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

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

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

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

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

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

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

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

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

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

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

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

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

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

As fans around the world search for tickets to FIFA World Cup 2026 matches, a team of Georgia Tech students is working to make buying tickets to major events less stressful and more transparent. 

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

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

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

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

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

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

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

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

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

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

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

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

DeMillo was among 80 computer scientists worldwide selected for the Academy's founding class and was the only Georgia Tech faculty member recognized.

"I am honored to be included in the inaugural class of the ACM SIGSOFT Software Engineering Academy," said DeMillo. "This recognition reflects the work of outstanding students, collaborators, and colleagues over many years. Software engineering research has transformed the way society builds and depends upon software, and I hope our community will continue to advance the scientific foundations needed to make software more reliable, secure, and worthy of the trust people place in it."

Established by ACM SIGSOFT, the Software Engineering Academy is a standing body of honor that recognizes members of the software engineering community whose lasting technical contributions, leadership, and service have shaped the field. The Academy celebrates researchers whose cumulative work and influence have advanced software engineering as both a scientific discipline and professional practice.

"Rich's research in software testing is recognized among the most influential contributions to the field of software engineering," said SCP Interim Chair Mustaque Ahamad. "His inclusion in the inaugural ACM SIGSOFT Software Engineering Academy demonstrates the lasting impact of his research on building more reliable software systems."

The inaugural class was selected from leaders whose contributions have already been widely recognized across the software engineering community, including ACM Fellows and recipients of ACM SIGSOFT's Outstanding Research Award. Beginning next year, new Academy members will be elected annually through a community nomination process.

DeMillo served as the founding chair of Georgia Tech's School of Cybersecurity and Privacy, the first school of its kind at a top research university. He previously served as the John P. Imlay Dean of the College of Computing and directed the Georgia Tech Information Security Center.

Throughout his career, DeMillo has held leadership positions across academia, industry, and government. He served as Hewlett-Packard's first chief technology officer, led computing research at Bell Communications Research, directed the Computer Research Division at the National Science Foundation, and led the Software Test and Evaluation Project in the Office of the Secretary of Defense.

His research spans software engineering, cybersecurity, election security, and the societal impacts of digital technology. His 1979 paper, Social Processes and Proofs of Theorems and Programs, is widely regarded as a foundational work in software engineering and has been recognized as one of the 50 most influential papers in computer science.

In addition to his research, DeMillo has helped shape higher education through the creation of Georgia Tech's Center for 21st Century Universities. He is a fellow of both the Association for Computing Machinery and the American Association for the Advancement of Science.

The surrounding trees resemble those of a eucalyptus forest, which could indicate northern Portugal or the Spanish region of Galicia.

It’s the signposts on the road that give it away. They are flat and wide, which is common in Spain but not in Portugal.

Casapu, a master’s student in Georgia Tech’s School of City and Regional Planning, correctly reasons that the picture of a road he’s looking at is in Galicia.

Give Casapu a photo, and he will likely be able to tell you where it was taken. 

“I start with infrastructure clues that are specific to a country, region, state or province,” Casapu said. “They include roads or electricity poles, which often remain consistent throughout a country. Once you narrow down the country, you can use more specific factors like vegetation, specific landscapes, or architecture, because these are very nuanced. It’s a top-down approach.”

This is why Casapu is the reigning GeoGussr World Champion — and the ideal expert to test vision-language models (VLMs) on how good they are at geolocation.

GeoGuessr is a geography browser game launched in 2013 that invites players to guess the location of random Google Street View images. Casapu was already known as one of the top players in the world before he won the third annual GeoGussr World Championship in September.

At the beginning of the spring 2025 semester, School of Interactive Computing professor James Hays reached out to Casapu and invited him to collaborate on a new project. Hays was looking to create a dataset to evaluate VLMs' geolocation ability and reasoning. 

“VLMs are surprisingly good at geolocation right out of the box, even when they’re not trained to be good at it,” Hays said.

Hays and his colleagues, associate professors Alan Ritter and Wei Xu, took issue with many AI companies claiming that the VLMs they were releasing were not good at geolocation.

“When Open AI released GPT 4 Vision, there were privacy concerns about the model’s ability to geolocate someone based on photos they’ve shared on the internet,” Ritter said. “Open AI said this wasn’t a concern and claimed the model wasn’t good at geolocation beyond being able to recognize a city or famous monument. We found that wasn’t the case. These VLMs are state-of-the-art at image geolocation tasks.”

Show Your Work

Hays and Ritter enlisted a team of some of the world’s top geolocators. It consisted of Casapu, Joshua Diao, a master’s student in computer science, and Tejas Santanam, a Ph.D. student in industrial engineering. They each received 500 images to geolocate.

Team members recorded their reasons for each of their answers. The result was GeoRC, the first benchmark for VLM geolocation performance, consisting of 800 “ground truth” reasoning chains. 

Hays and Ritter gave the same images to GPT 5, Gemini, Llama, and Qwen. The highest-performing model geolocated with 90% accuracy — not far off from the team’s 96% score.

However, a major distinction showed up in the reasoning chains. While Casapu and Diao provided clear explanations for how they deduce each location, the VLMs either couldn’t provide reasoning for their guesses or were vague in their answers.

“The research community has been demanding explanations from these models,” Hays said. “For example, how do they know the location is in Italy?”

Hays has been researching this subject for almost 20 years. As a Ph.D. student at Carnegie Mellon University in 2008, he was the first researcher to take a machine learning approach to geolocation. He introduced a new algorithm that could estimate a geographic location from a single image.

“When experts have audited these reasoning chains, we’ve noted many suspicious or hallucinated attributes,” he said. “When they hallucinate a geographic property, why is it so often consistent with the correct guess?

“I believe they’re not revealing the true reasoning pathway that they used to determine the image was Italy. They’re just implicitly recognizing that it was Italy for many reasons, then hunting for evidence to support that. Some of the things they say are true and supported by the image, and some are fabrications.”

Practice Partner

Casapu said there may be only a handful of GeoGuessr players who can currently outperform some top-tier VLMs in geolocation, and it may not be long before no one can.

“I think it could be more difficult playing against these models than playing against another human because a human has the possibility of making mistakes at the top level,” Casapu said. “If a well-trained model has that level of consistency, that is far beyond a normal person, and it would be much more difficult to beat.”

He added that working with Hays and competing against a machine improved his skill level and provided valuable practice ahead of the world championship. 

“It helps to take a step back and see why you’re making the guesses that you are,” he said. “Since then, I’ve taken a more methodical approach to how I practice. Writing these things down is a great way to see what you know and see why you make the guesses that you do. It’s been a great training tool.”

Casapu will defend his title at the 2026 GeoGussr World Championship in September.

Hays, Ritter, Xu, Casapu, Diao, and Santanm are all co-authors of a paper on GeoRC along with lead author Mohit Talrej and Ph.D. students Ethan Mendes and Jim Thannikary. The paper will be presented next week at the 64th Annual Meeting of the Association for Computational Linguistics (ACL) in San Diego.

The outing, organized by Director of Research Operations Mary Helen Hayes, is one of a series of activities designed to bring the school’s staff together to foster close relationships between peers. Developing staff culture has been a priority for Hayes since she started at SCP in January 2024. 

“These types of events build trust and remove perceptions,” she said. “It brings together leadership and employees to show that we are all rowing the boat together.”

Soon after she was hired, Hayes attended a meeting to help shape SCP's culture. The fledgling school and its new staff had yet to develop a culture of their own, and Hayes saw an opportunity to draw on her experience planning staff outings to bring people together early on. 

It started with a staff retreat over lunch in 2024. Hayes built a playlist based on the staff’s favorite songs, and the group played icebreakers. From that point forward, SCP staff outings included ceramic pumpkin painting contests, holiday ornament decoration, and more. 

The school also sponsors a welcome breakfast each fall for SCP students and, more recently, hosted a food drive. Both organized by Hayes. 

“Mary Helen has the rare talent to bring process improvements to life while bringing people together, creating lasting improvements that benefit SCP, the College of Computing, and the Institute,” said School Administrative Officer Jan Morian.

While staff culture has become one of Hayes' most visible contributions, it is only part of her impact on SCP. In her two and a half years at the school, she has launched training on research proposals, travel, and expenses. She has also established regular meetings among faculty, administrative, and finance staff and created methods to expedite undergraduate hiring and research proposal intake. 

“I have been at Georgia Tech for about 15 years now, and I looked back on my experience when coming up with new processes for the school,” she said. 

Before joining SCP, Hayes was a financial manager and administrator at several Georgia Tech departments. In 2024 she was hired by SCP as the assistant director for financial operations.

Now as director of research operations, Hayes supports 26 school faculty and oversees a team of four research administrators. She oversees all financial operations for the school including proposal development, sponsor compliance, post-award administration, financial reporting, and award closeout activities.

Hayes runs a tight ship, a fact she will freely admit to. Her department is organized down to the filing system to keep everyone on the same page. Her team of four staff each bring their own strengths to the school’s finances, and Hayes has made sure they are cross trained so there is coverage if someone is out. 

“I want to make sure my team can rely on each other,” she said. “I want them to be able to ask questions and admit if they don’t know something.”

Just as dedicated to the culture of her own team as she is with the overall, Hayes encourages her colleagues to engage in career development conferences and courses. She also was vocal in the job families effort to ensure her staff receive job titles that reflect their day-to-day responsibilities. 

To develop her own career further, Hayes is currently enrolled in the MBA program at Georgia Southern University through the Tuition Assistance Program (TAP) offered to full-time employees by the University System of Georgia. She also received the Outstanding Staff Leadership Award from the College of Computing. 

Rather than waiting for challenges to arise, Hayes looks for opportunities to improve workflows before they become obstacles. She regularly gathers feedback from colleagues, refines procedures, and documents best practices to ensure knowledge is shared across the school instead of remaining with a single person.

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

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

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

Read Full Story on the ECE News Page

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The workshop is part of Georgia Tech’s Advanced Manufacturing Pathways (AMP) program, a collaboration between the Georgia Tech Manufacturing Institute (GTMI) and Georgia Tech Research Institute (GTRI), which connects rural educators with hands-on manufacturing training. This particular training was delivered through a partnership between GTMI, STEM@GTRI — GTRI’s K-12 outreach program — and the George W. Woodruff School of Mechanical Engineering, leveraging the facilities and expertise of the Montgomery Machining Mall to provide teachers with direct experience in modern manufacturing. Building on GTRI’s Rural Computer Science Initiative, the program expands access to high-skill, high-wage career pathways across rural communities. The initiative is supported through state funding.

The workshop comes at a time when demand for skilled manufacturing workers continues to grow nationwide, particularly in roles requiring precision, technical expertise, and problem-solving.

Inside the Machine Shop 

The training took place June 3 – 5 in the Montgomery Machining Mall, where staff provided access to facilities, equipment, and technical expertise that made the immersive learning experience possible.

Teachers designed and manufactured a metal meat tenderizer and a metal coaster etched with both the Georgia Tech logo and their name. For many, this was their first exposure to advanced manufacturing tools and processes, and a glimpse into high-skill, high-wage careers within reach for their students.

“Many of these teachers have never been exposed to any advanced manufacturing,” said Sean Mulvanity, a program manager for STEM@GTRI and project lead for this workshop. “By the time they walk out of here, they’ve actually created and manufactured physical items they can take back to their students.” Unlike traditional professional development, the workshop places teachers directly in the machine shop, working on heavy equipment. 

For AMP program leaders, this pilot was a way to build momentum for school districts that may add advanced manufacturing courses and to make the machine shop feel less intimidating in the process. 

“One of the biggest misconceptions about modern manufacturing is that it is inaccessible or limited to specialized factory environments,” said GTMI Deputy Director Steven Ferguson. “Today’s manufacturing combines hands-on skills, digital technologies, AI, and problem-solving in ways that are relevant to students across many career pathways. By giving teachers direct experience in the machine shop, we help them bring that excitement back to their classrooms and show students that they can design, build, and innovate in their own communities.”

From the Shop Floor to the Classroom 

One of the workshop participants is James Beveridge, who teaches computer science for grades 6-12 in the Chattahoochee County School District, a small, rural district south of Columbus. He has participated in multiple Georgia Tech-led training programs, and he runs a full computer science pathway for 450 middle and high school students. This fall will mark his third year in the Rural Computer Science Initiative and teaching computer science after two decades in industry.

Beveridge had some informal experience with tools growing up — his father taught him basic carpentry and welding — but he had never done formal machining work before the AMP workshop.

“Working with metal is different than working with wood, obviously, but it’s been really interesting to see the precision involved,” he said. “With wood, you can be off by a sixteenth of an inch, and nobody cares. When you’re machining metal parts, it has to be very, very precise. Learning to use the precision measuring tools has been eye-opening.”

For Beveridge, one of the biggest benefits of his ongoing work with Georgia Tech through the Rural Computer Science Initiative and related programs is that he never leaves empty-handed.

“Every time I come up here to learn something new, they send me home with the equipment to teach it with,” he said. “The first time, I left with a classroom set of robots so my students could learn to program. Another time, it was a more advanced humanoid robot with artificial intelligence. Now, I’m going back with new skills in machining and a physical project I can show my students.”

Another participant, Juone Brown, teaches high school computer science and AI to students at Dooly County High School in Vienna, Georgia. This is her second year in the rural computer science partnership and her fourth year teaching at Dooly. Previously, Brown was a professor for 25 years at Fort Valley State University. 

Like Beveridge, Brown has no formal machining background but said the way workshop instructors broke down each step — especially the math behind the cuts — made the work feel approachable.

“It has been fantastic and really well-paced,” she said. “We all come from different backgrounds, but the way they present the information makes it click. We know the math, but when you’re on the machine, and they show you easier ways to get the cut you need, it’s very encouraging.”

She’s already thinking about how to translate that feeling for her students, many of whom prefer building things to writing code. “I’m always telling them that skills pay the bills,” Brown said. “A lot of my students are hands-on. Now I can connect what we’re doing in class to real parts and jobs.”

Bringing Advanced Manufacturing to More Georgia Classrooms 

After the workshop, teachers are expected to integrate machining concepts into existing courses or help build new manufacturing pathways at their schools.  

AMP program leaders intentionally kept this pilot cohort small. The team plans to repeat the workshop several times over the coming year, expanding to more schools and districts across Georgia, building local champions who can help launch advanced manufacturing programs in their communities.

About the Georgia Tech Manufacturing Institute (GTMI)

The Georgia Tech Manufacturing Institute (GTMI) convenes industry leaders, government partners, and top researchers to collaborate on the grand challenges facing manufacturing today: accelerating technology development and deployment; creating, maintaining, and filling quality jobs; ensuring global competitiveness; and advancing economic and environmental stability. 
Our vision is to ensure rapid innovation that secures U.S. dominance in advanced manufacturing. Through the design and development of artificial intelligence systems, secure digital manufacturing, additive and subtractive processes, and large-scale production enterprises, GTMI stands at the forefront of manufacturing innovation — leveraging state-of-the-art facilities, including the Advanced Manufacturing Pilot Facility, to turn research breakthroughs into market-ready solutions. 

About the Georgia Tech Research Institute (GTRI)

The Georgia Tech Research Institute (GTRI) is the nonprofit, applied research division of the Georgia Institute of Technology (Georgia Tech). Founded in 1934 as the Engineering Experiment Station, GTRI has grown to more than 3,000 employees, supporting eight laboratories across more than 20 locations nationwide and performing more than $919 million in problem-solving research annually for government and industry. GTRI's renowned researchers combine science, engineering, economics, policy, and technical expertise to solve complex problems for the U.S. federal government, state, and industry.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Georgia Tech Class of 2026 celebrated one of its most significant accomplishments last week. Among those crossing the stage for the 2026 Spring Commencement is Marina Vemmou, who will be receiving her Ph.D. in computer science.   

The Board of Regents of the University System of Georgia awarded the program the 2026 Teaching Excellence Award for Department or Program.

MSHCI program director Dick Henneman and assistant director Carrie Bruce received the award on May 12 during a Board of Regents (BOR) meeting.

Henneman has served as director of the program since 2015, and Bruce has served as assistant director since 2014. The program began in 1996 and has since expanded to be offered by four Georgia Tech schools:

• Interactive Computing
• Industrial Design
• Literature, Media, and Communications
• Psychology 

“As we put our award submission together, it was nice for us to reflect on all our hard work and to understand the impact this program has had on students,” Bruce said. “We recently surveyed alums, and so many said they were thankful for the way this program shaped their careers.”

Under the leadership of Henneman and Bruce, the program has achieved a 99% graduation rate, with about 60 graduates per year, up from about 30 since 2015. Henneman said the program has become one of the most competitive of its kind in the world, with an admission rate under 10%.

“We have some incredibly qualified students who are a part of the program,” he said. “We’ve had a number of graduates move into design management positions, and some have started their own companies.”

Henneman and Bruce said that one thing that distinguishes Tech’s MSHCI program is its close partnerships and alignment with industry. The program has an industry advisory board that keeps students informed about the skills companies value.

“We adapted our core classes quite a bit to ensure that they weren’t just getting the academic version of HCI methods,” Bruce said. “Our program is practical and focuses on what they are going to do when they get into industry.”

Though the program continues to grow, Henneman says it has maintained a sense of community among students, which he says is another thing that sets it apart. Many alumni keep in touch and return to offer industry advice, critique resumes, and conduct mock interviews with current students.

“A lot of times graduate school can be all about the individual,” he said. “As we prepare students to go work in industry, it’s all about collaboration and the people you’re working with and learning how to work on teams.”

Georgia Tech had 21 faculty and researchers recognized in the 2026 Regents Awards. From the College of Computing, Santosh Vempala was named a Regents’ Professor, while Srinivas Aluru and Ellen Zegura had their Regents’ titles renewed.

“From my seat, I can feel the energy. I see the momentum. We're all about growth and change,” said Vivek Sarkar, dean and John P. Imlay Jr. Chair of the College of Computing. 

“But those are just words. What's really behind the words is all the hard work put in by all of you.Today's celebration is for the entire College, all of you, and all your hard work.”

Students, faculty, and staff from the School of Computational Science and Engineering (CSE) were among those recognized at the celebration. Their accomplishments reflected a year dedicated to excellence in research, teaching, and service. School of CSE award recipients included:

• Grace Kim, M.S. computer science (CS) student: Donald V. Jackson Fellowship
• Sri Ranganathan Palaniappan, M.S. CS student: Donald V. Jackson Fellowship
• Ethan Yang, M.S. CSE student: Marshall D. Williamson Fellowship
• Alumnus Austin Wright (Ph.D. ML-CSE 2025): Outstanding Doctoral Dissertation Award
• Huili Huang, Ph.D. CSE student: Outstanding Graduate Research Assistant Award
• Arlene Washington-Capers, school administrative officer: 25 Years of Service Acknowledgment

Two lecturers in the School of Computing Instruction with ties to the School of CSE received awards at the celebration.

Max Mahdi Roozbahani received a Dean’s Award, which went to instructors who taught class sizes over 350 students this year. A Class of 2019 CSE alumnus, Roozbahani teaches CSE 6242: Data and Visual Analytics.

Nimisha Roy received the Monica Sweat Outstanding Lecturer in External Engagement Award. She earned her Ph.D. in CSE in 2021. 

Professor Polo Chau advises Kim, Palaniappan, and Wright, and recommended them for their awards. 

Chau is an associate director of Georgia Tech’s M.S. Analytics program, which won the UPS George D. Smith Prize at the Institute for Operations Research and the Management Sciences (INFORMS). The award recognizes excellence in preparing students to become practitioners of operations research and analytics.

For Kim, the Jackson Fellowship was the latest achievement in a year decorated with accolades. She was one of two School of CSE students to receive the National Science Foundation (NSF) Graduate Research Fellowship Program award (GRFP). Kim was also selected for a Fulbright U.S. Student Program Research award.

Ph.D. student Abir Haque was CSE’s second NSF GRFP awardee, receiving the grant to advance research in scientific computing. Advised by School of CSE Professor and Associate Chair Edmond Chow, Haque additionally received a Department of Energy (DOE) Computational Science Graduate Fellowship.

Chow was appointed to several leadership roles this year in the Society for Industrial and Applied Mathematics (SIAM). The organization selected Chow as vice president for programs. SIAM also named him as co-chair of next year’s Conference on Computational Science and Engineering. 

NSF presented the CAREER award to two CSE faculty. Assistant Professor Yunan Luo received a grant to build artificial intelligence models to study understudied proteins in biology. 

Elizabeth Qian is an assistant professor in the Daniel Guggenheim School of Aerospace Engineering with a joint appointment in the School of CSE. Her NSF CAREER award will support research developing machine learning methods that learn from multi-fidelity data.

Researchers from the School of CSE were finalists for the 2025 Gordon Bell Prize. Assistant Professor Spencer Bryngelson led a team that included Ph.D. students Ben Wilfong and Anand Radhakrishnan, Research Staff member Dan Vickers, and alumnus Henry Le Berre (CS 2025). 

The team achieved the largest computational fluid dynamics (CFD) simulation to date, exceeding the current record by a factor of 20. The group simulated interacting plumes of 33 rocket thrusters inspired by the SpaceX Super Heavy booster.

Bryngelson advises Melody Lee, an undergraduate student who was one of three Georgia Tech students to receive a Barry Goldwater Scholarship this year. She received the award to continue research at the intersection of quantum computing and CFD.

Assistant Professor Qi Tang received the DOE Early Career Research Award. He is the first-ever faculty member from CSE and the College of Computing to receive the award. 

The $875,000 award will support Tang for five years as he researches particle data processing and compression, with applications in fusion, accelerator, and nuclear physics.

Tang was also selected as a Summer Early Career Scholar of Digital Futures at KTH Royal Institute of Technology in Sweden.

Bryngelson and Tang were selected as collaborators for three DOE Predictive Science Academic Alliance Program (PSAAP IV) Centers. The program leverages the academic community to advance science-based modeling and simulation. 

One of Tang’s students, Alex de Magalhaes, received a SPARK Award scholarship from the Georgia Tech Strategic Energy Institute. The award recognizes outstanding student engagement in energy research.

The National Institutes of Health (NIH) awarded prestigious R01 grants to three CSE faculty, each valued at $1.2 million. 

Assistant Professor Anqi Wu is using the grant to study multi-animal social behavior using advanced representation learning and reinforcement learning. 

NIH awarded a grant to Assistant Professor Kai Wang and Professor B. Aditya Prakash to build an AI framework to efficiently treat patients diagnosed with diabetes and other chronic diseases.

Prakash advises M.S. student Sudarshan Anand, who claimed two awards at the 2025 International Conference on Biomedical and Health Informatics. First, Anand was the champion of the conference’s data challenge competition. Then, the conference selected him as a Young Professional NextGen Scholar. 

Ph.D. student Yiqiao (Ahren) Jin was selected as a 2026 MLCommons ML and Systems Rising Star. He was one of 39 total awardees and participated at the 2026 ML and Systems Rising Stars workshop is hosted by AMD.

Assistant Professor Victor Fung won a 2025 Google Scholar Program award. He received the award in the Applied Science category for multi-modal scientific agents for in silico materials discovery and inverse design. The Research Scholar Program provided up to $60,000 to early-career professors to support advancement of their research.

This year, the College of Computing selected School of CSE Professor Rich Vuduc as director of Georgia Tech’s Center for Scientific Software Engineering (CSSE). The center was formed in 2022 from an $11 million investment from Schmidt Sciences. Georgia Tech was one of four universities that Schmidt Sciences selected to host a center. 

CSSE develops custom software tools and best practices to meet scientists' needs. Overall, this approach accelerates the pace and quality of scientific discovery.

Vuduc advised alumnus Elizabeth Hong (CS 2025), who received a Fulbright U.S.-Korea Presidential STEM Initiative Award. Designed to promote academic and cultural exchange, the award provided graduating college seniors and graduate students funding to pursue independent research projects in Korea on STEM topics of their choice.

Vuduc advises Ph.D. student Max Hawkins, who was selected for the OMSCS Pre-Doctoral Fellowship program. 

The program provided Hawkins support to design and teach a one-credit, pass/fail/audit seminar course. Hawkins taught Computing at Scale: The Design, Operation, and Societal Impacts of Data Centers in Fall 2025 and a research course in Spring 2026.

Hawkins also received a $3,000 scholarship through the 7X24 Exchange Atlanta Scholarship Program. He was one of three scholarship recipients awarded to students in the greater Atlanta area with research interests in the data center industry.

Vuduc advises Team Phoenix, Georgia Tech’s student cluster competition team. Team Phoenix placed first among USA participants and sixth internationally at SC25’s IndySCC competition (30 total teams, 12 USA and 18 international). The team was graded on optimizing techniques and running industry standard benchmarks on supercomputers. 

The team included computer science undergraduate students Alexander Ichtovkin, Alex Kim, Aiden Lambert, Sahil Samar, Seth Yiming Shi, and Venkata Sai Aditya Reddy Devarapalli. Graduate students Charles Lindsey and Jay Saraha mentored the team coached by Research Scientists Jeff Valdez, Aaron Jezghani, and Will Powell.

Alumni Ziyi (Francis) Yin (Ph.D. CSE-CSE 2024), Rafael Orozco (Ph.D. CSE-CSE 2024), Mathias Louboutin (Ph.D. CS-CSE 2020), and Professor Felix Herrmann received an honorable mention for the Best Paper of 2024 from the journal Geophysics. The award, presented in 2025, recognized the group’s work on WISE: a full-waveform variational inference via subsurface extensions.

Georgia Tech approved both of CSE’s promotion cases this year. Elizabeth Cherry will be promoted to full professor. Srijan Kumar will be promoted to associate professor with tenure. 

That sense of purpose shaped Spring 2026 Commencement, where School of Computational Science and Engineering (CSE) graduates celebrated years of research, collaboration, and discovery.

“Today, we celebrate you, your accomplishments, and your potential,” said Georgia Tech President Ángel Cabrera in his commencement address to Ph.D. graduates.

“I thank you for choosing a career of science and research when some question science and research. We need you to continue on this path. The world needs you.”

In addition to administering its flagship CSE Ph.D. and M.S. programs, the School of CSE offers doctoral degrees in computer science and machine learning. Ph.D. students who received their diplomas and doctoral hoods on May 7 at McCamish Pavilion were:

• Muhammed Balin (Ph.D. CS-CSE 2026), advised by School of CSE Professor Ümit Çatalyürek
• Andrew Hornback (Ph.D. CS-CSE 2026), co-advised by School of CSE Assistant Professor Yunan Luo and Wallace H. Coulter Department of Biomedical Engineering Professor May Wang
• Chengrui Li (Ph.D. CSE-CSE 2026), advised by School of CSE Assistant Professor Anqi Wu
• Xinhai Pan (Ph.D. CSE-CSE 2026), advised by School of CSE Assistant Professor Anqi Wu
• Kaan Sancak (Ph.D. CSE-CSE 2026), advised by School of CSE Professor Ümit Çatalyürek
• Agam Shah (Ph.D. ML-CSE 2026), co-advised by Scheller College of Business Professor Sudheer Chava and School of CSE Associate Professor Chao Zhang
• Kartik Sharma (Ph.D. CS-CSE 2026), advised by School of CSE Assistant Professor Srijan Kumar

This semester, 14 CSE doctoral students completed M.S. degrees and will continue their studies at Georgia Tech. They are: 

• Bin Bai (M.S. EAS-CSE), advised by School of Earth and Atmospheric Sciences Assistant Professor Pengfei Liu
• Afrouz Delshad (M.S. CSE-CSE 2026), advised by College of Computing Associate Dean for Graduate Education and School of CSE Associate Professor Elizabeth Cherry
• Roy Makkar Gabriel (M.S. ECE-CSE 2026), advised by School of Electrical and Computer Engineering Professor Ali Adibi
• Alina Maximovna Gorbunova (M.S. ISYE-CSE 2026), co-advised by H. Milton Stewart School of Industrial and Systems Engineering Professors Kamran Paynabar and Jianjun Shi
• Wenbo Hao (M.S. MATH-CSE 2026), advised by School of CSE Assistant Professor Peng Chen
• Xueyu Hu (M.S. MSE-CSE 2026), advised by School of Materials Science and Engineering Regents’ Professor Meilin Liu
• Dayoung Kang (M.S. AE-CSE 2026), advised by Daniel Guggenheim School of Aerospace Engineering and School of CSE joint Assistant Professor Elizabeth Qian
• Soohwan Kim (M.S. ME-CSE 2026), advised by George W. Woodruff School of Mechanical Engineering Professor David Hu
• Xuanang Li (M.S. MATH-CSE 2026), advised by School of Mathematics Assistant Professor Tom Kelly
• Nitya Maruthuvakudi Venkatram (M.S. AE-CSE 2026), advised by School of AE Regents’ Professor Dimitri Mavris
• Nilavrah Sensarma (M.S. BIO-CSE 2026), advised by School of Biological Sciences Professor John McDonald
• Benjamin Wilfong (M.S. CSE-CSE 2026), advised by School of CSE Assistant Professor Spencer Bryngelson
• Xiaofeng (Alex) Wu (M.S. CEE-CSE 2026), advised by School of Civil and Environmental Engineering Regents’ Entrepreneur David Frost
• Keyang (Alfred) Zhong (M.S. ISYE-CSE 2026), advised by School of ISyE Professor Chelsea White

Georgia Tech’s CSE graduate program includes 12 schools and departments participating as home units. These home units represent the colleges of Computing, Engineering, and Sciences. This approach facilitates an immersive, interdisciplinary experience in which students study computational methods within their respective domains.

Georgia Tech celebrated master’s graduates at a May 9 ceremony at Bobby Dodd Stadium. After the Institute celebration, graduates were recognized during ceremonies held by their respective colleges.

Mohammed Wazir Adain (M.S. CSE 2026)

Aditi Agarwal (M.S. CSE 2026)

Sudarshan Anand (M.S. CSE 2026)

Arjun Bansal (M.S. CSE 2026)

Shraddha Bharadwaj (M.S. CSE 2026)

Aarushi Biswas (M.S. CSE 2026)

Anurita Bose (M.S. CSE 2026)

Hao-Cheng Chang (M.S. CSE 2026)

Nai-Jen Cheng (M.S. CSE 2026)

Yida Cheng (M.S. CSE 2026)

Denys Chernenko (M.S. CSE 2026)

Aarushi Vishal Dhanuka (M.S. CSE 2026)

MacKenzie Taylor Starr Drury (M.S. CSE 2026)

Chandra Sekhar Reddy Edula (M.S. CSE 2026)

Shiqi Fan (M.S. CSE 2026)

Yuanting Fan (M.S. CSE 2026)

Wen (Ava) Feng (M.S. CSE 2026)

Amrutha Praveen Ganapathiyat Othayoth (M.S. CSE 2026)

Aman Garg (M.S. CSE 2026)

Xin Guan (M.S. CSE 2026)

Yunmei Guan (M.S. CSE 2026)

Srihas Gunda (M.S. CSE 2026)

Yihui Han (M.S. CSE 2026)

Keenan Wai-sean Hom (M.S. CSE 2026)

Shiqi Hu (M.S. CSE 2026)

Wenxin Jiang (M.S. CSE 2026)

Harneet Singh Khanuja (M.S. CSE 2026)

Hyunjeong Kim (M.S. CSE 2026)

Vijay Prabhas Kodamalla (M.S. CSE 2026)

Neel Kothari (M.S. CSE 2026)

Ziji Li (M.S. CSE 2026)

Qinye Liu (M.S. CSE 2026)

Ruixin Liu (M.S. CSE 2026)

Yibo Liu (M.S. CSE 2026)

Shenyifan Lu (M.S. CSE 2026)

Wenliya Lyu (M.S. CSE 2026)

Aditya Akash Mavle (M.S. CSE 2026)

Samuel Wesley Moss (M.S. CSE 2026)

Shruti Santosh Murarka (M.S. CSE 2026)

Karthic Palaniappan (M.S. CSE 2026)

Shrey P. Patel (M.S. CSE 2026)

Tanish R. Patwa (M.S. CSE 2026)

Tim Minh Phan (M.S. CSE 2026)

Jing Qi (M.S. CSE 2026)

Wanrong Qi (M.S. CSE 2026)

Hui Qiao (M.S. CSE 2026)

Aditya Raghavan (M.S. CSE 2026)

Araceli Rodriguez Vallejo (M.S. CSE 2026)

Chloé Saleh (M.S. CSE 2026)

Vanshika Shah (M.S. CSE 2026)

Kaichen Shen (M.S. CSE 2026)

Bohan Shu (M.S. CSE 2026)

Kunhao Song (M.S. CSE 2026)

Ajeet Karthik Subramanian (M.S. CSE 2026)

Jingyun Sun (M.S. CSE 2026)

Yupeng Tang (M.S. CSE 2026)

Michael Kenneth Thompson (M.S. CSE 2026)

Yu Chu Tsai (M.S. CSE 2026)

Viren Dipin Varma (M.S. CSE 2026)

Aarushi Chetan Wagh (M.S. CSE 2026)

Yiling Wu (M.S. CSE 2026)

Yitong Wu (M.S. CSE 2026)

Jiayi Xu (M.S. CSE 2026)

Shuyan Yang (M.S. CSE 2026)

Yiming Ye (M.S. CSE 2026)

Zhenghao You (M.S. CSE 2026)

Yijia Zeng (M.S. CSE 2026)

Jinkai Zhan (M.S. CSE 2026)

Yuehan Zhang (M.S. CSE 2026)

Xinyu Zhao (M.S. CSE 2026)

Yuqian Zheng (M.S. CSE 2026)

Alexander Zhou de Magalhaes (M.S. CSE 2026)

Shizhuo Zhu (M.S. CSE 2026)

Xiaoai Zhu (M.S. CSE 2026)

Xinjie Zhu (M.S. CSE 2026)

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

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

Graduate: Chengrui Li

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

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

Faculty Advisor: School of CSE Assistant Professor Anqi Wu

What persuaded you to attend graduate school at Georgia Tech?

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

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

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

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

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

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

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

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

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

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

What advice would you give someone interested in graduate school?

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

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

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

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

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

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

What is your favorite Georgia Tech memory?

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

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

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

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

Graduate: Agam Shah

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

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

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

What persuaded you to attend graduate school at Georgia Tech?

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

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

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

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

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

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

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

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

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

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

What advice would you give someone interested in graduate school?

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

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

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

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

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

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

What is your favorite Georgia Tech memory?

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

That plan lasted about three years.

“I initially thought I’d just continue in research without a Ph.D.,” Bhaskar said. “My advisor encouraged me to stay, but I wanted to experience industry first.”

In addition to perspective, working outside academia offered Bhaskar clarity. Over time, he found himself drawn back to the kind of deeper, more impactful research he had glimpsed during his graduate studies. Watching colleagues with doctoral degrees tackle complex problems reinforced his decision.

“I realized I missed research,” he said. “And seeing the kind of work Ph.D. graduates were doing motivated me to apply.”

That decision led him to Georgia Tech, where the strength of its cybersecurity and network security research stood out. The program’s interdisciplinary approach was a major draw.

“Some schools are strong in one area,” he said. “Here, there’s collaboration across domains. That was important to me.”

When Bhaskar arrived, the School of Cybersecurity and Privacy had not yet fully taken shape. Research groups, including the Institute for Information Security and Privacy, were already active, but the school's formal structure emerged during his second year.

Working with his advisor, Paul Pearce, Bhaskar shifted his research focus from binary and static analysis to network security, a transition that required both adjustment and curiosity.

“I wanted to move into something with more direct real-world impact,” he said.

This led him to work on censorship measurement, internet security and privacy, areas where technical findings can have global implications. With guidance from Pearce and support from other faculty working in similar areas, he quickly found his footing.

His favorite research project however, was in fact his first. 

In earlier research, unexplained anomalies had appeared in measurement data. At first, the assumption was that they stemmed from geolocation issues. However, after digging deeper, Bhaskar uncovered a different cause: routing changes.

“That discovery showed that routing can significantly affect measurement results,” he said.

What began as a narrow investigation turned into a broader insight. One that reshaped how those measurements could be interpreted.

Along the way, other milestones followed, including his first paper accepted to a top conference.

“That was a big moment,” Bhaskar said.

But beyond publications and research breakthroughs, it’s the day-to-day experience of doctoral life that made the strongest impression.

“The camaraderie stands out the most,” he said. “Especially during deadlines. Everyone is working hard, but you still take time to step away, talk, and support each other.”

That sense of community extended beyond his own lab. Interactions with neighboring groups and researchers in fields like cryptography broadened his perspective and shaped his approach to problems.

“The way different groups think about problems is really valuable,” he said.

Outside the lab, Atlanta played its own role in the experience. From sporting events to concerts, the city offered a balance to the intensity of research.

“There’s a lot to do,” Bhaskar said. “I tried to take advantage of that.”

Now, as he prepares to graduate, the next chapter is already in motion. Bhaskar will move to the West Coast for a post-doctorate security research role at Stanford University, following a summer internship that offers time to reset before the transition.

“Abhi's work is excellent,” said Pearce. “During the course of his Ph.D. he discovered important underlying phenomena that influence how we measure and understand internet censorship and end-to-end network behaviors broadly.” 

“He's an outstanding researcher and community leader, and while his contribution as a student to my group and SCP will be missed, we're excited about what he accomplished and his next steps!”

Mizan Rahman knows there’s much that academia and industry can learn from each other.

He’s living proof of it.

The 52-year-old entrepreneur will receive his Ph.D. in human-centered computing (HCC) as he walks across the stage on Thursday at Georgia Tech’s Spring 2026 Ph.D. Commencement.

When Rahman was accepted into the HCC Ph.D. program, he’d already founded three successful tech startups and was an angel investor in numerous others. He also earned a master’s in computational science and engineering from Georgia Tech in 2013.

Rahman took on the challenge of a Ph.D. because he’s always been in pursuit of a holistic view of technology. One perspective he said he needed to understand was that of the end user.

“I’d already done computer science and computational science and engineering, so I wanted to look at the human dimension, the user’s perspectives, and society,” Rahman said. “You’ve got to build technology that fits into our human dynamics.”

Rahman’s journey began as an undergraduate in chemical engineering at Miami Dade College and Florida Atlantic University. He switched to computer science after his roommate, also a CS major, showed him some programming he had been working on.

“I couldn’t sleep after that,” Rahman said. “I was writing software all night. I loved solving problems through technology.”

Early Success

Rahman invented BayBuilder, a strategic sourcing automation technology, in 1999. The software was adopted by major Fortune 500 companies. Rahman estimates it has saved these companies $1 billion in procurement spending.

Baybuilder was acquired by a NASDAQ-listed firm in 2001, and he was ready to start his next company.

“I’ve been an entrepreneur as far back as I can remember,” Rahman said. “I was born with it. If I saw something that didn’t exist, I created it.”

After relocating to Atlanta, Rahman founded a new company, M2SYS Technology. Governments around the world used the company’s innovative identity technology to automate processes and deliver efficient services to citizens. M2SYS also worked with the CDC to treat HIV in Haiti and Zambia, as well as many U.S. hospitals, including Grady Memorial in Atlanta, to protect patients from fraud and receiving the wrong treatment.

Rahman’s most recent startup, CloudApper AI, introduced a new system architecture that generates secure software requiring minimal ongoing maintenance. His non-biased algorithm, which he created during his Ph.D. for CloudApper, is now used by major companies to streamline automated resume analysis and candidate scoring.

Living in Two Worlds

Rahman began his Ph.D. in 2021, but he kept his new venture to himself and his family. He didn’t tell his employees he was pursuing a Ph.D., and he didn’t disclose his industry background to his fellow doctoral students.

“I kept the other side of me far away,” he said. “The people who knew, they knew, but I purposefully didn’t discuss my outside activities and experience. I wanted to fit in, and I think I was able to do that.”

When Rahman was at his company, he was a CEO and entrepreneur, and when he was at Georgia Tech, he was a researcher. But what he was learning as a researcher began to change how he perceived his business. 

“I wanted to be a researcher and think like a researcher and not just always think about sales and marketing,” he said. “I started bringing in more ideas about how the user should be thought of in our products. I’m sure they were wondering why I was emphasizing that so much, but it was because I was applying what I was learning in my Ph.D. 

“Now I’ve been on both sides, I want to be connected to both in the future, applying research principles and practices in product development and innovation.”

Building Community Through Makerspaces

When it came time for Rahman to choose a subject for his dissertation, he returned to his roots and looked for ways technology can support young entrepreneurs and their startups. That’s when he began conducting research in makerspaces.

“I wanted to find out how we can bring innovation to a scale where anybody can participate,” he said. “I saw this happening in makerspaces where regular people learn, collaborate, and build products and companies from scratch. I saw that the community at large is facing a sustainability crisis.”

Rahman argued in his dissertation that makerspaces can play a significant role in local innovation. When people struggle to survive, it disrupts communities in numerous ways.

Rahman details four studies conducted over three-and-a-half years that show how socio-technical factors drive organizational sustainability in makerspaces and how AI tools can foster an innovative culture within them.

“The compelling thing about his research is that he shows that people come to makerspaces for the tools, but they stay for the people,” said Rosa Arriaga, associate professor and Rahman’s advisor.

“He has plenty of work from his ethnographic research that shows that a makerspace can have all the tech and resources, but if there isn’t cohesion among the people, there’s a problem.”

It Takes a Village

Rahman is the first to admit that it’s not possible for one man to run a company while pursuing a Ph.D. He needed a community. This starts with his family. His wife, Mohu Sultana, now serves as interim CEO of M2SYS and has supported Rahman throughout his Ph.D. research.

The Georgia Tech community has been part of Rahman’s life in some way since he started his career. 

Sultana holds a bachelor’s degree in computer science from Tech, and their daughter, Malisha Rahman, is graduating this week with a bachelor’s in economics and international affairs. Malisha Rahman has also been accepted into the HCC program and will begin her Ph.D. in the fall. 

Rahman said that any student who wants to create a tech startup will have an advantage from access to Georgia Tech’s network.

“The Georgia Tech startup community is fantastic,” he said. “There is a tremendous amount of knowledge here, and the research community can help shape the next big thing. We have CREATE-X, a place where you can find mentorship from faculty who started in industry. You’ll learn things I wish I knew before I started.”

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

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

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

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

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

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

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

Professor Named to Sustainability Cohort

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

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

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

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

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

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

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

Jill Watson Creator Receives AAAI Lecture Award

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

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

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

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

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

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

“I am pleased to be able to recognize all of this hard work,” said Dean Vivek Sarkar during the ceremony.

One student, two staff members, and four faculty members were nominated by their SCP peers and received awards for their achievements over the past year. 

Student Solves Real World Problems

Yibin Yang (Ph.D. CS 2025) was awarded a 2025 Dissertation Award for his thesis on zero-knowledge proofs in real-world problems. SCP Professor and Senior Associate Chair Vlad Kolesnikov advised Yang and acknowledged that Yang’s work advances the field of cryptography. 

Yang contributed to the advancement of zero-knowledge proofs and multi-party computations, while also building toolchains that are faster and more usable than existing systems. His work earned a distinguished paper award at the 2023 ACM CCS, and he also served as an RSAC Security Scholar.

Staff Lead the Way

In the staff category, Mary Helen Hayes was awarded the Outstanding Staff Leadership Award, and Gina Anderson received the Ruthie Book Outstanding Staff Team Member Award.

The Outstanding Staff Leadership Award is given to a full-time administrative staff member in recognition of an outstanding record of leadership that has resulted in a significant positive impact on the College of Computing, the Institute, or the computing community. Hayes was nominated by four faculty and staff members for this award for her steady presence in SCP since she began her role as director of research operations in 2024. 

The Ruthie Book Outstanding Staff Team Member Award is presented to a staff member in recognition of their outstanding performance in honor of Ruthie Book, who exemplified excellence in her work. Anderson was nominated by SCP faculty and staff for her outstanding leadership and mentorship as assistant director of business operations.

Both received praise for their hard work from the college as well as from their supervisor, Senior Academic Officer Jan Morian. 

“I am so incredibly proud of our staff in the School of Cybersecurity and Privacy who won awards this year at the College of Computing Annual Awards ceremony,” she said.

“Mary Helen Hayes and Regina Anderson are truly outstanding staff members who exemplify Georgia Tech’s values. Their leadership has contributed substantially to the success of the school.”

Cybersecurity Faculty Net Four Awards

The College of Computing also recognized four SCP faculty members for excellence in teaching and research during the college’s annual award ceremony. 

Assistant Professor Teodora Baluta received the Junior Faculty Teaching Award for developing a new graduate-level course that brought together generative artificial intelligence (AI) security, adversarial machine learning, cryptography, and differential privacy. Her nominator, SCP Associate Professor Vassilis Zikas, said the course bridged a critical gap in a rapidly evolving area of computing. 

For his role in leading Team Atlanta to victory in the DARPA AI Cyber Challenge, Professor Taesoo Kim received the Outstanding Senior Faculty Research Award. His nominator, Regents Professor Wenke Lee, praised the team’s performance, which not only won the competition but also beat the combined score of all other competitors. The AI developed by Team Atlanta is now open sourced with the Open Source Security Foundation. 

Associate Professor Frank Li received the Junior Faculty Research Award for establishing world-class research BEES Lab at Georgia Tech. One of his nominators, Associate Professor Saman Zonouz, put Li’s name forward for his work empirically evaluating and improving internet security and privacy from an operational standpoint. 

Finally, Associate Professor Brendan Saltaformaggio received the Mid-Career Faculty Research Award. Zikas nominated him for establishing internationally recognized research in cybersecurity forensics, malware analysis, AI security, and software supply chain security. Saltaformaggio’s research highlights include the discovery of over $2 billion in stolen funds on the Ethereum blockchain. 

"We know SCP faculty conduct highly impactful research that is of the highest quality,” said SCP Interim Chair Mustaque Ahamad. “Our faculty receiving research awards at all levels recognizes this and shows how we are working to realize SCP’s vision of creating security for everyone and everything."

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

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

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

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

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

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

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

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

Good Explanations

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

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

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

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

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

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

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

Knowing the Context

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

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

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

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

Not Just Checking Boxes

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

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

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

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

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

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

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

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

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

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

[Related: GT @ CHI 2026]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Now, the team is working with the Linux Foundation and the Open Source Security Foundation (OpenSSF) to ensure that its breakthrough doesn’t remain confined to a competition environment. The team’s new initiative, OSS-CRS, aims to standardize and operationalize cyber reasoning systems (CRSs) for real-world use.

“The AI Cyber Challenge pushed the boundaries of autonomous software security, with seven teams developing systems capable of finding and remediating vulnerabilities at scale,” said Andrew Chin, a Georgia Tech Ph.D. student and lead on the OSS-CRS program. 

“However, after the competition’s conclusion, it has been difficult to apply these advancements to the open-source community due to infrastructure incompatibilities and the lack of long-term maintenance for the open-sourced CRS implementations.”

To address this gap, Georgia Tech’s Systems Software Lab (SSLab), directed by Professor Taesoo Kim, is leading the development of OSS-CRS, which provides both a common framework for CRS development and the infrastructure needed to deploy these systems seamlessly across open-source projects.

As part of this effort, the team has ported its competition-winning system, Atlantis, into the OSS-CRS framework. The move makes it compatible with laptops and other everyday machines with flexible resource and budget configurations.

Interoperability is also central to the framework’s design. Atlantis can be combined with other CRSs to improve performance, including systems developed by fellow AIxCC finalists and newer agentic, command-line-based tools. This modular approach reflects a key lesson the team learned from the competition: collaboration between systems can outperform any single solution.

OSS-CRS has been accepted as a sandbox project within OpenSSF’s AI/ML Security Working Group, a milestone that brings added technical guidance and community support to the project. This includes:

• Access to mentorship
• Dedicated working group meetings
• Broader visibility through industry events, publications, and outreach efforts

The collaboration will also foster stronger connections with open-source maintainers, helping streamline vulnerability disclosure and remediation workflows.

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.”

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.”

The programming style relies on using generative artificial intelligence (AI) to create software code using tools like Claude, Gemini, and GitHub Copilot. According to graduate research assistant Hanqing Zhao of the Systems Software & Security Lab (SSLab), no one had been tracking these common vulnerabilities and exposures before the launch of their Vibe Security Radar.

“The vulnerabilities we found lead to breaches,” he said. “Everyone is using these tools now. We need a feedback loop to identify which tools, which patterns, and which workflows create the most risk.”

The radar extensively scans public vulnerability databases, finds the error for each vulnerability, and then examines the code’s history to find who introduced the bug. If they discover an AI tool's signature, the radar flags it. 

Of the 74 confirmed cases uncovered so far by the tool, 14 are critical risks, and 25 are high. These vulnerabilities include command injection, authentication bypass, and server-side request forgery. Zhao explained that since AI models tend to repeat the same mistakes, an attacker would need to find these bugs just once. 

“Millions of developers using the same models means the same bugs showing up across different projects,” he said. “Find one pattern in one AI codebase, you can scan for it across thousands of repositories.”

Despite its success, the team has only scratched the surface of the problem. The radar can trace metadata like co-author tags, bot emails, and other known tool signatures, but it can't identify an issue if these markers have been removed. 

The next step is behavioral detection. AI-written code has patterns in how it names variables, structures functions, and handles errors. 

“We're building models that can identify AI code from the code itself, no metadata needed,” said Zhao. “That opens up a lot of cases we currently can't touch.”

The team is also improving its verification pipeline and expanding its sources to include more vulnerability databases. The goal is to get a more complete picture of AI-introduced vulnerabilities across open source, not just the ones that happen to leave signatures behind. 

As more programmers rely on vibe coding, Zhao warns that it still needs to be reviewed as thoroughly as any other project. 

“The whole point of vibe coding is not reading it afterward, I know,” he said. “But if you're shipping AI output to production, review it the way you'd review a junior developer's pull request. Especially anything around input handling and authentication.”

When prompting AI, SSLab also recommends providing more detailed instructions to get it closer to production-ready. There are also tools to check the code for vulnerabilities after  code it has been generated. Not double-checking could lead to a catastrophe. 

“The attack surface keeps growing,” said Zhao. “More people running AI agents locally means the attacker doesn't need to break into the company infrastructure. They just need one vulnerability in a model context protocol server that someone installed and never reviewed.”

One reason the attack surfaces are expanding rapidly is AI’s evolution. In the second half of 2025, the Vibe Security Radar found about 18 cases across seven months. Then, in the first three months of 2026, it identified 56. March 2026 alone had 35, more than all of 2025 combined. 

Many tools, like Claude, are now more autonomous, allowing developers to write entire features, create files, and even make architecture decisions. 

“When an agent builds something without authentication, that's not a typo,” said Zhao. “It's a design flaw baked in from the start. Claude Code and Copilot together account for most of what we detect, but that's partly because they leave the clearest signatures.”

The approaches of over a dozen quantum computing companies are now being evaluated through the Quantum Benchmarking Initiative (QBI), a project of the U.S. Defense Advanced Research Projects Agency (DARPA). According to the agency, QBI “aims to rigorously verify and validate whether any quantum computing approach can achieve utility-scale operation – meaning its computational value exceeds its cost – by the year 2033.”
 

Supporting the effort, a 40-person interdisciplinary research team from the Georgia Tech Research Institute (GTRI) has joined the test and evaluation component of QBI, providing unbiased subject-matter experts to work with 13 other research organizations in evaluating the R&D plans of participating quantum computer companies. Through this collaboration, the GTRI team is working with more than 400 other third-party experts on the project.
 

Read the complete article on the GTRI news site

TokenSmith is a citation-supported large language model (LLM) tutor that can be hosted locally on a user’s personal computer. The tutor only provides answers based on course materials, such as the textbook or lecture slides.  

Associate Professor Joy Arulraj began the project with support from the Bill Kent Family Foundation AI in Higher Education Faculty Fellowship last year. The fellowship, led by Georgia Tech’s Center for 21st Century Universities, supports faculty projects exploring innovative and ethical uses of AI in teaching.   

Arulraj has enlisted assistant professors Kexin Rong and Steve Mussmann to help build TokenSmith.  

Mussmann said TokenSmith is a synergistic blend of a database system and a machine learning system. The model stores textbooks, textbook annotations by course staff, common questions and answers, a learning state of the student, and student feedback in a structured database system. However, machine learning plays a key role in the answer generation as well as adapting the system to the student, course staff guidance, and user feedback.

"What excites me most is demonstrating how data-driven ML and principled database systems design can reinforce each other — one providing adaptability and flexibility, the other providing structure and traceability — in a way that benefits students," Mussmann said.

Keeping the model local has been an important focus of the project. The team wanted to create an AI tutor that helps students learn from their class resources rather than just giving answers. With each response, TokenSmith cites the origin of the answer in the provided documents.  

“One problem with LLMs is that they can hallucinate and provide wrong answers, but in this controlled environment, we can add these guardrails to make sure it’s actually helpful in an educational setting,” Rong said.  

Rong said she feels that students often undervalue textbooks, and she hopes TokenSmith can motivate students to make better use of them.  

“Textbooks can sometimes be daunting, but maybe if we combine them with the model, students might be more willing to read a paragraph or page in the textbook, and that could help clarify something for them,” she said.  

Running the model locally is more cost-effective and helps preserve the user’s privacy. But running the new tool locally comes with technical challenges.  

One challenge with creating the model is speed. Since it is a locally based model, TokenSmith depends solely on the user’s computer memory.  Tests have also shown that the tutor currently struggles to answer more complex questions. 

“We are interested in pushing the boundaries of these local models so that they give students good answers and also run fast enough to keep students engaged,” Arulraj said.  

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

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

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

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

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

Masking Symptoms

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

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

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

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

Feeling Isolated

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

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

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

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

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

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

Raising Awareness

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

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

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

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

Design Solutions

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

It can start with the husbands. 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Georgia Tech students who presented posters included:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

1. Make Cybersecurity Training Actually Maritime

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

Researchers recommend:

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

2. Move Beyond “Call IT”

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

Researchers recommend:

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

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

3. Create Unified, Ship-Specific Regulations

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

Key recommendations include:

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

4. Invest in Maritime-Specific Cyber Research

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

Priority areas include:

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

The Bottom Line

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

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

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

Li, an accomplished computer security and privacy researcher, joined Georgia Tech in 2020 as the Institute was launching the School of Cybersecurity and Privacy (SCP). He holds a joint appointment with the School of Electrical and Computer Engineering (ECE). 

“While tenure may be an individual's milestone, in reality, it reflects the help, support, and hard work of countless others,” Li said.

He credits his accomplishments to the ongoing mentorship and support he has received from faculty and staff at SCP, ECE, and Georgia Tech.

“I'm also extremely thankful to work with such amazing students at Georgia Tech, especially the Ph.D. students in my research lab, and the BS and MS students in my classes, who help our research efforts. Georgia Tech has been an amazing place to start my faculty career,” said Li.

Li advises five Ph.D. students at his Better Empirically Established Security (BEES) lab in SCP. They take a data-driven approach to understanding how security and privacy concerns manifest in practice, and use the insights gained to drive improvements in real-world security.

Their research examines how users, security operators, and attackers behave in various security and privacy-sensitive situations, often using internet-wide measurements, network traffic analysis, user studies and experiments, and large-scale data mining.

“The tenure and promotion to associate professor rank is in recognition of the outstanding research program Frank has developed at SCP,” said Mustaque Ahamad, interim chair and Regents’ Entrepreneur.

“He is an award-winning educator. We look forward to his continued leadership in the important areas of usable security and network security in the future.”

Li was among nine College of Computing faculty members who received promotion and tenure this year.

John P. Imlay Jr. Dean of Computing Vivek Sarkar emailed the College community with the good news.

“We are truly thrilled to celebrate this moment with you, as we recognize your contributions to our students and to the advancement of our College and Institute in so many ways,” he said.

In 2025, Li received the prestigious CAREER Award from the National Science Foundation (NSF). His CAREER project will investigate real-world uses of FIDO2/passkeys and address security and usability issues that can arise. A goal of his research is to identify and resolve problems before they become widespread and more difficult to solve.

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

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

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

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

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

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

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

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

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

Knowing the Pose

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

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

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

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

Computer Vision Breakthrough

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

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

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

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

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

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

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

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

Measuring Success

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

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

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

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

Georgia Stoica is one of 38 Ph.D. students worldwide researching machine learning who were named a 2025 Google Ph.D. Fellow.

Stoica is designing AI training methods that bypass fine-tuning, which is the process of adapting a large pre-trained model to perform new tasks. Fine-tuning is one of the most common ways engineers update large-language models like ChatGPT, Gemini, and Claude to add new capabilities. 

If an AI company wants to give a model a new capability, it could create a new model from scratch for that specific purpose. However, if the model already has relevant training and knowledge of the new task, fine-tuning is cheaper.

Stoica argues that fine-tuning still uses large amounts of data, and that other methods can help models learn more effectively and efficiently.

“Full fine-tuning yields strong performance, but it can be costly, and it risks catastrophic forgetting,” Stoica said. “My research asks if we can extend a model’s capabilities by imbuing it with the expertise of others, without fine-tuning?

“Reducing cost and improving efficiency is more important than ever. We have so many publicly available models that have been trained to solve a variety of tasks. It’s redundant to train a new model from scratch. It’s much more efficient to leverage the information that already exists to get a model up to speed.”

Stoica said the solution is a cost-effective method called model merging. This method combines two or more AI models into a single model, improving performance without fine-tuning.

On a basic level, Stoica said an example would be combining a model that is efficient at classifying cats with one that works well at dogs.

“Merging is cheap because you just take the parameters, the weights of your existing models, and combine them,” he said. “You could take the average of the weights to create a new model, but that sometimes doesn’t work. My work has aimed to rearrange the weights so they can communicate easily with each other.”

Through his Google fellowship, Stoica seeks to apply model merging to create a cutting-edge vision encoder. A vision encoder converts image or video data into numerical representations that computers can understand. This enables tasks such as image or facial recognition and generative image captioning.

“I want to be at the frontier of the field, and Google is clearly part of that,” Stoica said. “The vision encoder is very large-scale, and Google has the infrastructure to accommodate it.”

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Itamar Kimchi, assistant professor, School of Physics

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

Wenjing Lao, associate professor, School of Mathematics

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

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

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

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

Sonification is the process of translating data points into sound.

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

The four habitats are:

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

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

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

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

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.  

Advancing Robotics Research 

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.” 

Researching Inclusion in Computing Education 

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

As artificial intelligence (AI) drives explosive growth in data centers, communities across the U.S. are facing rising electricity costs, new industrial development, and mounting strain on an aging power grid.

At Georgia Tech, several faculty members are approaching these sustainability challenges from different but complementary angles: examining how data center policy affects local communities, modeling how AI-driven demand reshapes regional energy systems, and building tools that help the public understand the tradeoffs embedded in grid planning. Together, their work highlights how better data, thoughtful policy, and public engagement can guide more resilient and equitable decisions in an AI-powered future.

AI’s Hidden Footprint: How Data Centers Reshape Communities

Ahmed Saeed studies the infrastructure most people never see. An assistant professor in the School of Computer Science and a Brook Byers Institute for Sustainable Systems (BBISS) Faculty Fellow, Saeed focuses on how data centers — the backbone of modern AI — are built, operated, and regulated, and what their growth means for host communities.

“Data centers are the infrastructure for our digital life, so more of them are necessary to keep doing what we’re doing,” he said.

Data center energy consumption could double or triple by 2028, accounting for up to 12% of U.S. electricity use, according to a report by Lawrence Berkeley National Laboratory. U.S. spending on data center construction jumped nearly 70% between May 2023 and May 2024, according to the American Edge Project.

Georgia is an AI data center hub, ranked fourth globally, with $4.6 billion in AI-related venture capital invested across 368 deals, the American Edge Project reported. At a recent town hall in DeKalb County, Georgia, Saeed helped residents connect AI’s promise to its local consequences. Training large AI models can require tens of thousands of graphics processing units (GPUs) running for days or weeks, driving an unprecedented wave of data center construction. AI-focused chips, he noted, can consume 10 to 14 times more power than traditional processors.

That demand often shows up as pressure on local infrastructure. Communities are increasingly concerned about electricity and water use, grid upgrades, and who ultimately pays. In Virginia, Saeed pointed to a legal dispute in which consumer advocates warned that data centers could raise electricity bills by 5% in the short term and up to 50% over time, while utilities argued those investments were inevitable and could benefit customers in the long run.

Environmental concerns add another layer. Saeed cited controversies over water use and backup diesel generators in states, including Georgia and Tennessee, alongside a recent Environmental Protection Agency (EPA) ruling that tightened generator regulations. While diesel generators are clearly harmful, he cautioned that long-term, rigorous evidence linking data centers to regional health impacts remains limited.

Saeed’s research aims to reduce those impacts directly. By optimizing how workloads are scheduled across large server fleets, his team has demonstrated power savings of 4 – 12%, a meaningful gain if U.S. data centers approach projected levels of up to 12% of national electricity use by 2028.

For Saeed, data centers are akin to highways: essential to modern life, disruptive to nearby communities, and shaped by policy choices. The question, he argues, is not whether AI infrastructure should exist, but how transparently and fairly it is built.

Economist Probes the Energy Costs of the AI Boom

While headlines often frame AI as an energy crisis, Georgia Tech environmental and energy economist and BBISS Faculty Fellow Tony Harding is focused on measuring its real — and uneven — impacts. Harding, an assistant professor in the Jimmy and Rosalynn Carter School of Public Policy, uses economic modeling to examine how AI adoption affects energy use, emissions, and local communities.

In recent work published in Environmental Research Letters, Harding and his co-author analyzed how productivity gains from AI could influence national energy demand. Their findings suggest that, at a macro level, AI-related activity may increase annual U.S. energy use by about 0.03% and CO₂ emissions by roughly 0.02%.

“Those numbers are small in the context of the overall economy,” Harding said. “But the impacts are highly uneven.”

That unevenness is evident in where data centers are built. While Northern Virginia remains the country’s top data center hub, with 343 operational data centers, states like Georgia, which currently has 94 operational data centers, are rapidly attracting facilities due to reliable power and favorable tax policies. 

Harding’s latest research focuses on local effects, asking why data centers cluster in urban areas, how they influence housing markets, what happens to electricity prices, and whether they exacerbate water stress. Early evidence suggests large facilities can increase local electricity rates, contributing to public backlash and regulatory response. In Georgia, the Public Service Commission has begun requiring new, high power draw customers (like data centers) to cover more of the costs associated with grid expansion.

Harding’s goal is to give policymakers better evidence to design incentives and guardrails. “To manage these technologies responsibly,” he said, “we need a clear picture of their intended and unintended consequences.”

Gamifying a Strained and Aging Power Grid

Daniel Molzahn is tackling another side of the problem: how to modernize an aging power grid under growing demand. Electricity demand is expected to rise about 25% by 2030, driven by data centers, electric vehicles, and broadscale electrification. At the same time, much of the U.S. electricity grid is nearing the end of its lifespan, with many transformers being decades old.

To make these challenges tangible, Molzahn, an associate professor in the School of Electrical and Computer Engineering, developed a browser-based game with a group of students through Georgia Tech’s Vertically Integrated Projects program called Current Crisis. Players take on the role of a utility decision-maker, balancing reliability, wildfire risk, renewable integration, and affordability.

The game grew out of Molzahn’s National Science Foundation CAREER award and reflects his belief that complex systems are best understood experientially. Its initial focus is wildfire resilience, modeling how grid infrastructure can both spark and suffer damage from fires.

But resilience comes at a cost. Burying power lines, for example, reduces wildfire risk but dramatically increases expenses. Players must confront the same tradeoffs utilities face: improve reliability or keep rates low.

Molzahn hopes the game will help students and the public grapple with the realities of planning future power systems. “These choices aren’t abstract,” he said. “They shape affordability, resilience, and our path toward a cleaner grid.”

The project now involves nearly 40 students from across campus, supported by Sustainability NEXT funding and a collaboration with Jessica Roberts, former BBISS Faculty Fellow and director of the Technology-Integrated Learning Environments (TILES) Lab in the School of Interactive Computing.

“As a learning scientist, I look at how to engage people with science and scientific data and get people having conversations they might not otherwise have,” says Roberts, who hopes the seed grant helps the team determine first that they are going in the right direction and, second, how to broaden the impact.

One student, Stella Quinto Lima, a graduate research assistant in Human-Centered Computing, has made the game the focus of her doctoral thesis. Through the game, she wants players to notice their misconceptions about the power grid, energy use, and AI, and to use critical thinking to identify, question, and possibly undo those misconceptions.

 “I hope that we can really engage adults and help them see it’s not black and white. The game is not only about power grids, but how AI affects the grid, how it affects our lives, and how it will impact our future.”

The team plans to expand the game’s features, use it in outreach programs, and analyze player decisions as a source of data to study energy-system decision-making.

“We want to change the conversation about power and power grid stability, reliability, and sustainability, Roberts said, “and find a way to get this message to a larger public.”

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.”

“It was the era of look left and look right, and one of you will not be here at graduation,” Craig said.

Craig worked hard and graduated with his computer science (CS) bachelor’s degree in 2000, just as the dot-com bubble burst. He returned to Georgia Tech about a year later and has been here ever since.

Craig is the enterprise cybersecurity architect in the Office of Information Technology and has spent nearly three decades at Tech as a student, employee, and instructor.

Along the way, he has earned three degrees from the Institute and helped shape how Georgia Tech approaches cybersecurity in an increasingly complex digital landscape.

Craig began his career at Tech supporting student registration and other core IT systems. He moved fully into cybersecurity about 15 years ago. His technical background was strong, but he saw a gap in his experience.

“I had a lot of technical background and work experience, but not much policy experience,” he said.

Craig enrolled in Georgia Tech’s Master of Science in Information Security to fill in this gap. He said his decision to enroll in the policy track was intentional.

“If you’ve been doing the technical work for 10 years, a technical master’s helps some,” Craig said. “But it is much more useful to study the areas you do not already know well.”

Craig moved into management as his GT career progressed. This path led him once again to the classroom. This time, he pursued an MBA from Georgia Tech’s Scheller College of Business.

Craig believes the combination of cybersecurity and business education is increasingly important for leaders and others.

“There is a big gap in the industry,” he said. “You need people who understand cybersecurity and the business side, and people in business leadership who understand cybersecurity risk.”

Craig is an instructor in the online Master of Science in Cybersecurity program. He teaches incident response and often sees this gap among his students.

“Many business professionals do not know how to respond to a cybersecurity incident,” Craig said. “They are not trained in it. At the same time, many cybersecurity professionals are learning business impacts on the job.”

Craig said business knowledge is essential for aspiring chief information security officers.

“At that level, understanding how cybersecurity supports business goals is more important than deep technical detail,” he said. “You still need the basics, but you also need to talk to the CFO.”

At Georgia Tech, Craig focuses on cybersecurity architecture. His work centers on the design and protection of enterprise systems.

“For example, student information systems have a design,” he said. “We look at how firewalls and other controls fit into that design to protect the data.”

His role continues to evolve as the Institute’s cybersecurity needs change. That evolution mirrors the field itself, especially with the rise of artificial intelligence (AI).

“AI has impacted cybersecurity for longer than people want to admit,” Craig said. “Understanding what is unusual is a big part of security, and AI can be very good at that. It can also be very good at avoiding detection.”

Craig said AI introduces new architectural risks, particularly around data privacy. Tools that analyze student or employee data must be carefully designed to prevent sensitive information from leaking through training or outputs.

“You have to understand the inputs and outputs,” he said. “Otherwise, you can accidentally release data you really care about.”

Privacy has been a recurring theme throughout Craig’s career. He credits courses such as the privacy policy class taught by Professor Peter Swire, the J.Z. Liang Chair in the School of Cybersecurity and Privacy, with shaping his thinking.

“So much of security is about personal data,” Craig said. “Understanding what actually makes data anonymous or not is critical.”

Craig believes that privacy protection depends on training and system design within an institution as large and decentralized as Georgia Tech.

“Training can only get you so far,” Craig said. “People make mistakes. Strong processes limit exposure even when human error happens.”

Looking back, Craig describes his time at Georgia Tech as one of constant growth.

“The industry has massively changed,” he said. “What you learn becomes outdated quickly. You have to keep growing.”

From undergraduate student to cybersecurity leader, Craig’s career reflects both the evolution of Georgia Tech and the fast-changing world of cybersecurity. For him, the learning never stops.

To address that challenge, researchers at the Georgia Tech Research Institute (GTRI) have developed an improved cryogenic vacuum chamber that helps reduce some common noise sources by isolating ions from vibrations and shielding them from magnetic field fluctuations. The new chamber also incorporates an improved imaging system and a radio frequency (RF) coil that can be used to drive ion transitions from within the chamber. 
 

“There’s a lot of excitement around quantum computing today, and trapped ions are just one of the research platforms available, each with their own benefits and drawbacks,” explained Darian Hartsell, a GTRI research scientist who leads the project. “We are trying to mitigate multiple sources of noise in this chamber and make other improvements with one robust new design.”
 

The chamber design is described in a paper published January 20, 2026 in the journal Applied Physics Letters. Some of the technical improvements developed for the project are already being applied at GTRI and collaborating organizations. This work was done in collaboration with Los Alamos National Laboratory.
 

The goal of the vibration isolation is to reduce the laser amplitude and phase noise when addressing the ions, increasing operation fidelity. The goal of the magnetic field noise reduction is to preserve the coherence of qubits for longer periods of time so researchers can use them for more complex algorithms.

See the complete article on the GTRI news site