Georgia Tech researchers applied their expertise to a national research program that will shape the future of computing. Their work may yield more energy-efficient computers and better predictions for environmental challenges like carbon storage, tsunamis, wildfires, and sustainable energy. 

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.”

A recent study by EPIcenter affiliates Brian An and John Kim and researchers at Georgia Tech, Iowa State University, and Clemson University examines how utility-level characteristics—such as ownership structure, electricity pricing, and incentive programs—shape residential electricity consumption in the Southeastern U.S. Using data from 105 electric utilities in Georgia and North Carolina, the authors analyze how governance models (investor-owned, municipal, cooperative), demographic factors, and program offerings interact to influence household energy use. 

The study finds that higher electricity rates and greater shares of college-educated residents are associated with lower household consumption, while larger homes, electric heating, and higher incomes drive usage upward. Notably, electric vehicle (EV) incentive programs correlate with increased household electricity demand—even after controlling for public charging infrastructure—suggesting these programs effectively promote EV adoption and at-home charging. In contrast, energy efficiency (EE) and renewable energy (RE) programs show no clear relationship with consumption in multivariate models. 

Read Full Story and listen to a related podcast on the EPIcenter Newspage

A recent study by EPIcenter faculty affiliates Joe F. Bozeman III (School of Civil and Environmental Engineering, Georgia Institute of Technology) and Daniel C. Matisoff (Carter School of Public Policy, Georgia Institute of Technology), along with John D. Kim (Carter School of Public Policy, Georgia Institute of Technology) and co-authors Sanya Carley, David M. Konisky, Jeremy J. Michalek, and Destenie Nock, examines U.S. household electric vehicle (EV) ownership and adoption intent beyond upfront costs, focusing on charging access, travel behavior, housing, and demographics. The study utilizes a nationally representative survey of 2,870 households to examine how these factors shape both current EV ownership rates and consumers’ intentions to purchase or lease an EV in the future.

The study finds that EV ownership remains relatively low among households with “median” characteristics — approximately 1% of household vehicles are electric — but increases substantially when households report access to community charging infrastructure. In contrast, single‑vehicle households and households located in states without Tesla dealerships exhibit significantly lower EV ownership rates. When examining adoption intent, the authors find that access to community and workplace charging, trust in the federal government, more liberal political ideology, younger age, and urban residence are consistently associated with higher stated interest in EV adoption. Notably, single‑vehicle households express significantly greater intent to adopt one in the future, despite being less likely to own an EV today. The analysis also reveals that public transit users show elevated EV adoption intent at earlier stages of consideration, suggesting potential complementarities between transit use and personal vehicle electrification.

Read Full Story and listen to a related podcast on the EPIcenter Newspage

Written by: Anne Wainscott-Sargent

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.”

Two research teams within the College of Lifetime Learning are piloting new approaches to online education that integrate artificial intelligence and immersive virtual reality with thoughtful instructional design. More than technology experiments, these projects show how the College refines learning innovations before scaling them across programs.

Research Scientists Eunhye Grace Flavin, Abeera Rehmat, and Jeonghyun (Jonna) Lee are developing an AI-assisted course titled Design of Learning Environments. The course is being piloted within the College to gather feedback and data before broader implementation.

“We want to study how AI can meaningfully support learning,” Flavin said, “and how it can deepen engagement and enhance instructional design rather than distract from it.”

Faculty and staff are contributing in two ways: some are enrolling in the course and participating in AI-supported activities and surveys, while others are reviewing instructional models and providing feedback. Insights from both groups will guide refinements before future rollout.

Meanwhile, Research Scientists Meryem Yılmaz Soylu and Jeonghyun (Jonna) Lee, along with Research Associate Eric Sembrat, are piloting an immersive VR module within the Online Master of Science in Analytics (OMSA) program. The module features case-based scenarios with a virtual agent, enabling students to practice leadership and workplace decision-making in realistic environments.

“Technical expertise alone is no longer enough. Our students need opportunities to practice leadership, navigate conflict, and communicate across stakeholders in realistic settings. Virtual reality allows us to create emotionally resonant, high-stakes scenarios in a safe environment where students can experiment, reflect, and grow,” Yılmaz Soylu said.

The VR experience uses branching 360° scenarios in which students’ communication choices and strategic decisions influence virtual stakeholders’ responses in real time. Insights from the pilot will inform refinements to strengthen usability, instructional alignment, and scalability before broader implementation.

“In many ways, we are building the future of online learning. We’re asking what works and what supports learning. It’s incredibly exciting to be part of a college that embraces this sort of thoughtful experimentation. Innovation like this can help us responsibly design courses for the individuals we serve,” Flavin said.

The VR module is being developed in collaboration with Lifetime Learning colleagues in instructional design, media production, and technology, as well as partners across Georgia Tech, including OMSA leadership and faculty collaborators.

Together, these initiatives reflect the College’s approach to innovation: integrating research, technology, and delivery to improve learning systems. By piloting and refining new models before scaling, the College strengthens its capacity to expand access while preserving quality and meaningful outcomes for learners across career stages.

While Italy’s 2026 Winter Olympics draw the world’s attention to snow and ice, Georgia Tech researchers are also confronting cold at its most extreme.

Some labs in the School of Electrical and Computer Engineering (ECE) use liquid nitrogen and liquid helium to chill cryogenic test systems to as low as 4 Kelvins (K), or -452.47 degrees Fahrenheit (F), temperatures that rival the coldest regions of deep space.

At this point, materials and electronic devices stop behaving in familiar ways, which is exactly why ECE researchers use these extreme conditions to explore and develop new semiconductor technologies.

“Electronics are very temperature dependent,” Professor John Cressler said, whose lab houses some of these cryogenic test systems. “Whether you see it or not, every electronic you buy has a tested temperature spec associated with it.”

Current commercially sold devices, including most cell phones, are made to run between 32 F and 85 F. Researchers in ECE test across a far wider range, as they develop technology with extraterrestrial and quantum computing applications in mind.

Other ECE teams work in natural extremes, carrying instruments into polar regions where cold creates challenges that no lab can fully replicate.

Just as cold pushes athletes in different ways, it guides ECE research down its own distinct paths.

Read the full story on the School of Electrical and Computer Engineering's website.

A new robot could solve one of the biggest challenges facing indoor farmers: manual pollination.

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.

Written by: Shweta Ram and Seungho Lee

What does it mean to design systems that endure even after major disruptions? This question framed the 2026 Brook Byers Institute for Sustainable Systems (BBISS) Sustainability Showcase, where conversations over two days spanned the Georgia coast, wildfire modeling, AI data centers, infrastructure, community engagement, and the joy of working for a more sustainable and resilient world. Across disciplines and scales, a unifying theme emerged: resilience is not a single solution. It is a systems-level challenge requiring integration across science and technology, policy, communities, and human experience.

From Coastlines to Communities

The showcase opened with a keynote from President Emeritus G. Wayne Clough on wildlife management and resiliency along Georgia’s coast. The conversation that followed between Clough and BBISS Executive Director Beril Toktay highlighted the interconnection between public policy, wilderness conservation, community leadership, and scientific research. The session highlighted not only the urgency of protecting fragile ecosystems, but also that resilience works best when it is community-focused and community-driven.

Subsequent panels continued this systemic perspective. Sessions on community engagement, biotechnology-derived, climate-resilient plants, the flood resilience of Georgia coastal communities, wildfire prediction and prevention, and infrastructure resilience analytics all emphasized that resilience depends on the synthesis of many disciplines.

Across sessions, researchers emphasized that infrastructure resilience must include governance frameworks informed by good science, community engagement based on trust, and sustained collaboration that seeks to constantly improve the science, policy, and stakeholder relationships. The researchers demonstrated that they understand their role to be greater than merely modeling risk, but as collaborators who translate research into practical solutions that communities can adopt, maintain, and trust.

AI Data Centers: A New Resilience Frontier

Day two shifted attention to data centers, which are emerging as a critical resilience frontier. As artificial intelligence systems scale rapidly, so does the infrastructure that powers them, as well as the growing realization that digital systems are physical systems. Conversations examined the feedback loops that play a significant role in determining environmental impacts, such as chip architecture, AI workloads, data center sustainability, appropriate AI usage, and who makes the decisions on data center infrastructure development. 

One of the most fascinating sessions came from Alexandria Smith, assistant professor in the School of Music at Georgia Tech. She presented an artistic yet algorithmic composition that sonified data from AI data centers. Through translating kilowatt-hour usage and interconnection data into immersive soundscapes, she reframed data centers not as static input-output machines, but as adaptive, living systems. Drawing inspiration from Physarum polycephalum, a slime mold without a brain or nervous system known for its innate problem-solving abilities, she invites the listener to imagine infrastructure that senses, adapts, and self-optimizes.

Campus as a Living Laboratory

In her session, Professor Jennifer Chirico, associate vice president of Sustainability, highlighted Georgia Tech’s 2024 Climate Action Plan, focusing on building energy efficiency, renewable integration, materials management, and mobility transitions. The plan frames the Georgia Tech campus as a test bed for resilience strategies — an ecosystem where research, operations, and policy intersect. Chirico highlighted several examples where the alignment between research and implementation was essential in moving projects from modeling to pilot projects to sustained institutional change.

Finding Joy in Climate Action

Rebecca Watts Hull, Matthew Realff, and Christie Stewart led an interactive discussion inspired by Ayana Elizabeth Johnson’s framework for accelerating long-term climate action. Participants were asked three simple questions: What are you good at? What work needs doing? What brings you joy? Sustainability and climate research are fields often defined by serious urgency, crisis narratives, and burnout. This session offered a personal framework for resilience where emotional sustainability, professional fulfillment, and joy matter just as much as the motivation to drive a mission ever forward.

Building a Shared Vision

The Sustainability Showcase concluded with a facilitated visioning session led by Kristin Janacek, associate director for Interdisciplinary Research Impact, and Beril Toktay. In small groups, leaders, researchers, and community members worked to define what resilience looks like for them.

After the conversations, several themes emerged:

• Resilience must move from research to practical and community-based solutions to sustained action.
• Networks create opportunity but require long-term stewardship to endure.
• Choosing the right metrics to measure resilience will galvanize efforts to strengthen it.
• Community capacity is at least as important as built infrastructure.

Over two days, it became clear that Georgia Tech is not approaching resilience as a narrow technical problem. It is approaching it as a systems challenge — one that spans coastlines, campuses, disciplines, data centers, the Appalachian Mountains, data models, the arts, and human relationships. Designing systems that endure requires more than innovation. It requires collaboration, stewardship, and a shared commitment to long-term impact. The conversations launched at this year’s BBISS Sustainability Showcase laid the foundation for continued coordination and ambitious action in the months ahead.