Operator Inference: Non-Intrusive Reduced Order Modeling from Scarce Data
Xian Mae Hadia (CSE), Data Efficiency of Surrogate Models: Learning Physics Data from Full Field Data vs. Inductive Bias from Approximate PDE Solvers
Xiangming Huang (CSE), Neural Operator Accelerated Evolutionary Strategies for PDE-Constraint Optimization
Zhaiming Shen (Math), Understanding In-Context Learning on Structured Manifolds: Bridging Attention to Kernel Methods
Zhongjie Shi (Math), Towards Understanding Generalization in DP-GD: A Case Study in Training Two-Layer CNNs
]]>The Georgia Scientific Computing Symposium (GSCS) held its annual meeting on February 21 in Athens. Since 2009, the event has hosted researchers from across the Peach State to showcase homegrown advances in scientific computing.
The symposium highlighted Georgia’s reputation as a computing innovation hub. People from around the world come to Georgia universities to lead computing research. By advancing science, engineering, medicine, and technology, their work improves communities at home and abroad.
]]>New cybersecurity research from the Georgia Institute of Technology, however, revealed that crews aboard commercial vessels were often not adequately prepared to manage cyberattacks effectively due to systemic training gaps.
The findings are based on interviews conducted by researchers with more than 20 officer-level mariners to assess the maritime industry’s readiness to handle cybersecurity attacks at sea.
"Historically, cybersecurity research has focused heavily on cyber-physical systems like cars, factories, and industrial plants, but ships have largely been overlooked,” said Anna Raymaker, Ph.D. student and lead researcher.
“That gap is concerning when more than 90% of the world’s goods travel by sea. Recent incidents, from GPS spoofing to ships linked to subsea cable disruptions, show that maritime systems are increasingly part of the global cyber threat landscape.”
The researchers proposed four practical strategies to strengthen maritime cyber defenses and close the training gaps. Their findings were presented recently at the ACM SIGSAC Conference on Computer and Communications Security (CCS).
Many of those interviewed for the study described current cybersecurity training as “boilerplate” — generic modules that don’t reflect real shipboard risks.
Researchers recommend:
At sea, crews can’t simply escalate a cyber incident to a shore-based IT department and wait. Operational resilience requires onboard readiness.
Researchers recommend:
Related Article: When GPS lies at sea: How electronic warfare is threatening ships and their crews by Anna Raymaker
Maritime cybersecurity regulations are often reactive and fragmented. Researchers argue the industry needs a cohesive, domain-specific framework.
Key recommendations include:
Finally, the researchers stress that long-term resilience requires deeper technical research focused on maritime systems.
Priority areas include:
Cyber threats at sea are no longer hypothetical. Mariners report real-world incidents ranging from GPS spoofing to ransomware that disrupts global trade.
“Through our interviews with mariners, I saw firsthand how much dedication and pride they take in their work,” said Raymaker. “Our goal is for this research to serve as a call to action for researchers, policymakers, and industry to invest more attention in maritime cybersecurity and support the people who risk their lives every day to keep global trade, food, and energy moving."
A Sea of Cyber Threats: Maritime Cybersecurity from the Perspective of Mariners was presented at CCS 2025. It was written by Raymaker and her colleagues, Ph.D. students Akshaya Kumar, Miuyin Yong Wong, and Ryan Pickren; Research Scientist Animesh Chhotaray, Associate Professor Frank Li, Associate Professor Saman Zonouz, and Georgia Tech Provost and Executive Vice President for Academic Affairs Raheem Beyah.
]]>Communications Officer II School of Cybersecurity and Privacy
]]>Amino acids also play a critical role commercially where they are manufactured and added to pharmaceuticals, dietary supplements, cosmetics, animal feeds, and industrial chemicals — an energy-intensive process leading to greenhouse gas emissions, resource consumption, and pollution.
A landmark new system developed at Georgia Tech could lead to an alternative: a commercially scalable, environmentally sustainable method for amino acid production that is carbon negative, using more carbon than it emits.
The breakthrough builds on a method that the team pioneered in 2024 and solves a key issue – increasing efficiency to an unprecedented 97% and reducing the bioprocess cost by over 40%. It’s the highest reported conversion of CO2 equivalents into amino acids using any synthetic biology system to date.
Published in the journal ACS Synthetic Biology, the study, “Cell-Free-Based Thermophilic Biocatalyst for the Synthesis of Amino Acids From One-Carbon Feedstocks,” was led by Bioengineering Ph.D. student Ray Westenberg and Professor Pamela Peralta-Yahya, who holds joint appointments in the School of Chemistry and Biochemistry and School of Chemical and Biomolecular Engineering. The team also included Shaafique Chowdhury (Ph.D. ChBE 25) and Kimberly Wennerholm (ChBE 23); alongside University of Washington collaborators Ryan Cardiff, then a Ph.D. student and now a Chain Reaction Innovations Fellow at Argonne National Laboratory, and Charles W. H. Matthaei Endowed Professor in Chemical Engineering James M. Carothers; in addition to Pacific Northwest National Laboratory Synthetic Biology Team Leader Alexander S. Beliaev.
"This work shifts the narrative from simply reducing carbon emissions to actually consuming them to create value,” says Peralta-Yahya. “We are taking low-cost carbon sources and building essential ingredients in a truly carbon-negative process that is efficient, effective, and scalable.”
The work builds on the cell-free technology the team used in their earlier study. “Previously, we discovered that a system that uses the machinery of cells, without using actual living cells, could be used to create amino acids from carbon dioxide,” Peralta-Yahya explains. “But to create a commercially viable system, we needed to increase the system’s efficiency and reduce the cost.”
The team discovered that bits of leftover cells were consuming starting materials, and — like a machine with unnecessary gears or parts — this limited the system’s efficiency. To optimize their “machine,” the team would need to remove the extra background machinery.
"Leftover cell parts were using key resources without helping produce the amino acids we were looking for,” says Peralta-Yahya. “We knew that heating the system could be one way to purify it because heat can denature these components.”
The challenge was in how to protect the essential system components from the high temperatures, she adds. “We wondered if introducing enzymes produced by a heat-loving bacterium, Moorella thermoacetica, might protect our system, while still allowing us to denature and remove that inefficient background machinery.”
The results were astounding: after introducing the enzymes, heating and “cleaning” the system, and letting it cool to room temperature, synthesis of the amino acids serine and glycine leaped to 97% yield — nearly three times that of the team’s previous system.
To make the system viable for large-scale use, the team also needed to reduce costs. “One of the most costly components in this system is the cofactor tetrahydrofolate (THF),” Peralta-Yahya shares. “Reducing the amount of THF needed to start the process was one way to make the system more inexpensive and ultimately more commercially viable.”
By linking reaction steps so waste from one step fueled the next, the team devised a method to recycle THF within the system that reduces the amount of THF needed by five-fold — lowering bioprocessing costs by 42%.
“This decrease in cost and increase in yield is a critical step forward in creating a method with real potential for use in industry and manufacturing,” Peralta-Yahya says. “This system could pave the way for moving this carbon-negative technology out of the lab and onto the continuous, industrial scale."
Funding: The Advanced Research Project Agency-Energy (ARPA-E); U.S. Department of Energy; and the U.S. Department of Energy, Office of Science, Biological and Environmental Research Program.
DOI: https://doi.org/10.1021/acssynbio.5c00352
]]>Selena Langner
College of Sciences
Georgia Institute of Technology
Set in the heart of Tech Square on the Georgia Tech campus, this year’s event explores how energy systems, materials, technologies, supply chains, and policy must evolve in response to AI’s accelerating impact. As digital infrastructure expands and computation intensifies, the need for reliable, resilient, and sustainable power has never been more urgent.
“Energy Day reflects Georgia Tech’s strength in connecting world-class research in materials and components with the infrastructure and partnerships needed to translate discovery into scalable energy technologies that serve industry, society, and the future economy,” said Eric Vogel, executive director of the IMS and the Hightower Professor in Materials Science and Engineering.
Energy Day 2026 also marks an important milestone with the introduction of its first group of corporate sponsors: GE Vernova, Southern Company, Georgia Power, ExxonMobil, Southwire Spark, Gems Setra, and Tektronix. Their support reflects a shared commitment to advancing energy solutions.
“Tektronix is excited to be part of Energy Day because advancing the future of energy starts with precise measurement and trusted insights,” said Christopher Bohn, president of Tektronix. “From power electronics and high voltage systems to grid scale renewables and AI driven control technologies, the breakthroughs discussed here directly align with the innovations we support through our products and solutions. Collaborating with Georgia Tech allows us to engage early with emerging research and the next generation of engineers—critical collaborators in building a cleaner, smarter, and more resilient energy ecosystem.”
The keynote address will be delivered by Vanessa Z. Chan, a nationally recognized leader at the intersection of innovation, commercialization, and emerging technologies. Chan will provide insights on accelerating technological discovery, emphasizing how AI is transforming energy and materials design. She will discuss how commercialization strategies must rapidly evolve across multidisciplinary energy domains from grid modernization to advanced batteries and clean manufacturing.
Building on the themes introduced in the keynote, the program transitions into a fireside chat with Georgia Tech EVPR Tim Lieuwen featuring Amit Kulkarni and Jim Walsh. Kulkarni is vice president of Product Management and Strategy for the Gas Power business within GE Vernova, where he oversees the world’s largest portfolio of power generation equipment. Walsh, vice president of GE Vernova’s Consulting Services, leads teams providing innovative solutions across the full spectrum of power generation, delivery, and utilization.
Next comes a policy-focused panel that will explore the surge in power demand driven by AI, how the United States is addressing today’s most urgent energy challenges, and the long-term implications of today’s decisions for a sustainable energy future. Bringing together leading voices in U.S. environmental and energy policy, the panel features Joe Aldy of Harvard University and former special assistant to the president for Energy and Environment; Al McGartland of New York University’s Institute for Policy Integrity and former Environmental Protection Agency lead economist and director of the National Center for Environmental Economics; and Kevin Rennert, fellow and director of the Comprehensive Climate Strategies Program at Resources for the Future and former staff member on the U.S. Senate Committee on Energy and Natural Resources.
The second panel focuses on critical materials — the foundation of advanced energy systems and digital technologies. As AI, data centers, and advanced energy technologies drive demand for critical materials, securing them now requires integration and coordination across the entire value chain. Panelists include Rachel Galloway, British consul general in Atlanta; Vijay Murugesan, head of Materials Intelligence and Digital Innovation at Amazon; Colin Spellmeyer, executive strategic sourcing leader at GE Vernova; Charles Sims, Tennessee Valley Authority Distinguished Professor of Energy and Environmental Policy at the University of Tennessee; and Nortey Yeboah, principal engineer at Southern Company. Together, they will offer perspectives on the policy and economic frameworks shaping the energy supply chain, from developing raw resources to manufacturing the technologies essential to future energy systems.
In the afternoon, participants can dive deeper into specialized topics through three focused technical tracks.
“Meeting the rapidly rising electricity demand driven by AI requires bold ideas, coordinated action, and research that moves at the speed of innovation,” said Yuanzhi Tang, executive director of the SEI. “Energy Day 2026 brings together the people and expertise needed to shape resilient, sustainable energy systems for the future. At Georgia Tech, we see this event as a catalyst for new partnerships, new solutions, and a shared commitment to strengthening the nation’s energy foundation.”
Energy Day 2026 is designed for researchers advancing emerging energy technologies, policymakers navigating shifting regulatory and geopolitical landscapes, industry professionals seeking insight into emerging tools and supply chains, and students preparing to enter one of the most consequential sectors of the decade. It also welcomes anyone interested in AI, sustainability, electrification, and critical materials.
Join us to explore the future of energy. To learn more and register, visit: Energy Day 2026.
]]>In the International Disaster Reconnaissance (IDR) course, students now use Filio, a platform built by School of Computing Instruction Senior Lecturer Max Mahdi Roozbahani, to capture immersive 360° media, photos, and video that transform real disaster sites in India and Nepal into living digital classrooms.
Offered by the School of Civil and Environmental Engineering and taught by IDR director and Regents’ Professor David Frost, the course pairs traditional fieldwork with Roozbahani’s expertise in immersive technology and data-driven learning, transforming on-the-ground observations into reusable, interactive educational resources.
Disasters are not only physical events; they are also information events, Roozbahani says. Effective response and long-term resilience depend on the ability to observe, record, and communicate critical data under pressure. Georgia Tech’s IDR course pairs structured on-campus preparation with international field experiences, enabling students to study the cascading effects of major disasters, including how local building practices, governance, and culture shape damage and recovery.
“When students step into a disaster zone, they learn quickly that resilience is a systems problem: physical, social, and informational. Our job in computing is to help them capture and reason about that system responsibly,” Roozbahani said.
During spring break last year, the cohort traveled along the Teesta River corridor in Sikkim, India. The region is shaped by steep terrain, fast-moving water, and critical infrastructure in narrow valleys.
The visit followed the October 2023 glacial lake outburst flood from South Lhonak Lake, which destroyed the Teesta III hydropower dam and impacted downstream towns, including Dikchu and Rangpo. Field stops across India included Lachung, Chungthang, Dikchu, Rangpo, Gangtok, and New Delhi.
Students explored both upstream and downstream consequences.
Upstream, the team examined how steep terrain and river confinement amplify flood forces, creating cascading risks for infrastructure. Using Filio’s interactive 360° media, students captured conditions in Lachung and Chungthang, allowing viewers to explore the landscape through a 360° photo and 360° video that reveal how topography and river dynamics intensify disaster impacts.
They studied community-scale effects downstream, including damaged buildings, disrupted access, and prolonged recovery timelines.
Rangpo offered a glimpse of recovery in motion, with materials staged for rebuilding bridges and roads essential to commerce and emergency response.
Students documented their field experience using Filio, an AI-powered visual reporting platform developed by Roozbahani through Georgia Tech’s CREATE-X ecosystem. Filio captures high-resolution photos, video, and 360° immersive media, preserving both the facts and the context of disaster sites; what the site felt like, what was lost, and what communities prioritized in recovery.
“A 360° capture lets students return months later and ask better questions. That second look is where learning accelerates,” Roozbahani said.
Supported by alumni and faculty mentors, including Tech alumnus Chris Klaus and Georgia Tech mentor Bill Higginbotham, the platform is evolving into a reusable educational library for future courses on immersive technology, responsible AI, and global resilience.
The course concluded in Kathmandu, Nepal, where students examined how heritage, governance, and the everyday use of public space shape resilience.
Through Filio’s immersive documentation — including a 360° photo and 360° video from Kathmandu — the focus broadened from hazard impacts to cultural context, highlighting how recovery is not only about rebuilding structures, but also about preserving identity, memory, and community.
Frost and Roozbahani envision the IDR immersive media library as a reusable resource for students even when they cannot travel, supporting future courses on immersive technology, responsible AI, and global resilience. Spring 2026 cohorts will continue to build on this foundation by documenting, analyzing, and sharing insights that can improve education and real-world disaster response.
In the International Disaster Reconnaissance (IDR) course, students now use Filio, a platform built by School of Computing Instruction Senior Lecturer Max Mahdi Roozbahani, to capture immersive 360° media, photos, and video that transform real disaster sites in India and Nepal into living digital classrooms.
]]>Ryan Punamiya (CS 2025) and Summer Abramson, a third-year computational media student, have been honored by the Computing Research Association (CRA) through its 2025–2026 Outstanding Undergraduate Researcher Award (URA) program.
Punamiya was named a runner-up for the prestigious award, while Abramson received an honorable mention among hundreds of applicants from universities across North America.
The CRA Outstanding Undergraduate Researcher Award program recognized eight awardees in 2026, along with eight runners-up, nine finalists, and over 200 honorable mentions from thousands of applications.
Punamiya knew early on that he didn’t want to wait until starting his Ph.D. to do meaningful and impactful robotics research.
Punamiya joined the Robot Learning and Reasoning Lab (RL2) directed by Assistant Professor Danfei Xu. While there, he contributed to the lab’s Meta-sponsored EgoMimic project, which trains robots to perform human tasks using recordings captured by Meta’s Project Aria research glasses.
Punamiya is also the first author of a paper accepted to the 2025 Conference on Neural Information Processing Systems (NeurIPS), one of the world’s most prestigious artificial intelligence (AI) and machine learning conferences.
“Ryan is the strongest undergraduate I've worked with,” Xu said, “including students who went on to Stanford, Berkeley, and leadership roles in major tech companies. He’s already operating at the level of a strong third-year Ph.D. student.”
Punamiya said it was a challenge to balance his undergraduate coursework with his research in Xu’s lab.
“You get out how much you put in,” he said. “I built my class schedule to give myself as much time to do research as possible. It also boils down to having the right research mentors.
“(Xu) never saw me as an undergrad who’s just there to do grunt work. I was fortunate he saw my curiosity and cultivated me as a researcher. That’s really how you get more undergrads motivated to research — giving them the chance to be independent and explore ideas of their own.”
Punamiya said his work in Xu’s lab has already helped him identify the research areas he wants to focus on as he considers his next steps. He will continue developing generalized training models for robots using human data so they can perform tasks instantly upon deployment.
"The amount of data needed to train a robot is difficult to obtain even for top industry companies," he said. "We have embodied robot data available in billions of humans. With the advent of extended reality devices, we can get a scalable source of diverse interactions within environments."
Punamiya graduated in December and recently started an internship at Nvidia. He mentioned he has been accepted into several Ph.D. programs, including Georgia Tech, and he is choosing where to continue his research.
“It’s the first time my research has been acknowledged externally by the robotics community,” he said. “It’s good to know the problem I’m working on is important, and that motivates me. Robotics is an exciting field. We are doing things now that two years ago were difficult to do.”
Abramson conducts research in the People-Agents Research for Computing Education (PARCE) Laboratory under the mentorship of Pedro Guillermo Feijóo-García, a faculty member in the School of Computing Instruction. He and the Associate Dean for Undergraduate Education, Olufisayo Omojokun, nominated her for the award.
Her work focuses on the intersection of computing education and human-AI interaction, where she’s been exploring ways to create more equitable technology.
“This is such a huge milestone, and I couldn't be prouder of Summer,” Feijóo-García said. “Mentoring her for almost two years has been an amazing experience.”
Abramson has received the Georgia Tech President’s Undergraduate Research Award (PURA) twice, which supports her research exploring how user-centered design curricula can help address attrition among women in computing.
“I’ve had the amazing opportunity to pursue research at the intersection of student identity, community belonging, and how we can build tools that support our diverse student population,” Abramson said.
“Dr. Pedro and I have a goal to build community through a human-first approach, and I could not be more grateful for his support and guidance in my own journey. The CRA highlights the best of what the computing discipline has to offer, and I am incredibly honored for our work to be recognized.”
Abramson will spend the summer researching how user-centered design curricula can help promote confidence, belonging, and retention for women in computing.
Nominees for the PURA program were recognized for contributing to multiple research projects, authoring or coauthoring papers, presenting at conferences, developing widely used software artifacts, and supporting their communities as teaching assistants, tutors, and mentors.
School of Computing Instruction Communications Officer Emily Smith contributed to this story.
Main Photo: Ryan Punamiya works with a robot during the 2025 International Conference on Robotics and Automation in Atlanta. Photo by Terence Rushin/College of Computing.
]]>Punamiya was named a runner-up for the prestigious award, while Abramson received an honorable mention among hundreds of applicants from universities across North America.
The CRA Outstanding Undergraduate Researcher Award program recognized eight awardees in 2026, along with eight runners-up, nine finalists, and over 200 honorable mentions from thousands of applications.
]]>Georgia Tech researchers say HPC and artificial intelligence (AI) advances this year are poised to improve how people power their homes, design safer buildings, and travel through cities.
According to Qi Tang, scientists will take progressive steps toward cleaner, sustainable energy through nuclear fusion in 2026.
“I am very hopeful about the role of advanced computing and AI in making fusion a clean energy source,” said Tang, an assistant professor in the School of Computational Science and Engineering (CSE).
“Fusion systems involve many interconnected processes happening across different scales. Modern simulations, combined with data-driven methods, allow us to bring these pieces together into a unified picture.”
Tang’s research connects HPC and machine learning with fusion energy and plasma physics. This year, Tang is continuing work on large-scale nuclear fusion models.
Only a few experimental fusion reactors exist worldwide compared to more than 400 nuclear fission reactors. Tang’s work supports a broader effort to turn fusion from a promising idea into a practical energy source.
Nuclear fusion occurs in plasma, the fourth state of matter, where gas is heated to millions of degrees. In this extreme state, electrons are stripped from atoms, creating a hot soup of fast-moving ions and free electrons. In plasma, hydrogen atoms overcome their natural electrical repulsion, collide, and fuse together. This releases energy that can power cities and homes.
Computers interpret extreme temperatures, densities, pressures, and plasma particle motion as massive datasets. Tang works to assimilate these data types from computer models and real-world experiments.
To do this, he and other researchers rely on machine learning approaches to analyze data across models and experiments more quickly and to produce more accurate predictions. Over time, this will allow scientists to test and improve fusion reactor designs toward commercial use.
Beyond energy and nuclear engineering, Umar Khayaz sees broader impacts for HPC in 2026.
“HPC is the need of the day in every field of engineering sciences, physics, biology, and economics,” said Khayaz, a CSE Ph.D. student in the School of Civil and Environmental Engineering.
“HPC is important enough to say that we need to employ resources to also solve social problems.”
Khayaz studies dynamic fracture and phase-field modeling. These areas explore how materials break under sudden, rapid loads.
Like nuclear fusion, Khayaz says dynamic fracture problems are complex and data-intensive. In 2026, he expects to see more computing resources and computational capabilities devoted to understanding these problems and other emerging civil engineering challenges.
CSE Ph.D. student Yiqiao (Ahren) Jin sees a similar relationship between infrastructure and self-driving vehicles. He believes AI will innovate this area in 2026.
At Georgia Tech, Jin develops efficient multimodal AI systems. An autonomous vehicle is a multimodal system that uses camera video, laser sensors, language instructions, and other inputs to navigate city streets under changing scenarios like traffic and weather patterns.
Jin says multimodal research will move beyond performance benchmarks this year. This shift will lead to computer systems that can reason despite uncertainty and explain their decisions. In result, engineers will redefine how they evaluate and deploy autonomous systems in safety-critical settings.
“Many foundational problems in perception, multimodal reasoning, and agent coordination are being actively addressed in 2026. These advances enable a transition from isolated autonomous systems to safer, coordinated autonomous vehicle fleets,” Jin said.
“As these systems scale, they have the potential to fundamentally improve transportation safety and efficiency.”
]]>Georgia Tech researchers say HPC and artificial intelligence (AI) advances this year are poised to improve how people power their homes, design safer buildings, and travel through cities.
]]>Led by School of Computing Instruction faculty member and Georgia Tech alumna Jennifer Whitlow, the course gives students a founder’s perspective on building technology that meets real user needs.
Unlike the traditional CS Junior Design course where teams work with sponsors, students in the entrepreneurial track act as their own clients. They begin the semester with no predetermined problem and follow a structured process, which is anchored by deliverables that reflect professional expectations.
“Students come in with nothing,” Whitlow said. “They identify a problem, conduct customer discovery, realize which assumptions were wrong, refine their direction, figure out what to build and then build it. And they own it 100 percent.”
Customer-discovery interviews ensure every idea is grounded in real user needs, and the semester culminates in a fully functioning prototype paired with a written justification of the decisions behind it. This combination of development and reflection gives students a framework that mirrors startup practices.
To further simulate a startup environment, Whitlow recruited alumni coaches with startup or executive experience. Coaches were paired with teams based on their areas of expertise, advising anywhere from one to four groups. The roster includes a former chief technology officer and longtime startup advisor, along with alumni startup founders.
Students also incorporate AI tools into development, accelerating early prototype work while still making critical decisions themselves.
“AI can accelerate the early stages,” Whitlow said. “But students have to understand their design well enough to guide it. AI doesn’t replace their decision-making.”
Sixteen teams completed the entrepreneurial capstone this fall.
The top two scoring projects earned automatic acceptance into CREATE-X Launch, Georgia Tech’s startup accelerator:
These teams showcase the program’s ability to quickly bring student ideas to a level that’s ready for real-world startup incubation.
One team that exemplifies how the capstone’s structure supports innovation is LunchBox. Created by computational media major Abigail Rhea and her teammates, LunchBox helps parents and caregivers of neurodivergent children navigate limited safe-food options.
The idea evolved after early customer discovery revealed that the original concept had too much competition, so the team narrowed its focus.
“During research, one of our teammates came across a testimonial from the mother of an autistic child,” Rhea said. “It spoke to all of us and helped us shift toward a truly underserved demographic.”
The team conducted more than 20 interviews with caregivers and special education teachers, reshaping its approach. “We realized families didn’t need another daily task,” Rhea said. “They needed personalized guidance that runs in the background. Everything we built came directly from those conversations.”
The team's biggest technical challenge was engineering a dynamic, emotionally supportive roadmap for food-exposure therapy. While AI accelerated development of SwiftUI code, all core decisions remained human-driven.
At the Capstone Expo, attendees connected strongly with the project. “So many people told us how applicable LunchBox is to their lives,” Rhea said. “Most joined the waitlist. We couldn’t be more excited for what’s next.”
Whitlow sees the pilot already fulfilling its purpose: giving students the tools and confidence to turn ideas into real ventures. Teams can continue work by applying to CREATE-X programs or building on their prototypes after the semester.
“This course shows students they can create something real,” Whitlow said. “That’s the goal: empowering them to innovate.”
Led by School of Computing Instruction faculty member and Georgia Tech alumna Jennifer Whitlow, the course gives students a founder’s perspective on building technology that meets real user needs.
Inspired by SpaceX’s Super Heavy booster, a team led by Georgia Tech’s Spencer Bryngelson and New York University’s Florian Schäfer modeled the turbulent interactions of a 33-engine rocket. Their experiment set new records, running the largest ever fluid dynamics simulation by a factor of 20 and the fastest by over a factor of four.
The team ran its custom software on the world’s two fastest supercomputers, as well as the eighth fastest, to construct such a massive model.
Applications from the simulation reach beyond rocket science. The same computing methods can model fluid mechanics in aerospace, medicine, energy, and other fields. At the same time, the work advances understanding of the current limits and future potential of computing.
The team finished as runners-up for the 2025 Gordon Bell Prize for its impactful, multi-domain research. Referred to as the Nobel Prize of supercomputing, the award was presented at the world’s top conference for high-performance computing (HPC) research.
“Fluid dynamics problems of this style, with shocks, turbulence, different interacting fluids, and so on, are a scientific mainstay that marshals our largest supercomputers,” said Bryngelson, an assistant professor with the School of Computational Science and Engineering (CSE).
“Larger and faster simulations that enable solutions to long-standing scientific problems, like the rocket propulsion problem, are always needed. With our work, perhaps we took a big dent out of that issue.”
The Super Heavy booster reflects the space industry’s move toward reusable multi-engine first-stage rockets that are easier to transport and more economical overall.
However, this shift creates research and testing challenges for new designs.
Each of Super Heavy’s 33 thrusters expels propellant at ten times the speed of sound. As individual engines reach extreme temperatures, pressures, and densities, their combined interactions with the airframe make such violent physics even more unpredictable.
Frequent physical experiments would be expensive and risky, so scientists rely on computer models to supplement the engineering process.
Bryngelson’s flagship Multicomponent Flow Code (MFC) software anchored the experiment. MFC is an open-source computer program that simulates fluid dynamic models. Bryngelson’s lab has been modifying MFC since 2022 to run on more powerful computers and solve larger problems.
In computing terms, this MFC-enhanced model simulated fluid flow resolution at 200 trillion grid points and one quadrillion degrees of freedom. These metrics exceeded previous record-setting benchmarks that tallied 10 trillion and 30 trillion grid points.
This means MFC simulations provide greater detail and capture smaller-scale features than previous approaches. The rocket simulation also ran four times faster and achieved 5.7 times the energy efficiency of comparable methods.
Integrating information geometric regularization (IGR) into MFC played a key role in attaining these results. This new approach improved the simulation’s computational efficiency and overcame the challenge of shock dynamics.
In fluid mechanics, shock waves occur when objects move faster than the speed of sound. Along with hampering the performance of airframes and propulsion systems, shocks have historically been difficult to simulate.
Computational scientists have used empirical models based on artificial viscosity to account for shocks. Although these approaches mimic the physical effects of shock waves at the microscopic scale, they struggle to effectively capture the large-scale features of the flow.
Information geometry uses curved spaces to study concepts of statistics and information. IGR uses these tools to modify the underlying geometry in fluid dynamics equations. When traveling in the modified geometry, fluid in the model preserves the shocks in a more natural way.
“When regularizing shocks to much larger scales relevant in these numerical simulations, conventional methods smear out important fine-scale details,” said Schäfer, an assistant professor at NYU’s Courant Institute of Mathematical Sciences.
“IGR introduces ideas from abstract math to CFD that allow creating modified paths that approach the singularity without ever reaching it. In the resulting fluid flow, shocks never become too spiky in simulations, but the fine-scale details do not smear out either.”
Simulating a model this large required the Georgia Tech researchers to run MFC on El Capitan and Frontier, the world's two fastest supercomputers.
The systems are two of four exascale machines in existence. This means they can solve at least one quintillion (“1” followed by 18 zeros) calculations per second. If a person completed a simple math calculation every second, it would take that person about 30 billion years to reach one quintillion operations.
Frontier is housed at Oak Ridge National Laboratory and debuted as the world’s first exascale supercomputer in 2022. El Capitan surpassed Frontier when Lawrence Livermore National Laboratory launched it in 2024.
To prepare MFC for performance on these machines, Bryngelson’s lab followed a methodical approach spanning years of hardware acquisition and software engineering.
In 2022, Bryngelson attained an AMD MI210 GPU accelerator. Optimizing MFC on the component played a critical step toward preparing the software for exascale machines.
AMD hardware underpins both El Capitan and Frontier. The MI300A GPU powers El Capitan while Frontier uses the MI250X GPU.
After configuring MFC on the MI210 GPU, Bryngelson’s lab ran the software on Frontier for the first time during a 2023 hackathon. This confirmed the code was ready for full-scale deployment on exascale supercomputers based on AMD hardware.
In addition to El Capitan and Frontier, the simulation ran on Alps, the world’s eight-fastest supercomputer based at the Swiss National Supercomputing Centre. It is the largest available system that features the NVIDIA GH200 Grace Hopper Superchip.
Like with AMD GPUs, Bryngelson acquired four GH200s in 2024 and began configuring MFC to the latest hardware innovation powering New Age supercomputers. Later that year, the Jülich Research Centre accepted Bryngelson’s group into an early access program to test JUPITER, a developing supercomputer based on the NVIDIA superchip.
The group earned a certificate for scaling efficiency and node performance on the way toward validating that their code worked on the GH200. The early access project proved successful for JUPITER, which launched in 2025 as Europe’s fastest supercomputer and fourth fastest in the world.
“Getting the level of hands-on experience with world-leading supercomputers and computing resources at Georgia Tech through this project has been a fantastic opportunity for a grad student,” said CSE Ph.D. student Ben Wilfong.
“To leverage these machines, I learned more advanced programming techniques that I’m glad to have in my tool belt for future projects. I also enjoyed the opportunity to work closely with and learn from industry experts from NVIDIA, AMD, and HPE/Cray.”
El Capitan, Frontier, JUPITER, and Alps maintained their rankings at the 2025 International Conference for High Performance Computing Networking, Storage and Analysis (SC25). Of note, the TOP500 announced at SC25 that JUPITER surpassed the exaflop threshold.
The SC Conference Series is one of two venues where the TOP500 announces updated supercomputer rankings every June and November. The TOP500 ranks and details the 500 most powerful supercomputers in the world.
The SC Conference Series serves as the venue where the Association for Computing Machinery (ACM) presents the Gordon Bell Prize. The annual award recognizes achievement in HPC research and application. The Tech-led team was among eight finalists for this year’s award.
Along with Bryngelson, Georgia Tech members included Ph.D. students Anand Radhakrishnan and Wilfong, postdoctoral researcher Daniel Vickers, alumnus Henry Le Berre (CS 2025), and undergraduate student Tanush Prathi.
Schäfer’s partnership with the group stems from his previous role as an assistant professor at Georgia Tech from 2021 to 2025.
Collaborators on the project included Nikolaos Tselepidis and Benedikt Dorschner from NVIDIA, Reuben Budiardja from ORNL, Brian Cornille from AMD, and Stephen Abbot from HPE. All were co-authors of the paper and named finalists for the Gordon Bell Prize.
“I’m elated that we have been nominated for such a prestigious award. It wouldn't have been possible without the combined and diligent efforts of our team,” Radhakrishnan said.
“I’m looking forward to presenting our work at SC25 and connecting with other researchers and fellow finalists while showcasing seminal work in the field of computing.”
]]>Inspired by SpaceX’s Super Heavy booster, a team led by Georgia Tech’s Spencer Bryngelson and New York University’s Florian Schäfer modeled the turbulent interactions of a 33-engine rocket. Their experiment set new records, running the largest ever fluid dynamics simulation by a factor of 20 and the fastest by a factor of over four.
To construct such a massive model, the custom software ran on the world’s two fastest supercomputers, as well as the eighth fastest.
The team finished as runners-up for the 2025 Gordon Bell Prize for its impactful, multi-domain research. Referred to as the Nobel Prize of supercomputing, the award was presented at the world’s top conference for high-performance computing (HPC) research.
]]>The surface and atmosphere of Mars have seen many changes over its 4.5-billion-year history. While the planet's current atmosphere is very thin (about 0.6% of Earth's), it was once thick enough to sustain liquid water.
According to new research published in Communications Earth & Environment, these atmospheric changes could play a key role in how we interpret sediment deposits on the planet.
“We found that the changing pressure resulting from atmospheric changes would have produced sediment-rich water flows with varying shapes over time,” says co-author and Georgia Tech Assistant Professor Frances Rivera-Hernández, adding that since Mars’ present-day atmosphere is very thin, the associated low pressures would produce behaviors not seen on Earth.
“Earth’s thicker atmosphere means that there are higher pressures on our planet, which produce very different behaviors,” she explains. “This means that Earth analogs may not be reliable for interpreting some Martian sedimentary landscapes.”
“At low present-day pressures, Mars mud would boil and levitate if the surface temperature was warm, or freeze and flow more like lava if the temperature was cold,” adds study lead Jacob Adler, who began working on the project while a postdoctoral researcher in Rivera-Hernández’s PLANETAS Lab at Georgia Tech, and continued the study in his current role as an assistant research professor in Arizona State University's School of Earth and Space Exploration.
The team also included Georgia Tech Ph.D. student and current PLANETAS Lab member Sharissa Thompson, along with researchers from the Open University and Czech Academy of Sciences.
“This study adds a critical layer of nuance to analogue research,” says Rivera-Hernández. “By comparing our lab results to real Martian landforms, we can better reconstruct Mars’ past climate — leading to increasingly successful research in the future.”
In order to recreate past conditions on the red planet, the team conducted over 70 experiments in a Mars simulation chamber, testing how flowing water-sediment mixtures would be affected by the varying pressures and temperatures throughout the planet’s history.
Thompson, who specializes in understanding these types of mixtures, played a key role in interpreting the results. “As part of my Ph.D. work at Georgia Tech, I uncover how and why flow shapes evolve as pressure changes, which helped us understand how these flows could have shifted with changing pressures on Mars over time,” she says. “I’m thrilled to have contributed to the innovative flow experiments this study conducted.”
The experiments revealed that at higher atmospheric pressures, water and mud would have similar flow physics (rheology) as on Earth, indicating that some of the oldest sedimentary features on the surface should appear similar to Earth environments. In these scenarios, surface conditions may also have been more habitable for life.
On the other hand, as Mars started to lose most of its atmosphere, the dominant physics in sediment flow experiments changed to freezing and boiling. The team found that at the lower pressures Mars has experienced after the Noachian, the rheology and deposit shapes (morphology) were not at all Earth-like.
“When we mapped out where on Mars, we would expect this different behavior, we found that this opposite behavior could happen at the same time at different locations on the planet,” Adler shares. “The small-scale climate variations across Mars’ topography are enough to see these opposing effects.”
The research suggests that studying the specific shapes of features like sediment flows, debris flows and mudflows could help scientists better estimate climate conditions. It also highlights how laboratory experiments are a critical part of planetary science activities, as they can help scientists better interpret remote sensing and modeling results.
"By finding matching morphologies of what we see on Mars and what we see in these lab experiments, we might be able to better time-stamp the paleoclimate record,” Adler explains.
"We’ve sent rover missions to Mars largely because we find compelling remote sensing evidence of deposits formed by water or mud that could indicate a habitable environment,” he adds. “We are often eager to compare what we find to Earth analogs, but these are not always suitable for comparison. This study shows there is still much we can learn about Mars by conducting experiments under Mars conditions.”
Funding: NASA
DOI: https://doi.org/10.1038/s43247-025-02879-w
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