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 two‑day event took place Feb. 4 – 5, coinciding with the Critical Minerals Ministerial hosted by U.S. Secretary of State Marco Rubio in Washington, D.C., on Feb. 4, which brought together more than 50 nations to strengthen and diversify global critical mineral supply chains. During this ministerial, U.K. Minister Seema Malhotra and U.S. Under Secretary of State Jacob Helberg signed a Critical Minerals Memorandum of Understanding, strengthening bilateral cooperation between the United States and the United Kingdom on critical mineral supply chains. 

These broad efforts are supported by White House Executive Order 14363, which defines the Genesis Mission and aims to accelerate scientific discovery through AI. The order identifies critical minerals supply chain resilience as a national security imperative.

In Atlanta, these themes were brought to life in real time. The GEMs-4 workshop brought together researchers, policymakers, national labs, industry leaders, and workforce organizations from both the U.S. and the U.K. to address shared challenges in technology translation, permitting, investment, and talent development. 

The state of Georgia’s integrated ecosystem, linking research universities, legacy industries, technical colleges, national labs, and public‑private partnerships, served as a case study. Presenters highlighted how existing industrial assets in the Southeast are being incorporated into emerging clean energy and critical minerals supply chains, offering a model for other regions seeking to build capabilities around extraction, processing, and manufacturing.

A U.K. member of Parliament representing Cornwall, where the U.K. has lithium reserves and deep critical mineral expertise, joined the convening, as well as representatives from the U.K. Critical Mineral Association, Camborne School of Mines, and the University of Kent. Together, they explored opportunities and challenges, from a fundamental science to a commercialization perspective grounded in real-world experience. 

The alignment between the ministerial in Washington and the expertise present in Atlanta demonstrated the value of state-level engagement and how national agreements translate into practical collaboration on the ground. 

“The Southeast has the research depth, industrial footprint, and collaborative spirit needed to lead in critical minerals innovation,” said Yuanzhi Tang, Georgia Power Professor in the School of Earth and Atmospheric Sciences, executive director of the Strategic Energy Institute, and founding director of the Center for Critical Mineral Solutions at Georgia Tech. “GEMs‑4 showed what’s possible when universities, industry, and government partners align around shared priorities.” 

Day one featured strategic dialogue on critical mineral resources, innovation pathways, and partnership models. A recurring theme was the co-production of critical minerals alongside major mineral commodities. “Many critical minerals are produced as byproducts of larger mining operations, making it essential to integrate recovery strategies into existing mineral industries rather than developing entirely new extraction systems,” noted Crawford Elliott, professor of geosciences at Georgia State University.

Day two transitioned to field‑based learning, led by Paul Schroeder, professor of geology at the University of Georgia. Participants visited active operations to better understand how regional industrial strengths can support national and international supply chain goals. Schroeder said, “Connecting people to the long-standing mineral extraction economy at the mining and plant sites, where the work gets done with an amazingly skilled workforce, underscores the unique role of Georgia’s place‑based capacity in advancing national and transatlantic supply chain goals.”

Organizers emphasized that resilient supply chains rely on regional capabilities built over time through university collaboration, industry partnerships, and community engagement. With three years of inter‑university coordination now underpinning the GEMS platform, the 2026 workshop demonstrated how the Southeast is contributing actionable models for U.S.-U.K. cooperation.

“Ecosystem-building at this scale requires participation from every part of the value chain, and we are encouraged by the model GEMs presents,” said Rachel Galloway, Consul General at British Consulate General Atlanta. “The collaboration across universities, industry, and government is exactly what enables long‑term impact on both sides of the Atlantic.”

Through focused dialogue and partnership-building, the symposium strengthened transatlantic collaboration, highlighted regional strengths, and accelerated innovation and translation across the critical minerals value chain, from resource characterization and processing to recycling, manufacturing, and deployment.

For more information about the GEMS initiative, visit: https://gems.research.gatech.edu/.

“The effects move slowly and appear in places people do not connect to energy,” said Tibor Besedes, professor in the School of Economics. “Oil and natural gas are part of the cost structure for an enormous range of goods.”

About 20% of global oil and liquefied natural gas flows through the waterway linking the Persian Gulf to world markets. When that flow is constrained, the impact ripples outward across industries most people never associate with an energy crisis.

“In complex supply chains, a disruption in one critical link, even if only briefly, can cascade through the system, well beyond the initial event,” says Pinar Keskinocak, chair and professor in the H. Milton Stewart School of Industrial and Systems Engineering. “As delays persist and compound, interconnected systems often take a long time to recover, rebalance, and return to normal.”

Price Pressures That Arrive Quietly

Early effects are already visible. 

Jet fuel availability is tightening, and diesel prices are rising across Asia. China has ordered refineries to stop exporting fuel, creating shortages that are increasing shipping costs for U.S. imports, from consumer electronics to pharmaceuticals.

The strait is also a key corridor for naphtha, a feedstock used to produce plastics, packaging, solvents, textiles, and pharmaceutical components. Roughly 85% of Middle Eastern polyethylene exports move through the strait. 

“Consumers won't see the effect of this quickly,” Besedes says, “but the longer the strait is closed, the higher the cost will be of all of these products naphtha is used for.”

Aluminum is equally exposed. 

“Smelters require sustained, low-cost energy,” said Chris Gaffney, a professor of the practice in the Stewart School. “The Middle East accounted for roughly 21% of U.S. unwrought aluminum imports in 2025. When energy prices spike or supply is constrained, capacity is reduced or shut down, and those decisions are difficult and slow to reverse.”

Fertilizer is one of the clearest examples of delayed inflation. Natural gas is essential for its production, and Persian Gulf states account for one-third of global urea exports and half of global sulfur exports. Urea prices at the New Orleans import hub have already climbed sharply.

“We won't see the effects quickly, but rather in six to 12 months, depending on the crop and its cycle,” Besedes says. “Without or with less fertilizer, crop yields will decrease, resulting in higher prices.”

Why Hormuz Is Different From Other Chokepoints

On top of all those factors, the strait closure presents a uniquely dangerous vulnerability. 

“Unlike a port strike or canal blockage, there is no meaningful way to reroute volume,” says Gaffney. “If it is disrupted, flow is constrained rather than redirected.” Pipeline alternatives replace only a fraction of the 20 million barrels per day that normally transit the strait.

“Choke point vulnerability arises when a large portion of flow depends on a route that is hard to substitute,” said Mathieu Dahan, associate professor in the Stewart School. “Hormuz has no scalable alternatives with sufficient capacity.” 

Alan Erera, senior associate chair in the Stewart School expanded on Dahan’s point, noting that strait disruptions raise costs across manufacturing and distribution.

“Ships are rerouted onto longer paths, which drives up fuel and labor costs, ties up vessels and containers for longer periods, and ultimately raises inventory costs for shippers because capital is locked up while goods are still in transit,” Erera said.

When Geopolitics Meets Global Supply Chains

Additionally, the strait closure raises the risk of wartime miscalculation. 

“We haven’t seen a disruption on this scale since the tanker wars of the late 1980s,” said Larry Rubin, associate professor in the Sam Nunn School of International Affairs. Gulf states' dependence on the strait constrains both regional actors and U.S. strategy, raising risks around crisis decision-making.

Rubin also points to a dimension most coverage has missed entirely. “One thing that has been overlooked by many commentators is the fact that the Iranian people have probably been hit the hardest economically,” he says. “They were already in a challenging situation. The Iranian economy won't recover quickly after the war.”

Resilience Has a Short Memory

Meanwhile, for the United States, “The Strategic Petroleum Reserve provides a buffer, and domestic energy production has improved resilience,” says Gaffney. “But the gap remains between enabling capacity and sustaining resilience. Policy can support infrastructure, but it cannot ensure private sector participants invest in resilience when cost pressures rise.”

For policymakers and industry leaders, the disruption reinforces a familiar pattern. "The supply chain remains optimized for efficiency rather than resilience, in part due to the high investment costs required to build flexibility," says Dahan. 

Gaffney added that resilience does improve after disruption, but that “it erodes over time if not actively maintained.”

Even if the strait reopens, higher costs and slow restart timelines mean the system will not snap back. Experts suggest that when headlines have moved on from this disruption, it will still be shaping prices across the economy. 

The paper examines various strategies for enhancing the flexibility of data center energy use. One approach is to use backup power systems, such as uninterruptible power supplies, to support the grid during emergencies. Another method involves rerouting computing jobs to different data centers in other locations to balance energy demand. The authors also discuss implementing smart scheduling techniques that shift workloads to off-peak hours, reducing strain on the grid. Additionally, they highlight adjusting processor speeds by lowering CPU (central processing unit) and GPU (graphics processing unit) clock rates to limit power consumption when needed. Finally, the paper suggests pre-cooling data center equipment to limit the energy required for cooling during peak demand periods. Notably, experimental evidence shows that underclocking GPUs can cut power consumption by 40% with only a 22% performance loss, suggesting technical feasibility for demand-response interventions.

Despite these technical options, the authors find that real-world cost considerations and reliability concerns limit widespread adoption. Data center operators generally do not change their behavior in response to electricity prices, as job revenue far outweighs energy costs under normal conditions. For example, a GPU rented at $2 per hour consumes only $0.04 worth of electricity at average prices, making curtailment unattractive except during extreme price spikes. Surveys indicate that operators are reluctant to compromise reliability or deploy backup systems for ancillary services. Consequently, price-based incentives alone are unlikely to drive meaningful flexibility.

Read more on the EPIcenter Webpage
Listen to a podcast on the research here

As new data centers continue to be built and proposed in Georgia, counties and municipalities across the state are considering how to guide this growth. EPIcenter’s data center dashboard provides policymakers, planners, researchers, and community stakeholders with a centralized resource to better understand how data center regulations are being developed and applied across Georgia and the U.S.

“Our Data Center Hub provides Georgia communities with a one-stop shop to understand how their neighbors are managing land-use regulations for data centers,” said Laura Taylor, director of EPIcenter. “It brings together clear, accessible information to help jurisdictions plan when data center growth occurs in their area.”

The dashboard is organized around five thematic areas commonly addressed in data center land-use regulations: Site Planning and Building Design, Infrastructure and Utilities, Environmental and Community Protections, Public Safety and Security, and Lifecycle Governance. Within each theme, users can explore specific regulatory topics and access the relevant ordinances enacted by Georgia communities.

To build the dashboard, EPIcenter researchers conducted a comprehensive review of municipal codes across the state.

“We reviewed municipal codes for about 180 cities and counties across Georgia and identified ordinances that specifically address data center development,” said Yang You, EPIcenter’s research associate who developed the project. “In total, we found 19 data center-specific topics that ordinances tend to cover. We analyzed ordinances across jurisdictions and organized their ordinance provisions into topics such as building placement, setbacks, infrastructure, and environmental considerations to make it easier to compare how different jurisdictions regulate data centers.”

You added that the dashboard also incorporates examples from outside of Georgia. By gathering ordinances from other states and pairing them with Georgia-specific examples, EPIcenter aims to provide a clear framework to help communities efficiently address data center land-use regulation.

The Georgia Data Center Ordinance Hub is available through the Energy Policy and Innovation Center website.

Reliable energy is required to keep safe in cold winters and hot summers, making it a matter of national security. There are also vying economic policies to consider, political and financial incentives to navigate, and questions of social and economic inequality.

Experts in Georgia Tech’s Ivan Allen College of Liberal Arts examine the challenges we face with the U.S. energy transition, and work to help make it safe, fair, and effective for all.

• Challenge No. 1: Managing National Security — with Adam N. Stulberg, professor and chair of the Sam Nunn School of International Affairs.
• Challenge No. 2: Confronting Inequality — with Bijesh Mishra, a postdoctoral scholar in the Jimmy and Rosalynn Carter School of Public Policy.
• Challenge No. 3: Choosing the Right Economic Policies — with Bobby Harris, an assistant professor in the School of Economics.
• Challenge No. 4: Navigating Financial and Political Incentives — with Kate Pride Brown, a sociologist in the School of History and Sociology.

Read the article on the Ivan Allen College website.

Set in the heart of Tech Square on the Georgia Tech campus, this year’s event explores how energy systems, materials, technologies, supply chains, and policy must evolve in response to AI’s accelerating impact. As digital infrastructure expands and computation intensifies, the need for reliable, resilient, and sustainable power has never been more urgent. 

“Energy Day reflects Georgia Tech’s strength in connecting world-class research in materials and components with the infrastructure and partnerships needed to translate discovery into scalable energy technologies that serve industry, society, and the future economy,” said Eric Vogel, executive director of the IMS and the Hightower Professor in Materials Science and Engineering. 

Energy Day 2026 also marks an important milestone with the introduction of its first group of corporate sponsors: GE Vernova, Southern Company, Georgia Power, ExxonMobil, Southwire Spark, Gems Setra, and Tektronix. Their support reflects a shared commitment to advancing energy solutions. 

“Tektronix is excited to be part of Energy Day because advancing the future of energy starts with precise measurement and trusted insights,” said Christopher Bohn, president of Tektronix. “From power electronics and high voltage systems to grid scale renewables and AI driven control technologies, the breakthroughs discussed here directly align with the innovations we support through our products and solutions. Collaborating with Georgia Tech allows us to engage early with emerging research and the next generation of engineers—critical collaborators in building a cleaner, smarter, and more resilient energy ecosystem.”

The keynote address will be delivered by Vanessa Z. Chan, a nationally recognized leader at the intersection of innovation, commercialization, and emerging technologies. Chan will provide insights on accelerating technological discovery, emphasizing how AI is transforming energy and materials design. She will discuss how commercialization strategies must rapidly evolve across multidisciplinary energy domains from grid modernization to advanced batteries and clean manufacturing.

Building on the themes introduced in the keynote, the program transitions into a fireside chat with Georgia Tech EVPR Tim Lieuwen featuring Amit Kulkarni and Jim Walsh. Kulkarni is vice president of Product Management and Strategy for the Gas Power business within GE Vernova, where he oversees the world’s largest portfolio of power generation equipment. Walsh, vice president of GE Vernova’s Consulting Services, leads teams providing innovative solutions across the full spectrum of power generation, delivery, and utilization.

Next comes a policy-focused panel that will explore the surge in power demand driven by AI, how the United States is addressing today’s most urgent energy challenges, and the long-term implications of today’s decisions for a sustainable energy future. Bringing together leading voices in U.S. environmental and energy policy, the panel features Joe Aldy of Harvard University and former special assistant to the president for Energy and Environment; Al McGartland of New York University’s Institute for Policy Integrity and former Environmental Protection Agency lead economist and director of the National Center for Environmental Economics; and Kevin Rennert, fellow and director of the Comprehensive Climate Strategies Program at Resources for the Future and former staff member on the U.S. Senate Committee on Energy and Natural Resources.

The second panel focuses on critical materials — the foundation of advanced energy systems and digital technologies. As AI, data centers, and advanced energy technologies drive demand for critical materials, securing them now requires integration and coordination across the entire value chain. Panelists include Rachel Galloway, British consul general in Atlanta; Vijay Murugesan, head of Materials Intelligence and Digital Innovation at Amazon; Colin Spellmeyer, executive strategic sourcing leader at GE Vernova;  Charles Sims, Tennessee Valley Authority Distinguished Professor of Energy and Environmental Policy at the University of Tennessee; and Nortey Yeboah, principal engineer at Southern Company. Together, they will offer perspectives on the policy and economic frameworks shaping the energy supply chain, from developing raw resources to manufacturing the technologies essential to future energy systems.

In the afternoon, participants can dive deeper into specialized topics through three focused technical tracks. 

• “Meeting the Demand for Power” will examine how emerging technologies, advanced nuclear systems, and renewable integration can work together to deliver reliable, resilient electricity.
• “Data Center Infrastructure and Resources” will explore innovations in thermal management technologies, energy-efficient computing, and the broader resource impacts of expanding digital infrastructure.
• “Grid Technologies and Markets” will highlight strategies for strengthening grid capacity, incorporating demand-side management, and optimizing carbon performance as energy systems evolve.

“Meeting the rapidly rising electricity demand driven by AI requires bold ideas, coordinated action, and research that moves at the speed of innovation,” said Yuanzhi Tang, executive director of the SEI. “Energy Day 2026 brings together the people and expertise needed to shape resilient, sustainable energy systems for the future. At Georgia Tech, we see this event as a catalyst for new partnerships, new solutions, and a shared commitment to strengthening the nation’s energy foundation.”

Energy Day 2026 is designed for researchers advancing emerging energy technologies, policymakers navigating shifting regulatory and geopolitical landscapes, industry professionals seeking insight into emerging tools and supply chains, and students preparing to enter one of the most consequential sectors of the decade. It also welcomes anyone interested in AI, sustainability, electrification, and critical materials. 

Join us to explore the future of energy. To learn more and register, visit: Energy Day 2026.

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

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.”

Tang will lead the strategic vision, interdisciplinary research efforts, and internal and external partnerships at SEI, strengthening connections across Georgia Tech’s Colleges, Interdisciplinary Research Institutes (IRI), the Georgia Tech Research Institute (GTRI), and external partners to advance energy-related initiatives.

Founded in 2004, SEI is one of Georgia Tech’s IRIs and serves as a campuswide hub for energy research, education, and engagement.

Tang is the Georgia Power Professor in the School of Earth and Atmospheric Sciences. Her research and leadership focus on advancing secure, circular, and sustainable energy systems by integrating Earth, environmental, biological, materials, and sustainability sciences and innovations. She previously served as an initiative lead on critical minerals and sustainable resources at SEI as well as the associate director for interdisciplinary research at the Brook Byers Institute for Sustainable Systems.

“Professor Tang brings a strong record of research impact, leadership of complex initiatives, and a collaborative approach that will help elevate Georgia Tech’s energy research enterprise,” said Julia Kubanek, vice president for Interdisciplinary Research at Georgia Tech. “She brings deep expertise in fundamental Earth and environmental science, including water, soil, and energy research, while also leading state and regional partnerships in emerging, applied areas such as critical minerals. Most importantly, she is community-minded with excellent listening and consensus-building skills.”

As executive director, Tang will develop and communicate a unifying vision to advance interdisciplinary energy research and strategic thought leadership at Georgia Tech, integrating expertise across engineering, sciences, computing, business, design, economics, policy, and the humanities.

Tang is also the founding director of the Center for Critical Mineral Solutions and leads a multidisciplinary coalition spanning three University System of Georgia institutions. The coalition connects research, industry, and policy to build Georgia’s critical minerals innovation ecosystem, while driving resource advancement, workforce development, and economic impact.

“I'm honored to serve as the executive director of SEI. Georgia Tech’s energy research and the people behind it have always inspired me. I’m eager to listen, learn, and work alongside our community,” said Tang. “SEI connects research excellence with real-world impact, and I look forward to partnering across campus, industry, government, and communities to translate breakthrough ideas into solutions that strengthen energy security, reliability, and affordability.”

About the Strategic Energy Institute

The Strategic Energy Institute (SEI) serves as a system integrator for more than 1,000 Georgia Tech researchers working across the entire energy value chain. SEI brings together expertise to address complex energy challenges, from commercializing scalable technologies to informing long-term energy strategy and policy. Through research, education, community building, resource development, and thought leadership, SEI mobilizes Georgia Tech’s collective strengths to advance reliable, affordable, and lower-carbon energy solutions for a growing global demand.

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.”

The interactive dashboard compiles and visualizes data gathered by Matisoff, along with Program and Operations Manager Michael Morley, offering a comprehensive view of SAF production, feedstock availability, and policy trends.

EPIcenter Research Associate Yang You has designed the dashboard to translate complex datasets into policy-relevant insights for decision-makers. By organizing key metrics into interactive visuals, the dashboard helps stakeholders assess market readiness and identify regulatory actions that could accelerate SAF adoption.

Emphasizing the importance of data-driven insights, Matisoff said, “The Department of Energy has a Grand Challenge to produce 3 billion gallons a year of Sustainable Aviation Fuel by 2030, and 35 billion gallons a year by 2050. By compiling and visualizing SAF data, we can help policymakers and researchers understand progress towards these goals, where the key opportunities and bottlenecks are – and how to move forward effectively”. 

Why SAF Matters
While aviation only accounts for about 3% of global greenhouse gas emissions, it is a rapidly growing share, and decarbonizing this sector is considered one of the most challenging aspects of the energy transition. Produced from renewable feedstocks, sustainable aviation fuel offers a pathway to reduce lifecycle emissions from air travel without requiring major changes to aircraft or infrastructure. However, SAF production and deployment face hurdles related to cost, supply chain development, and policy support.

EPIcenter’s Director Laura Taylor highlighted the dashboard’s role in addressing these challenges:
“Sustainable aviation fuel is a cornerstone of decarbonizing air travel, but the market is complex and rapidly evolving. The dashboard provides clarity by organizing the relevant data in a way that’s accessible and actionable for decision-makers.”

“This tool is meant to bridge analysis and action,” said You. “By visualizing SAF production, capacity, and offtake dynamics, the dashboard allows policymakers and stakeholders to see where the market is moving, where gaps remain, and how targeted infrastructure investments or supportive policies could unlock scale.”

The EPIcenter SAF Dashboard is intended as a resource for industry leaders, policymakers, and researchers working to accelerate SAF adoption. By providing transparent, data-driven insights, Georgia Tech aims to support informed decisions that advance innovation and sustainability in aviation.

To explore the dashboard and learn more about Georgia Tech’s work on sustainable aviation fuel, visit EPIcenter’s SAF page. 

“While we often talk about food and housing insecurity, fewer people recognize energy as a basic necessity that shapes not only comfort, but also safety and stress,” said Assistant Professor Michelle Graff, who co-authored the paper published in JAMA Network Open.

Analyzing data from the U.S. Census Bureau’s Household Pulse Survey, the researchers found that 43% of households experienced energy insecurity in the past year. Among respondents who reduced spending on necessities to cover energy bills, nearly 39% reported symptoms of anxiety and 32% reported symptoms of depression — more than twice the incidence among respondents who didn’t need to make that tradeoff.

“Being able to afford your home does not guarantee you can afford to safely heat, cool, or power it,” Graff said.

Such instability disproportionately affects Black and Hispanic households, renters, and families dependent on electronic medical devices, Graff said.

And while the study was not designed to explain whether energy insecurity causes mental health issues or some other dynamic is at work, Graff said it’s incontrovertible that these groups face compounding stressors. Living in inefficient housing can lead to higher bills and unsafe temperatures, disrupting sleep and health. When combined with the financial anxiety of potential utility shutoffs and the need to sacrifice food or medicine to pay bills, these trade-offs create a cycle of chronic stress, she said.

Among other recommendations, Graff said healthcare providers should start screening for energy insecurity just as they do for food insecurity.

“We view this primarily as a data-collection initiative designed to generate the evidence needed to inform future policy recommendations and program improvements,” Graff said.

Graff is continuing to explore these issues with Carter School graduate students, including recent work on state-level aid implementation with Ph.D. student Ryan Anthony and upcoming research with other students on how energy insecurity impacts eviction rates.

The article, “Energy Insecurity and Mental Health Symptoms in US Adults,” was published Oct. 27, 2025, in JAMA Network Open. It is available at https://doi:10.1001/jamanetworkopen.2025.39479.

Read more »

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.

Countries around the world have been discussing the need to rein in climate change for three decades, yet global greenhouse gas emissions – and global temperatures with them – keep rising.

When it seems like we’re getting nowhere, it’s useful to step back and examine the progress that has been made.

Let’s take a look at the United States, historically the world’s largest greenhouse gas emitter. Over those three decades, the U.S. population soared by 28% and the economy, as measured by gross domestic product adjusted for inflation, more than doubled.

Yet U.S. emissions from many of the activities that produce greenhouse gases – transportation, industry, agriculture, heating and cooling of buildings – have remained about the same over the past 30 years. Transportation is a bit up; industry a bit down. And electricity, once the nation’s largest source of greenhouse gas emissions, has seen its emissions drop significantly.

Overall, the U.S. is still among the countries with the highest per capita emissions, so there’s room for improvement, and its emissions haven’t fallen enough to put the country on track to meet its pledges under the 10-year-old Paris climate agreement. But U.S. emissions are down about 15% over the past 10 years.

Here’s how that happened:

US Electricity Emissions Have Fallen

U.S. electricity use has been rising lately with the shift toward more electrification of cars and heating and cooling and expansion of data centers, yet greenhouse gas emissions from electricity are down by almost 30% since 1995.

One of the main reasons for this big drop is that Americans are using less coal and more natural gas to make electricity.

Both coal and natural gas are fossil fuels. Both release carbon dioxide to the atmosphere when they are burned to make electricity, and that carbon dioxide traps heat, raising global temperatures. But power plants can make electricity more efficiently using natural gas compared with coal, so it produces less emissions per unit of power.

Why did the U.S. start using more natural gas?

Research and technological innovation in fracking and horizontal drilling have allowed companies to extract more oil and gas at lower cost, making it cheaper to produce electricity from natural gas rather than coal.

As a result, utilities have built more natural gas power plants – especially super-efficient combined cycle gas power plants, which produce power from gas turbines and also capture waste heat from those turbines to generate more power. More coal plants have been shutting down or running less.

Because natural gas is a more efficient fuel than coal, it has been a win for climate in comparison, even though it’s a fossil fuel. The U.S. has reduced emissions from electricity as a result.

Significant improvements in energy efficiency, from appliances to lighting, have also played a role. Even though tech gadgets seem to be recharging everywhere all the time today, household electricity use, per person, plateaued over the first two decades of the 2000s after rising continuously since the 1940s.

Costs for Renewable Electricity, Batteries Fall

U.S. renewable electricity generation, including wind, solar and hydro power, has nearly tripled since 1995, helping to further reduce emissions from electricity generation.

Costs for solar and wind power have fallen so much that they are now cheaper than coal and competitive with natural gas. Fourteen states, including most of the Great Plains, now get at least 30% of their power from solar, wind and battery storage.

While wind power has been cost competitive with fossil fuels for at least 20 years, solar photovoltaic power has only been competitive with fossil fuels for about 10 years. So expect deployment of solar PV to continue to increase, both in the U.S. and internationally, even as U.S. federal subsidies disappear.

Both wind and solar provide intermittent power: The sun does not always shine, and the wind does not always blow. There are a number of ways utilities are dealing with this. One way is to use demand management, offering lower prices for power during off-peak periods or discounts for companies that can cut their power use during high demand. Virtual power plants aggregate several kinds of distributed energy resources – solar panels on homes, batteries and even smart thermostats – to manage power supply and demand. The U.S. had an estimated 37.5 gigawatts of virtual power plants in 2024, equivalent to about 37.5 nuclear power reactors.

Another energy management method is battery storage, which is just now beginning to take off. Battery costs have come down enough in the past few years to make utility-scale battery storage cost-effective.

What About Driving?

In the U.S., gasoline consumption has remained roughly constant but fuel efficiency has generally improved over the decades.

Sales of electric vehicle, which could cut emissions more, have been slow, however. Some of this could be due to the success of fracking: U.S. petroleum production has increased, and gasoline and diesel prices have remained relatively low.

People in other countries are switching to electric vehicles more rapidly than in the U.S. as the cost of EVs has fallen. Chinese consumers can buy an entry-level EV for under US$10,000 in China with the help of government subsidies, and the country leads the world in EV sales.

In 2024, people in the U.S. bought 1.6 million EVs, and global sales reached 17 million, up 25% from the year before.

The Unknowns Ahead: What About Data Centers?

The construction of new data centers, in part to serve the explosive growth of artificial intelligence, is drawing a lot of attention to future energy demand and to the uncertainty ahead.

Data centers are increasing electricity demand in some locations, such as northern Virginia, Dallas, Phoenix, Chicago and Atlanta. The future electricity demand growth from data centers is still unclear, though, meaning the effects of data centers on electric rates and power system emissions are also uncertain.

However, AI is not the only reason to watch for increased electricity demand: The U.S. can expect growing electricity demand for industrial processes and electric vehicles, as well as the overall transition from using oil and gas for heating and appliances to using electricity that continues across the country.

This article is republished from The Conversation under a Creative Commons license. Read the original article.