When people hear “nuclear waste,” they often imagine glowing green sludge leaking into the ground — a scene straight out of science fiction. The truth is far less dramatic and far more manageable. In fact, all the civilian nuclear waste produced by U.S. power plants so far could fit on a single football field stacked just 10 yards high. Managed under strict safety protocols, this byproduct of nuclear energy poses manageable risk compared to the billions of tons of greenhouse gases from fossil fuels. Today, researchers at Georgia Tech and around the world are working on safer reactor designs, advanced monitoring, and innovative recycling methods to turn nuclear waste into new opportunities — from clean energy to ultra-long-lasting batteries and even power for space missions.

Read more »

From zero to working prototype in just four months, students in the College of Computing’s new entrepreneurial Junior Design Capstone tackle real-world problems with guidance from startup mentors.

Supply chain management is poised to enter a new era. The Harvard Business Review has published a groundbreaking article co-authored by Andre Calmon, associate professor of operations management, alongside Flavio Calmon, Harvard University; Carol Long, Harvard University; and David Simchi-Levi, Massachusetts Institute of Technology. “The Age of Autonomous Supply Chains Has Arrived” explores how generative AI is transforming supply chain management from automated systems to truly autonomous operations.
 

Based on data collected at the Scheller College of Business, Calmon’s research demonstrates how AI models like Llama 4 Maverick 17B—equipped with optimized prompts, data-sharing rules, and guardrails—can outperform human teams in managing complex supply chains. Using the classic MIT Beer Distribution Game as a testbed, the authors benchmarked AI agents against more than 100 Georgia Tech students. The results were striking: AI-driven systems reduced total supply chain costs by up to 67% compared to human performance.
 

Traditional automated systems rely on rigid, human-designed rules. Calmon and his co-authors employed autonomous agents that learn, adapt, and coordinate across functions in real time. The study highlights four critical factors for success: selecting capable reasoning models, implementing guardrails to prevent costly errors, curating data through orchestration, and refining prompts for optimal performance.
 

“This breakthrough positions the Scheller College of Business as a thought leader at the intersection of AI and supply chain innovation,” said Calmon. “World-class supply chain management is becoming a plug-and-play capability. Businesses that understand how to guide generative AI agents with the right data and policies will gain a decisive competitive edge.”
 

The implications extend beyond cost savings. By delegating operational decisions to autonomous systems, human managers can focus on strategic priorities such as network design and supplier relationships. In an era of global volatility, this research emphasizes how future supply chain success depends on the strategic use of AI-driven technology.
 

Read More: Harvard Business Review 

The AI4Science Center has announced the first recipients of its semiannual seed grant competition. Supported by the Schools of Chemistry and Biochemistry, Physics, and Psychology, the seed grant aims to support the development of research projects centered on innovation and collaboration. 

“The selection committee received more than a dozen proposals that push the boundaries of AI-enabled science and encourage collaboration across units. I look forward to seeing the great science, strong results, and successful future external funding enabled by these seed grants,” says Dimitrios Psaltis, professor in the School of Physics and director of the AI4Science Center. 

Launched earlier this semester, the center promotes cross-disciplinary research on AI tools that address scientific challenges. The following three proposals were selected by the center based on their scientific goals, extent of interdisciplinary collaboration, and potential for outside funding: 

Spring 2026 AI4Science Center Seed Grant Recipients  

Graph Foundation Models for Protein Conformational Dynamics | School of Chemistry and Biochemistry 

• PIs: Professor Peter Kasson, School of Chemistry and Biochemistry; Professor JC Gumbart, School of Physics; Assistant Professor Amirali Aghazadeh, School of Electrical and Computer Engineering
• Graduate student: Jeffy Jeffy
• Team statement: “The AI4Science Center’s seed funding will allow us to complete and test a prototype of our new deep learning architecture for protein dynamics. We're super excited about the project and happy that this gives us support to pursue our new idea.”

Combinations of Verified AI and Domain Knowledge for New Insights in Theoretical Physics | School of Physics

• PIs: Assistant Professor Aishik Ghosh, School of Physics; Professor Vijay Ganesh, School of Computer Science
• Graduate student: Piyush Jha
• Team statement: “This seed funding gives us an opportunity to connect two fields in a way that could transform our approach to certain problems in theoretical physics.”

Harnessing the Manifold Geometry of Neural Representations for Robust LLM Safety | School of Psychology 

• PIs: Assistant Professor Audrey Sederberg, School of Psychology; Assistant Professor Pan Li, School of Electrical and Computer Engineering
• Graduate student: Ruixuan Deng
• Team statement: “Our project injects insights from human neuroscience directly into AI safety algorithm design, allowing us to move beyond black-box approaches toward more interpretable and principled safety mechanisms. By closing the loop, these computational models will also provide new feedback and insights for neuroscience.”

Deepakraj “Deepak” Divan

Professor Emeritus (2004-2025) 
Georgia Research Alliance Eminent Scholar 
School of Electrical and Computer Engineering 
Founder, Georgia Tech Center for Distributed Energy 

Deepakraj “Deepak” Divan is a globally recognized innovator in power electronics and grid transformation. He was awarded the IEEE Medal in Power Engineering in 2024.

He holds over 85 U.S. and international patents and has authored 400 refereed publications. His pioneering work on soft‑switching converters—integral for efficient energy storage, EV charging, and industrial controls—has spurred a global $70 billion power electronics industry.  

Divan laid the groundwork for grid‑forming inverter control, enabling high-renewables integration. He is the co-author of Energy 2040: Aligning Innovation, Economics and Decarbonization, named by Forbes as one of the “10 Essential Books and Podcasts Every Leader Needs in 2025”. 

“Being named an NAI Fellow is a tremendous honor,” said Divan. “It reflects years of effort to rethink how electricity is delivered and managed to solve real problems and to drive practical innovations that matter.” 

 As the founder of Georgia Tech’s Center for Distributed Energy, he led research that transforms electricity delivery through analytics, monitoring, and optimization.  

An entrepreneur, Divan co-founded Varentec (backed by Bill Gates and Khosla Ventures) and seeded ventures including GridBlock, Soft Switching Technologies, Innovolt, and Smart Wires—raising over $500 million. A National Academy of Engineering member and IEEE Fellow, he champions scalable energy-access solutions worldwide.

Proteins, including antibodies, hemoglobin, and insulin, power nearly every vital aspect of life. Breakthroughs in protein research are producing vaccines, resilient crops, bioenergy sources, and other innovative technologies.

Despite their importance, most of what scientists know about proteins only comes from a small sample size. This stands in the way of fully understanding how most proteins work and unlocking their full potential.

Georgia Tech’s Yunan Luo believes artificial intelligence (AI) could fill this knowledge gap. The National Science Foundation agrees. Luo is the recipient of an NSF Faculty Early Career Development (CAREER) award. 

“So much of biology depends on knowing what proteins do, but decades of research have concentrated on a relatively small set of well-studied proteins. This imbalance in scientific attention leads to a distorted view of the biological landscape that quietly shapes our data and our algorithms,” Luo said.

“My group’s goal is to build machine learning (ML) models that actively close this gap by generating trustworthy function predictions for the many proteins that remain understudied.”

[Related: Yunan Luo to use AI for Protein Design and Discovery with Support of $1.8 Million NIH Grant]

In his proposal to NSF, Luo coined this rich-get-richer effect “annotation inequality.” 

One problem of annotation inequality is that it slows progress in disease prognosis, drug discovery, and other critical biomedical areas. It is challenging to innovate the few proteins that scientists already know so much about. 

A cascading effect of annotation inequality is that it diminishes the effectiveness of studying proteins with AI.  

AI methods learn from existing experimental data. Datasets skewed toward well-known proteins propagate and become entrenched in models. Over time, this makes it harder for computers to research understudied proteins. 

“Protein annotation inequality creates an effect analogous to a vast library where 95% of patrons only read the top 5% popular books, leaving the rest of the collection to gather dust,” Luo said.

“This has resulted in knowledge disparities across proteins in current literature and databases, biasing our understanding of protein functions.”

The NSF CAREER award will fund Luo with over $770,000 for the next five years to tackle head-on the problem of protein annotation inequality.

Luo will use the grant to build an accurate, unbiased protein function prediction framework at scale. His project aims to:

• Reveal how annotation inequality affects protein function prediction systems
• Create ML techniques suited for biological data, which is often noisy, incomplete, and imbalanced  
• Integrate data and ML models into a scalable framework to accelerate discoveries involving understudied proteins

More enduring than the ML framework, Luo will leverage the NSF award to support educational and outreach programs. His goal is to groom the next generation of researchers to study other challenges in computational biology, not just the annotation inequality problem.

Luo teaches graduate and undergraduate courses focused on computational biology and ML. Problems and methods developed through the CAREER project can be used as course material in his classes.

Luo also championed collaboration with Georgia Tech’s Center for Education Integrating Science, Mathematics, and Computing (CEISMC) in his proposal. 

Through this partnership, local high school teachers and students would gain access to his data and models. This promotes deeper learning of biology and data science through hands-on experience with real-world tools.  

Luo sees reaching students and the community as a way of paying forward the support he received from Georgia Tech colleagues. 

“I am incredibly grateful for this recognition from the NSF,” said Luo, an assistant professor in the School of Computational Science and Engineering (CSE). 

“This would not have been possible without my students and collaborators, whose hard work laid the groundwork for this proposal.”

Luo praised CSE faculty members B. Aditya Prakash, Xiuwei Zhang, and Chao Zhang for their guidance. All three study machine learning and computational bioscience, two of CSE’s five core research areas. 

Luo also thanked Haesun Park for her support and recommendation for the CAREER award. Park is a Regents’ Professor and the chair of the School of CSE.

Georgia Tech proudly announces its faculty who have been named to the Clarivate Highly Cited Researchers 2025 list. This list is a global recognition of scholars with work among the top 1% most cited within their fields. This distinction demonstrates Georgia Tech’s leadership in advancing research with broad and lasting impact.

The Institute’s highly cited researchers include:

• Ian F. Akyildiz - retired professor, Electrical and Computer Engineering
• Antonio Facchetti – professor, Hightower Chair, Materials Science and Engineering
• Maohong Fan – adjunct professor, Civil and Environmental Engineering
• Konstantinos Konstantinidis – professor, Environmental Engineering
• Nian Liu – associate professor and Robert G. Miller Faculty Fellow, Chemical and Biomolecular Engineering
• Anant Madabhushi – professor, Biomedical Engineering
• H. Jerry Qi – Woodruff Professor, Mechanical Engineering
• Rampi Ramprasad – Regents’ Entrepreneur, Materials Science and Engineering
• Rodney J. Weber – professor, Earth and Atmospheric Sciences
• C.P. Wong – Charles Smithgall Institute Endowed Chair and Regents’ Professor, Materials Science and Engineering

“Our faculty’s recognition among the world’s most highly cited demonstrates Georgia Tech’s commitment to pioneering discoveries and solving complex global challenges through research,” said Tim Lieuwen, executive vice president for Research. “Congratulations to each of them on this impressive achievement.”

Clarivate’s annual list identifies researchers whose published work demonstrates exceptional influence, based on citation data from the Web of Science Core Collection over the past 11 years. These scholars have authored multiple Highly Cited Papers, which are publications consistently ranked in the top 1% by citations in their respective fields.

Spaceflight is becoming safer, more frequent, and more sustainable thanks to the largest computational fluid flow simulation ever ran on Earth.

Inspired by SpaceX’s Super Heavy booster, a team led by Georgia Tech’s Spencer Bryngelson and New York University’s Florian Schäfer modeled the turbulent interactions of a 33-engine rocket. Their experiment set new records, running the largest ever fluid dynamics simulation by a factor of 20 and the fastest by over a factor of four.

The team ran its custom software on the world’s two fastest supercomputers, as well as the eighth fastest, to construct such a massive model.

Applications from the simulation reach beyond rocket science. The same computing methods can model fluid mechanics in aerospace, medicine, energy, and other fields. At the same time, the work advances understanding of the current limits and future potential of computing. 

The team finished as runners-up for the 2025 Gordon Bell Prize for its impactful, multi-domain research. Referred to as the Nobel Prize of supercomputing, the award was presented at the world’s top conference for high-performance computing (HPC) research.

“Fluid dynamics problems of this style, with shocks, turbulence, different interacting fluids, and so on, are a scientific mainstay that marshals our largest supercomputers,” said Bryngelson, an assistant professor with the School of Computational Science and Engineering (CSE).

“Larger and faster simulations that enable solutions to long-standing scientific problems, like the rocket propulsion problem, are always needed. With our work, perhaps we took a big dent out of that issue.”

The Super Heavy booster reflects the space industry’s move toward reusable multi-engine first-stage rockets that are easier to transport and more economical overall. 

However, this shift creates research and testing challenges for new designs.

Each of Super Heavy’s 33 thrusters expels propellant at ten times the speed of sound. As individual engines reach extreme temperatures, pressures, and densities, their combined interactions with the airframe make such violent physics even more unpredictable.

Frequent physical experiments would be expensive and risky, so scientists rely on computer models to supplement the engineering process. 

Bryngelson’s flagship Multicomponent Flow Code (MFC) software anchored the experiment. MFC is an open-source computer program that simulates fluid dynamic models. Bryngelson’s lab has been modifying MFC since 2022 to run on more powerful computers and solve larger problems. 

In computing terms, this MFC-enhanced model simulated fluid flow resolution at 200 trillion grid points and one quadrillion degrees of freedom. These metrics exceeded previous record-setting benchmarks that tallied 10 trillion and 30 trillion grid points.

This means MFC simulations provide greater detail and capture smaller-scale features than previous approaches. The rocket simulation also ran four times faster and achieved 5.7 times the energy efficiency of comparable methods.   

Integrating information geometric regularization (IGR) into MFC played a key role in attaining these results. This new approach improved the simulation’s computational efficiency and overcame the challenge of shock dynamics.

In fluid mechanics, shock waves occur when objects move faster than the speed of sound. Along with hampering the performance of airframes and propulsion systems, shocks have historically been difficult to simulate.

Computational scientists have used empirical models based on artificial viscosity to account for shocks. Although these approaches mimic the physical effects of shock waves at the microscopic scale, they struggle to effectively capture the large-scale features of the flow. 

Information geometry uses curved spaces to study concepts of statistics and information. IGR uses these tools to modify the underlying geometry in fluid dynamics equations. When traveling in the modified geometry, fluid in the model preserves the shocks in a more natural way. 

“When regularizing shocks to much larger scales relevant in these numerical simulations, conventional methods smear out important fine-scale details,” said Schäfer, an assistant professor at NYU’s Courant Institute of Mathematical Sciences.

“IGR introduces ideas from abstract math to CFD that allow creating modified paths that approach the singularity without ever reaching it. In the resulting fluid flow, shocks never become too spiky in simulations, but the fine-scale details do not smear out either.” 

Simulating a model this large required the Georgia Tech researchers to run MFC on El Capitan and Frontier, the world's two fastest supercomputers. 

The systems are two of four exascale machines in existence. This means they can solve at least one quintillion (“1” followed by 18 zeros) calculations per second. If a person completed a simple math calculation every second, it would take that person about 30 billion years to reach one quintillion operations.

Frontier is housed at Oak Ridge National Laboratory and debuted as the world’s first exascale supercomputer in 2022. El Capitan surpassed Frontier when Lawrence Livermore National Laboratory launched it in 2024.

To prepare MFC for performance on these machines, Bryngelson’s lab followed a methodical approach spanning years of hardware acquisition and software engineering. 

In 2022, Bryngelson attained an AMD MI210 GPU accelerator. Optimizing MFC on the component played a critical step toward preparing the software for exascale machines.

AMD hardware underpins both El Capitan and Frontier. The MI300A GPU powers El Capitan while Frontier uses the MI250X GPU. 

After configuring MFC on the MI210 GPU, Bryngelson’s lab ran the software on Frontier for the first time during a 2023 hackathon. This confirmed the code was ready for full-scale deployment on exascale supercomputers based on AMD hardware. 

In addition to El Capitan and Frontier, the simulation ran on Alps, the world’s eight-fastest supercomputer based at the Swiss National Supercomputing Centre. It is the largest available system that features the NVIDIA GH200 Grace Hopper Superchip.

Like with AMD GPUs, Bryngelson acquired four GH200s in 2024 and began configuring MFC to the latest hardware innovation powering New Age supercomputers. Later that year, the Jülich Research Centre accepted Bryngelson’s group into an early access program to test JUPITER, a developing supercomputer based on the NVIDIA superchip.

The group earned a certificate for scaling efficiency and node performance on the way toward validating that their code worked on the GH200. The early access project proved successful for JUPITER, which launched in 2025 as Europe’s fastest supercomputer and fourth fastest in the world.

“Getting the level of hands-on experience with world-leading supercomputers and computing resources at Georgia Tech through this project has been a fantastic opportunity for a grad student,” said CSE Ph.D. student Ben Wilfong.

“To leverage these machines, I learned more advanced programming techniques that I’m glad to have in my tool belt for future projects. I also enjoyed the opportunity to work closely with and learn from industry experts from NVIDIA, AMD, and HPE/Cray.”

El Capitan, Frontier, JUPITER, and Alps maintained their rankings at the 2025 International Conference for High Performance Computing Networking, Storage and Analysis (SC25). Of note, the TOP500 announced at SC25 that JUPITER surpassed the exaflop threshold. 

The SC Conference Series is one of two venues where the TOP500 announces updated supercomputer rankings every June and November. The TOP500 ranks and details the 500 most powerful supercomputers in the world. 

The SC Conference Series serves as the venue where the Association for Computing Machinery (ACM) presents the Gordon Bell Prize. The annual award recognizes achievement in HPC research and application. The Tech-led team was among eight finalists for this year’s award.

Along with Bryngelson, Georgia Tech members included Ph.D. students Anand Radhakrishnan and Wilfong, postdoctoral researcher Daniel Vickers, alumnus Henry Le Berre (CS 2025), and undergraduate student Tanush Prathi.

Schäfer’s partnership with the group stems from his previous role as an assistant professor at Georgia Tech from 2021 to 2025. 

Collaborators on the project included Nikolaos Tselepidis and Benedikt Dorschner from NVIDIA, Reuben Budiardja from ORNL, Brian Cornille from AMD, and Stephen Abbot from HPE. All were co-authors of the paper and named finalists for the Gordon Bell Prize. 

“I’m elated that we have been nominated for such a prestigious award. It wouldn't have been possible without the combined and diligent efforts of our team,” Radhakrishnan said. 

“I’m looking forward to presenting our work at SC25 and connecting with other researchers and fellow finalists while showcasing seminal work in the field of computing.”