The three-year project, Close-Space Sublimation for Scalable Perovskite Optoelectronics Manufacturing, is led by Liqiu Zheng, assistant professor of physics at Albany State, and Juan-Pablo Correa-Baena, associate professor and Goizueta Junior Faculty Chair in Georgia Tech’s School of Materials Science and Engineering and School of Chemistry and Biochemistry. The researchers aim to develop manufacturing approaches for perovskite thin films that could make production faster, more material-efficient, and easier to scale.

The award also will expand Albany State’s research capabilities and create hands-on learning opportunities for students at both institutions.

Georgia Tech launched the Research Collaboration Forum (RCF) in 2023 to bring together researchers from Georgia Tech and historically Black colleges and universities, as well as representatives from industry, federal agencies, and national laboratories. Participants develop partnerships and pursue new research opportunities.

Through its Seed Grant Program, the forum provides early-stage funding to help teams generate preliminary results, build capacity, and compete for larger external awards. Grants have ranged from $10,000 to $50,000. The Georgia Tech-Albany State team was among the projects funded during the forum’s inaugural cycle.

"The RCF seed funding allowed us to establish the collaboration, generate preliminary data, and demonstrate that our teams could work effectively together," Correa-Baena said. "That initial year helped us identify the key scientific questions and provided the foundation for the new three-year, $1.3 million NSF award."

The project reflects a central goal of the RCF: helping promising collaborations move from initial conversations to sustained research partnerships with national impact.

"By bringing together Georgia Tech researchers, HBCU faculty, industry partners, and federal stakeholders, we are building research capacity, strengthening long-term collaborations, and creating pathways to larger funding opportunities," said George White, director of strategic partnerships at Georgia Tech.

The award will support new thin-film manufacturing and characterization equipment at Albany State. The investment will help researchers accelerate materials discovery, build long-term research capacity, and give students access to advanced semiconductor research tools.

“This award elevates the research footprint of Southwest Georgia and recognizes Albany State’s ability to contribute to nationally relevant scientific research,” Zheng said. “Beyond the research, it creates opportunities for students to gain experience with advanced semiconductor technologies and helps strengthen the pipeline of talent entering the semiconductor workforce.”

Albany State and Georgia Tech will jointly study thin-film growth, structure, and device performance. As part of the collaboration, Georgia Tech researchers will support Albany State as the team acquires and begins using the new equipment, sharing expertise in operation, data collection, and analysis.

Students will receive hands-on training in thin-film deposition, X-ray characterization, solar-cell fabrication, and data analysis. The project also includes exchanges between Georgia Tech and Albany State, giving students access to research facilities, expertise, and collaborative research opportunities at both institutions.

The research focuses on a manufacturing technique known as close-space sublimation. Researchers typically produce perovskite thin films using liquid inks. Close-space sublimation instead uses heat to transport material as vapor across a short distance. The team will investigate whether the approach can be adapted for perovskite materials, potentially enabling faster, more material-efficient, and more scalable manufacturing.

The progression from a Research Collaboration Forum seed grant to a federally funded research project illustrates the forum’s broader goal: helping Georgia Tech and HBCU researchers turn early connections into sustained collaborations that expand opportunity, strengthen research capacity, and advance discovery.

Hosted by Georgia Tech's Strategic Energy Institute (SEI), the annual summit brought together representatives from academia, industry, government, national laboratories, and nonprofits to discuss strengthening energy systems and preparing for a changing energy future. Topics focused on collaboration, workforce development, and strategies for building resilience amid the challenges in the energy sector.

"Energy resilience requires more than technological innovation. It depends on strong partnerships across universities, industry, government, and national laboratories," said SEI Executive Director Yuanzhi Tang, who delivered the summit’s opening remarks. "The UEILC Summit serves as an important forum to exchange ideas, identify shared challenges, and build collaborations among energy institutes across the country. These efforts are essential to strengthening energy systems and preparing the workforce that will support them."

Following Tang, Tim Lieuwen, Georgia Tech's interim president and executive vice president for Research, delivered a keynote address on academia's role in advancing a resilient energy future. Lieuwen emphasized the unique role of universities as trusted conveners and honest brokers, bringing together stakeholders with diverse perspectives to navigate an evolving energy ecosystem. 

A fireside chat featuring Mike Caravaggio of EPRI, Stephanie Gossman of Georgia Power, and Tang explored how artificial intelligence, data center expansion, and rising electricity demand are reshaping the energy landscape. The speakers also discussed how their organizations are working to strengthen grid reliability, expand infrastructure, and support a reliable energy future.

Founded with support from the Alfred P. Sloan Foundation to strengthen collaboration among university energy institutes, the University Energy Institute Leadership Collaborative provides a forum for advancing energy research, education, and engagement.

“A systems approach to today’s energy challenges — where academia, industry, and government collaborate to provide many perspectives on a problem — can only happen if there is a venue and a community where these discussions can safely take place,” says Daniel Tkacik, executive director of Carnegie Mellon’s Scott Institute for Energy Innovation as well as the principal investigator of the Sloan Foundation grant that supports the UEILC. “This summit continues to provide that valuable venue for collaboration, and we are grateful to our generous hosts at Georgia Tech.”

The summit theme, Expanding Collaborations for Resilient Energy Futures, framed discussions on regional energy resilience, grid vulnerabilities, geopolitical influences on energy planning, the growth of data centers, and workforce education. 

"The UEILC annual summit provides an excellent opportunity for energy institute leaders from across the nation to come together and share perspectives, strengthen partnerships, and advance solutions that can help shape more resilient energy futures," said Christine Conwell, director of planning and operations at SEI. "The conversations over the course of the summit reinforced the importance of cross-sector partnerships in creating new opportunities for innovation."

Conversations and networking opportunities provided a broader understanding of how different regions are strengthening energy resilience, addressing emerging threats to power infrastructure, adapting to geopolitical and economic shifts, and preparing for increased electricity demand driven by artificial intelligence and data centers.

"It was exciting to see so many diverse perspectives united by a common goal of building more resilient energy systems," said Jordann Britt, SEI program manager and a UEILC Bridge-Builder Fellow. 

Britt helped organize the summit as part of the UEILC Bridge-Builder Fellows Program, which supports emerging leaders in the university energy community.

The strategy, announced in July 2026 as part of Panama’s National Agenda on Advanced Technologies, was developed through a partnership between Panama’s National Secretariat of Science, Technology, and Innovation (SENACYT) and Tech AI, Georgia Tech’s AI solutions developer. The effort brought together leaders from government, academia, industry, and civil society.

The strategy identifies priorities for AI across economic development, workforce preparation, digital infrastructure, governance, research, and international collaboration.

Pascal Van Hentenryck, the A. Russell Chandler III Chair and professor at Georgia Tech and director of the National Science Foundation (NSF) Artificial Intelligence Institute for Advances in Optimization (NSF AI4OPT), helped lead the development of the strategy.

“Panama has a unique opportunity to leverage its position as a global logistics and financial hub to become a trusted leader in AI,” Van Hentenryck said. “The strategy lays out what is needed to develop the talent, infrastructure, data, and institutions to support that ambition.”

Panama’s focus on artificial intelligence comes as the country expands its role in Latin America’s technology ecosystem. The 2025 Latin American Artificial Intelligence Index classified Panama as an “Adopter” nation and reported a 1.47-point increase in the country’s score from the previous edition.

Building the strategy

Van Hentenryck and Timothy M. Brown, interim managing director of Tech AI at Georgia Tech, authored the strategy following months of engagement with stakeholders throughout Panama.

The process included workshops, interviews, and a nationwide survey that gathered input from more than 6,700 Panamanians. Government officials, researchers, industry representatives, educators, and community members contributed perspectives on the country’s opportunities and challenges.

“The strategy was shaped by what we heard from people across Panama,” Brown said. “That input helped identify where AI can have the greatest impact and what Panama needs to build the capacity to use it effectively.”

The resulting framework is organized around five foundational pillars:

1. Governance and regulatory framework
2. Human talent and institutional capacity
3. Data and infrastructure
4. Ethics, inclusion, and sustainability
5. International cooperation and global standards

Together, the priorities are intended to support AI adoption across sectors including logistics, financial services, healthcare, agriculture, education, manufacturing, government services, and energy.

At the center of the strategy is a vision for Panama to become “Latin America’s Trusted Hub for AI Innovation.” The plan builds on the country’s established role in global logistics and finance, envisioning a “Digital Canal” that supports the secure movement of data, technology, talent, and innovation throughout the region.

Eduardo Ortega-Barría, national secretary of science, technology, and innovation at SENACYT, said the strategy provides a foundation for Panama to participate more fully in the global AI economy.

“AI is transforming economies, societies, and the way we work,” Ortega-Barría said. “For Panama, this strategy is about preparing our country to participate in that transformation while ensuring that innovation creates opportunities for our people and strengthens our competitiveness.”

Connecting research and policy

The strategy reflects contributions from Georgia Tech researchers and experts, SENACYT leadership, policymakers, international advisers, and private-sector stakeholders. AMD also participated in the initiative.

Keith Strier, senior vice president of Global AI Markets at AMD, said international partnerships will be important as Panama develops its technology ecosystem.

“Panama is building its domestic AI capabilities and infrastructure together with the United States as a strategic partner,” Strier said. “This is an important moment as Panama establishes its role in the global AI ecosystem.”

For Georgia Tech, the project illustrates how the Institute’s research and technical expertise can inform national-level technology planning.

Rather than focusing solely on AI development, the strategy connects technical capabilities with workforce preparation, research capacity, infrastructure, entrepreneurship, cybersecurity, and responsible governance. It also calls for efforts to expand access to AI opportunities in rural and underserved communities.

Van Hentenryck said developing the strategy required understanding Panama’s priorities alongside the opportunities presented by emerging technologies.

“Working on a national strategy requires bringing together different perspectives,” he said. “Panama has its own strengths, challenges, and ambitions, and those were essential to shaping a strategy that can work for the country.”

From strategy to implementation

The launch marks the beginning of the next phase: putting the strategy into action.

The road map includes recommendations for expanding AI education and workforce training, strengthening research capacity, improving digital infrastructure and cybersecurity, establishing responsible AI governance, and creating mechanisms to support entrepreneurship and innovation.

Franklin Morales, head of the International Technical Cooperation Office at SENACYT and project lead for the AI initiative, said the strategy provides a starting point for that work.

“Now this is not the end. It is not even the beginning of the end. But it is, perhaps, the end of the beginning,” Morales said, quoting Winston Churchill. “After months of work, Panama launched its National Artificial Intelligence Strategy, a vision of what this country needs to build for a new technological era.”

“The launch of the strategy is an important milestone, but the real work begins with implementation,” Brown said. “The focus now is on turning these priorities into lasting capacity, developing talent, and infrastructure while supporting research, entrepreneurship, and responsible AI adoption.”

For Panama, the strategy establishes a framework for using artificial intelligence to strengthen economic competitiveness, expand opportunity, and support the country’s role in an increasingly technology-driven global economy.

New research from Georgia Tech suggests that a simple change to a chatbot’s instructions could reduce that risk. The work was accepted to the ACM AI Letters publication. 

Researchers Lorenzo de la Loza and Vijay Madisetti tested eight AI models in 1,280 experiments involving simulated conversations about delusional beliefs. They found that instructions designed to discourage excessive agreement reduced the models’ confirmation of those beliefs by 73.8%. Adding a self-reflection step increased the reduction to 77%.

“We think that this paper will help AI providers and solution creators better understand how best to protect and safeguard their users from harming themselves,” Madisetti said. “This may make AI safer for all of us.”

AI chatbots are designed to be helpful and supportive. But that behavior can become risky when a chatbot agrees with a user’s beliefs without questioning whether they reflect reality. Researchers call this behavior sycophancy.

Courtney Crooks, a principal research scientist at the Georgia Tech Research Institute and adjunct faculty member in Georgia Tech’s School of Cybersecurity and Privacy, said chatbot conversations can have both positive and negative effects.

People may feel more comfortable discussing difficult thoughts with a chatbot than with another person. But if the chatbot always agrees, users may miss opportunities to question their beliefs or consider other points of view.

The same behavior that makes a chatbot feel supportive, Crooks explained, can also encourage harmful patterns of thinking.

The researchers tested instructions that told AI models not to reinforce users’ beliefs simply to be agreeable. The team evaluated the models using 16 clinically derived scenarios involving delusional beliefs.

The team also tested other safety tools, including systems designed to flag potentially harmful content. The results suggest that changing a chatbot’s instructions can be an effective way to reduce excessive agreement in the scenarios tested.

Crooks called the findings a promising direction for future research combining psychology and AI safety.

The study did not test people in clinical settings or prove that the approach prevents real-world harm. However, it offers developers a potential way to make chatbots more careful when responding to vulnerable users without building an entirely new AI model.

NSF has awarded $20 million to renew the National AI Institute for Advances in Optimization (NSF AI4OPT) through 2031 and $18.4 million to renew the National AI Institute for Adult Learning and Online Education (NSF AI-ALOE) for a new five-year period. The awards will support research at the intersection of artificial intelligence and optimization, as well as the development of AI technologies for adult learning, higher education, and workforce development. A third Georgia Tech-led National AI Institute, the AI Institute for Collaborative Assistance and Responsive Interaction for Networked Groups (AI-CARING), will continue its research under a one-year no-cost extension.

Together, the institutes represent a broad range of AI research at Georgia Tech, with work spanning energy systems, advanced manufacturing, education, reskilling, and workforce development.

The renewed investments come as NSF continues to prioritize artificial intelligence, advanced research infrastructure, and development of the nation’s STEM workforce.

“Georgia Tech has built a nationally recognized ecosystem for artificial intelligence research, education, and application,” said Tim Lieuwen, interim president and executive vice president for Research at Georgia Tech. “These NSF investments recognize the strength of our researchers and the importance of advancing AI in ways that address real-world challenges. The continued support for NSF AI4OPT and NSF AI-ALOE will allow Georgia Tech researchers to push the boundaries of what AI can accomplish while preparing students, educators, and the workforce for an increasingly AI-enabled future.”

Advancing AI-Powered Decision-Making

The $20 million renewal of NSF AI4OPT builds on five years of research at the intersection of artificial intelligence and mathematical optimization.

Led by Pascal Van Hentenryck, the A. Russell Chandler III Chair and professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech, NSF AI4OPT develops AI and optimization technologies for complex systems where decisions must be made quickly, efficiently, and under uncertainty.

In its next phase, the institute will place greater emphasis on real-time and interactive optimization, advancing scalable, efficient, and trustworthy approaches to decision-making. Research will continue to address challenges in areas such as energy, supply chains, transportation, and advanced manufacturing.

“NSF AI4OPT’s first five years demonstrated the power of bringing AI and optimization together to address some of the most challenging decision-making problems facing society,” said Van Hentenryck. “This renewal gives us the opportunity to build on that foundation and develop AI systems that can reason, optimize, and make decisions in real time. We are especially excited about advancing trustworthy AI for complex systems in energy, supply chains, and manufacturing, while continuing to educate the next generation of AI researchers and practitioners, starting with middle and high schoolers.”

NSF AI4OPT combines artificial intelligence, mathematical optimization, and computing to create systems that support decisions in complex and dynamic environments. Its research includes applications in critical infrastructure and industry, where decisions often involve competing objectives, uncertainty, and rapidly changing conditions.

The institute also has an education and workforce development mission, introducing AI and optimization concepts to students and educators while preparing learners for an AI-enabled workforce.

“NSF AI4OPT represents exactly the kind of interdisciplinary research that Georgia Tech is positioned to lead,” said Raheem Beyah, provost and executive vice president of Academic Affairs at Georgia Tech and former dean of the College of Engineering and Southern Company Chair. “The institute connects foundational advances in artificial intelligence and optimization with engineering challenges that have direct implications for our economy and society. Its continued investment in research and education strengthens Georgia Tech’s ability to prepare students to lead in a rapidly changing technological landscape.”

Advancing Adaptive Expertise Through AI

NSF AI-ALOE’s renewed $18.4 million award launches the institute’s next five years of research into how artificial intelligence can transform adult learning and online education.

The renewal builds on NSF AI-ALOE’s previous work, including the development of seven AI agents and a data architecture for AI-augmented learning. Its next phase will focus on adaptive expertise, or the ability to apply and transfer learned skills to new situations.

AI-ALOE’s research in Phase 2 will expand beyond traditional educational settings into workplace and vocational training, with a focus on occupational skills in areas including advanced manufacturing and data science.

NSF AI-ALOE will also build on advances in generative and agentic AI to develop AI agents that support the acquisition of complex skills across higher education and workplace environments.

“NSF AI-ALOE’s renewal reflects an important shift in how we think about AI and learning,” said Ashok Goel, director of NSF AI-ALOE and professor of computer science and human-centered computing in the School of Interactive Computing at Georgia Tech. “Our goal is not simply to use AI to deliver more content or automate instruction. We are exploring how AI can help people develop adaptive expertise, so they can transfer what they learn to new and changing situations. That has enormous implications for higher education, workforce development, and lifelong learning.”

The institute’s research will examine four dimensions of AI-supported learning: scalability, personalization, transfer of learned skills, and whole-person learning. It will also advance foundational AI research in machine teaching, metacognition in AI agents, and mutual theory of mind between humans and AI agents.

NSF AI-ALOE will use the data architecture developed in Phase 1 that is capable of ingesting, standardizing, analyzing, and visualizing information generated through interactions with AI tools, learning management systems, and student information systems. Researchers will use that data to better understand how learners and instructors interact with AI and improve AI-powered learning technologies.

The institute is also expanding partnerships with higher education institutions and industry. Its research network includes five colleges and universities and six industry partners in advanced manufacturing and related fields, creating opportunities to reach as many as 370,000 learners nationally through skilling, reskilling, and upskilling efforts.

“AI is changing both what people need to learn and how they can learn it,” said David Joyner, Zvi Galil PEACE Chair in the College of Computing and interim vice provost for AI in Education at Georgia Tech. “NSF AI-ALOE is uniquely positioned to study that transformation at scale. Its work can help us understand how AI can support learners without replacing the human elements of education, while giving people new ways to build and apply skills throughout their lives and careers.”

Continuing AI-CARING’s Research

The renewed support for NSF AI4OPT and NSF AI-ALOE builds on Georgia Tech’s broader participation in the National AI Institutes program.

Georgia Tech’s AI-CARING Institute will continue its research under a one-year no-cost extension, allowing the institute to carry forward its research objectives and partnerships.

The continued work of AI-CARING, alongside the renewed investments in NSF AI4OPT and NSF AI-ALOE, reflects the breadth of Georgia Tech’s AI research community and its work across disciplines.

From AI Research to Real-World Impact

The renewed NSF investments build on Georgia Tech’s broader efforts to advance AI research, education, and application through Georgia Tech AI and Tech AI.

Georgia Tech AI connects Tech’s AI community across research, education, and impact. Tech AI complements this work by serving as Georgia Tech’s engineering and delivery arm, bringing together researchers, engineers, research software engineers, students, and technical expertise to move AI projects from early-stage concepts toward operational systems.

This connection helps create a pathway from foundational research to technologies that can be developed, tested, and deployed in real-world settings.

For NSF AI4OPT, that work includes AI systems that support complex decisions in critical systems and industries, from energy and manufacturing to supply chains and transportation. For NSF AI-ALOE, it includes AI technologies that help people acquire, apply, and transfer skills across classrooms, workplaces, and lifelong learning environments.

Tech AI works at the application and deployment stage, partnering with researchers, industry, and public-sector organizations to develop and implement scalable AI solutions. Its work spans AI engineering and solution delivery, applied research, industry and government partnerships, workforce development, and responsible and trustworthy AI.

“Georgia Tech’s strength in AI comes from the connections between research, talent, and application,” said Timothy M. Brown, managing director of Tech AI. “Our NSF AI Institutes advance research across critical areas, while Tech AI serves as Georgia Tech’s engineering and delivery arm, working with industry and public-sector partners to turn AI expertise and research into deployable solutions. Together, these efforts create a pathway from AI innovation to real-world impact.”

Together with AI-CARING and Georgia Tech’s broader AI research community, the institutes contribute to a universitywide effort to advance AI that is technically innovative, human-centered, and designed for meaningful impact.

“AI is transforming virtually every sector, and Georgia Tech has a responsibility to help shape that transformation,” Lieuwen said. “These institutes bring together some of Tech’s strongest capabilities in research, education, and interdisciplinary collaboration. Their renewed support gives us an opportunity not only to advance the science of AI, but also to ensure that the benefits of these technologies reach the people, organizations, and communities that will rely on them.”

About the Georgia Institute of Technology

The Georgia Institute of Technology, or Georgia Tech, is one of the top public research universities in the U.S., developing leaders who advance technology and improve the human condition.

The Institute offers business, computing, design, engineering, liberal arts, and sciences degrees, as well as professional development and K-12 programs for fostering success at every stage of life. Its more than 56,000 undergraduate and graduate students represent 54 U.S. states and territories and more than 146 countries. They study at the main campus in Atlanta, at instructional sites around the world, and through distance and online learning.

As a leading technological university, Georgia Tech is an engine of economic development for Georgia, the Southeast and the nation, conducting more than $1 billion in research annually for government, industry and society.

About the National Science Foundation

The U.S. National Science Foundation (NSF) is an independent federal agency that supports science and technology in all 50 states, the District of Columbia, and U.S. territories. For more than 75 years, NSF has advanced the frontiers of STEM research and innovation. To keep the United States at the leading edge of discovery, NSF funds basic and solutions-oriented research that generates new knowledge to fuel industries of the future, create world-leading technologies, and improve the lives of the American people.

After a grueling, world-class competition, the team won the $4 million first-place prize in August 2025. 

The team has since disbanded. Some members have moved into the corporate world, while others continue in academia. Despite these varied paths, Team Atlanta’s search for breakthrough AI cybersecurity solutions continues.

From AIxCC to Microsoft

Team Atlanta lead Taesoo Kim, principal security researcher Dongkwan Kim, and three other former team members joined Microsoft Security after the competition, where they continue to work on AI and cybersecurity in the Frontier Offensive Research & Generative Exploitation (FORGE) Lab.

The move has given the researchers an opportunity to apply concepts from AIxCC to real-world software. It has also exposed them to challenges that are difficult to replicate in a competition or research lab.

Dongkwan Kim said that team members continue to share what they learned through research papers, reports, software, and benchmarks. At Microsoft, these lessons are tested against real customer code.

This work led to MDASH, Microsoft's multi-agent, multi-model system for vulnerability discovery and remediation. The system uses more than 100 AI agents to search for vulnerabilities, validate potential findings, and help develop fixes.

Rather than asking a single AI model if a piece of code contains a vulnerability, MDASH can examine a problem using multiple agents. The system can also try to create an exploit to determine whether a potential vulnerability is real and reachable.

For Dongkwan Kim and the other former Team Atlanta members at Microsoft, moving from AIxCC to MDASH showed how research can look different when applied to real software.

“AIxCC was not an endpoint for Team Atlanta,” he said. “We continue sharing what we learned through technical reports, papers, open artifacts, and benchmarks for vulnerability discovery and patching.”

Working with customer code introduced new challenges for the Microsoft team. The researchers had to consider security boundaries, existing development processes, how findings would be reviewed, and how vulnerabilities would be prioritized for action.

These challenges created a new feedback loop between research and product development. The team took ideas from AIxCC, test them on real software, learn where they worked and where they fell short, and use those lessons to improve the system.

The experience also showed that AI does not replace the need for cybersecurity experts. Human researchers are still needed to provide context, decide how to break down difficult problems, validate findings, understand attack paths, and determine the potential impact of a vulnerability.

Taesoo Kim, a professor in the School of Cybersecurity and Privacy and vice president of security research at Microsoft, said the competition continues to influence the Microsoft team's work.

“Every single activity that we’ve done at Microsoft was influenced by AIxCC,” he said in a 2026 interview with Microsoft.

The work has continued to grow. In 2026, Microsoft introduced MAI-Cyber-1-Flash, an AI model built specifically for cybersecurity and designed to work with MDASH.

The model is designed to handle many of MDASH's tasks while allowing larger AI models to focus on harder problems. Microsoft reported that replacing 80% of the models used by MDASH with MAI-Cyber-1-Flash increased its performance on the CyberGym benchmark from 88.4% to 95.95%.

The benchmark includes more than 1,500 examples from 188 software projects. Microsoft also reported that the new setup can achieve similar performance at half the cost of the previous MDASH configuration.

“AIxCC has sparked cybersecurity innovation across the globe, from frontier AI companies to small academic labs,” said Taesoo Kim. “It is an incredibly exciting time to be working in AI-driven security engineering and research, as we see AI fundamentally reshape how we find and fix vulnerabilities at scale.”

Continuing the Research at Georgia Tech

The AIxCC experience also continues to shape cybersecurity research at Georgia Tech.

Jiho Kim, a Georgia Tech Ph.D. student who was part of Team Atlanta's research efforts, said one of the biggest lessons he’s brought back to campus was learning how AI and traditional security tools can work together.

One of these tools is fuzzing. Fuzzing tests software with large numbers of inputs to detect unexpected behavior or security problems. Large language models (LLMs), meanwhile, can reason about code and help guide the search.

During the AIxCC competition, the Team Atlanta combined these ideas in CtxFuzz, a system that uses an LLM to help guide fuzzing toward parts of software that may contain vulnerabilities.

The research was submitted to the ACM/IEEE International Conference on Automated Software Engineering, where it received the ACM Distinguished Paper Award.

“Since AIxCC, we’ve continued building on some of the core findings from our two-year journey with Team Atlanta,” Jiho Kim said.

“One of the biggest lessons for us was understanding how LLMs and traditional fuzzing can work together in a mutually beneficial way, using each to overcome the limitations of the other and ultimately find more and deeper vulnerabilities.”

The researchers also wanted to make the lessons from AIxCC useful to other cybersecurity researchers.

They created an Open-Source Cyber Reasoning System Framework (OSS-CRS) and CRSBench, which provides researchers and security professionals with ways to use, test, and build on technology developed through AIxCC.

“We’ve also been focused on making the results of AIxCC useful to the broader security community,” Kim said. “We created OSS-CRS and CRSBench so that researchers and practitioners can more easily use, reproduce, and build upon the capabilities and lessons from our AIxCC work.”

The team's research has also moved beyond identifying vulnerabilities. Researchers are studying how well AI models can understand and reason about computer code.

Jiho Kim and his lab mates in the Systems Software & Security Lab developed COIN, a benchmark designed to test how well LLMs can reason about code. The work has been accepted to the 2026 Conference on Empirical Methods in Natural Language Processing.

The Georgia Tech researchers are also developing additional benchmarks to study the various types of reasoning required for cybersecurity. They are using what they learn to create new datasets and methods for training AI models focused on cybersecurity.

“We’re using what we learn from these benchmarks to train cybersecurity-focused models through new datasets and training methodologies,” Jiho Kim said.

Photos by John Popham/College of Computing

Cognitive behavioral therapies require patients to practice skills between sessions. Clinicians rely on retrospective patient reports to understand what happens during that time.

However, recall bias, selective disclosure, and difficulty remembering can render those reports subjective, incomplete, or inaccurate.

Clinicians can now see sensor data on how patients’ bodies react in these situations thanks to a new technology toolkit from Georgia Tech and Emory University.

A new app that syncs with a FitBit wearable device can track and record heart rate, step count, noise level, and other sensor data when patients practice prolonged exposure homework assigned by their therapists.

A web dashboard lets clinicians review that data, add notes, and track patient progress. This information helps them structure therapy sessions more effectively and accelerate patient progress.

The toolkit is the culmination of a $1.2 million National Science Foundation grant awarded to Georgia Tech’s Rosa Arriaga and Emory’s Andrew Sherrill in 2019.

Arriaga is an associate professor at Tech’s School of Interactive Computing. Sherrill is an assistant professor in the Department of Psychiatry and Behavioral Sciences at the Emory School of Medicine.

Sherrill is also a practicing clinical psychologist at the Emory Healthcare Veterans Program and has worked with veterans with PTSD for more than a decade. 

Arriaga and the students in her Ubicomp Health and Wellness Lab spent five years developing the toolkit, which they call the Patient Engagement Computational Sensing System (PECSS).

“It was a lot of master’s students and undergrads who built the back end to the app and the dashboard,” Arriaga said. “It was a lot of work and effort by a lot of students to take an idea from conception to deployment.”

The researchers originally titled the toolkit the Prolonged Exposure Collecting Sensing System, but they changed the name after realizing it could be used for other types of therapy.

“The idea is that for any therapy that has a homework component, this can bridge the gap between patient self-reporting and clinician intuition,” Arriaga said.

Nathaniel Swinger, a Ph.D. student in Arriaga’s lab, co-led a paper on the study’s results along with Ph.D. student Myeonghan Ryu. Their paper received a best paper award at the 2026 ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW). Best papers are given to less than 1% of the papers published at the conference.

“Our findings showed that the deployment was quite successful,” Swinger said. “Over 75% of the patients who incorporated PECSS into their treatment experienced a clinically significant change in symptoms. We don’t know if that’s causal, but there is some evidence the system worked to facilitate the process.”

Prolonged exposure is a method for systematically approaching trauma memories and reminders to make them safe and tolerable for patients. It is typically delivered in one-on-one therapy sessions.

Sherrill said the toolkit shows promise toward enhancing prolonged exposure therapy outside of clinical settings.

“Prolonged exposure has been researched more than any other therapy that exists,” Sherrill said. “We know it’s effective, but we’ve had problems for decades implementing it in the real world.

“There’s a need for technology in taking what patients learn in their sessions and having them replicate it outside of the clinic.”

Until now, the impact prolonged exposure on a patient with PTSD depends on the patient’s willingness to self-report the results of their homework.

“The number of potential triggers is an endless list,” Sherrill said. “We want people to approach them with their guard down and not to engage in avoidance or soothing behaviors.”

Sherrill said most of his patients have shown interest in continuing to use the toolkit — even after therapy is no longer needed.

“A system like this could reduce the frequency of sessions with a therapist,” Sherrill said. “We’re teaching patients to be their own therapists. This toolkit gives them a greater sense of independence.”

Given the promising results, Swinger said that ideally the next step would be a large-scale clinical trial.

“We’re looking to better test the causal clinical impacts of the system and a more generalizable notion of efficacy,” Swinger said. “We want to rigorously test it in a clinical setting for people beyond veterans with PTSD.”

Researchers at Georgia Tech's Center for Advanced Communications Policy will use the funding to help older adults access assistive technologies that support independent living.

Principal research scientist Nathan Moon and a team of colleagues at the Center for Advanced Communications Policy (CACP) in Georgia Tech’s Ivan Allen College of Liberal Arts were awarded a three-year, $1.5 million grant from the National Institute on Disability, Independent Living, and Rehabilitation Research. The project is in collaboration with the Center for Inclusive Design and Innovation (CIDI) in the College of Design.

The funding will support the ACCESS for Aging Well with Assistive Technology (AT) 2.0 project, which builds on the researchers’ previous work to help older adults live independently.

“As Americans continue to age and we're growing older as a society, we want to ensure that people can continue to live independently and participate in the communities of their choice,” Moon said. “The tools that we have developed here are intended to do that.”
 

In a study accepted for the 2026 ACM Conference on Computer and Communications Security, Georgia Tech researchers interviewed 20 military sailors about their cybersecurity experiences, training, and responses to hypothetical scenarios. Their findings point to a gap between how mariners are trained to handle emergencies and how cyberattacks can compromise the systems they depend on. 

The study found several gaps in how crews prepare for cyber threats:

• Training often misses the mark. Thirteen participants said that standard cybersecurity training was largely irrelevant to their shipboard duties, and 14 reported receiving no mission-specific cybersecurity training.
• Operational technology is often overlooked. Eighteen participants said their training did not distinguish between traditional information technology and operational technology, such as navigation and propulsion systems.
• Obvious failures are easier to recognize. Eighteen participants said they would primarily look for visible signs of operational problems when identifying a potential cyberattack.
• Cybersecurity is often a low priority. Seventeen participants described their cybersecurity training as absent, low quality, or a low priority.

These gaps matter because cyberattacks can compromise a system without causing it to stop working. For example, an attacker could manipulate navigation data, leaving a crew to make decisions based on false information.

“I’d assume it was anything but cyber at first. It would probably take me quite some time to get to that conclusion,” one participant told the researchers.

The team examined three hypothetical scenarios: GPS spoofing, loss of propulsion control in a high-traffic area, and failure of electronic navigation systems in a congested channel at night.

In each scenario, mariners would first focus on keeping the vessel safe, using established procedures to stabilize the ship and troubleshoot its systems. While this response is essential during an emergency, it could delay recognition of an attack if crews do not consider that the system or its data may have been compromised.

For example, a ship navigating a narrow channel depends on accurate location information, but what happens when its navigation systems appear to work normally while a hacker manipulates that data?

An attacker could use GPS spoofing to falsify a vessel’s reported position. If the false position is close enough to the ship’s actual location, a mariner might not notice anything wrong. The vessel could gradually head toward shallow water or another obstacle without triggering an obvious error or alarm.

“By the time the mariner realizes something is wrong, the ship’s size and limited maneuverability could make it too late to avoid a grounding or collision,” said Anna Raymaker, a Georgia Tech Ph.D. student who studies maritime security.

The scenario illustrates a central challenge in maritime cybersecurity: An equipment failure may be easy to recognize, but a cyberattack that provides false information can be much harder to detect.

Batten the Hatches: Cybersecurity with Military Mariners was written by master’s student Ryan Von Brock, Raymaker, Research Scientist Animesh Chhotaray, Associate Professors Frank Li and Saman Zonouz, and Provost & Executive Vice President for Academic Affairs Raheem Beyah.

The research was conducted in Georgia Tech’s Cyber-Physical Security Lab, a collaboration between the School of Cybersecurity and Privacy and the School of Electrical and Computer Engineering. 

Photos by Terence Rushin/College of Computing

Policymakers, health system executives, community leaders and others working to address this challenge now have help from Rural Evaluation and Analytics for Community Health (REACH GA), an advanced decision-support platform that offers a comprehensive view of healthcare use in rural Georgia. In addition to updated information on rural health issues, the platform provides computer-based tools to guide allocation of healthcare resources and explore the potential impact of changes to rural health services. 

“The REACH GA platform provides up-to-date, actionable information to help leaders understand the big picture of healthcare in rural Georgia,” said Jon Duke, M.D., a principal research scientist in the Georgia Tech Research Institute (GTRI) and the project’s principal investigator. “Beyond more accurate data, the platform uses artificial intelligence (AI) and other computer-driven tools to support improved decision-making.”

Of Georgia’s 159 counties, 126 are considered rural. Approximately 24 percent of the state’s 11.3 million residents live in these rural areas.

Read the full article on the GTRI news page

For many of these patients, care has long meant pain and disfigurement alongside other severe side effects, rather than receiving treatment that addresses the disease itself. This new ARPA-H award marks a potential turning point.

Lead researcher Susan Napier Thomas, Woodruff Professor in the George W. Woodruff School of Mechanical Engineering and the Parker H. Petit Institute of Bioengineering and Bioscience (IBB), has collaborated with her colleague J. Brandon Dixon, Woodruff Professor in the Woodruff School and IBB, for more than a decade on this project. The research partners are driven by the lack of meaningful treatment options available to patients.

“Funding support at this level is unprecedented,” Thomas said. “It finally gives us a chance to move beyond symptom management and toward real treatment. We’re addressing an underserved population with a huge unmet need.” 

A Gap in Care

The lymphatic system helps keep fluid moving through the body and plays a key role in immune health. When it does not function properly, fluid can build up in tissues, causing chronic pain and other long-term complications. Thomas noted that despite its toll on patients, lymphatic disease has lagged decades behind cardiovascular care in both treatment and research investment. 

“We are excited about this groundbreaking project in lymphatic engineering,” said Andrés García, IBB executive director. “By uniting interdisciplinary expertise, this work addresses long-standing challenges in lymphatic disease and moves meaningful solutions closer to the patients who need them most.”

What Comes Next

In the coming years, Thomas, Dixon, and their research partners will work toward an initial human trial, with an early focus on rare lymphatic conditions in children, as well as chronic disease in adults.

“This award reflects Georgia Tech’s growing leadership in using engineering to solve some of healthcare’s biggest challenges,” said Carolyn Seepersad, Eugene C. Gwaltney Jr. School Chair and professor in the Woodruff School. “It reinforces the Institute’s role in advancing innovations that improve patient care and strengthen Georgia’s position as a hub for health technology and biomedical innovation.”

The award was made through ARPA-H’s Groundbreaking Lymphatic Interventions and Drug Exploration (GLIDE) program led by Dr. Kimberley Steele.

This research was funded, in part, by the Advanced Research Projects Agency for Health (ARPA-H) under Agreement No. 1AY2AX000137-01. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. government.

Clark Atlanta University (CAU) is strengthening its artificial intelligence programs through AIHUB@CAU, an initiative supported by a $2.79 million National Science Foundation (NSF) grant and developed in collaboration with Georgia Tech’s NSF AI Institute for Advances in Optimization (AI4OPT). 

One moment they're playing games or watching television. The next, they experience breathing failure, shock, or neurological decline. If the warning signs aren't recognized early enough, the child may require emergency transfer to the intensive care unit (ICU), where interventions can include breathing tubes, blood pressure medications, and other lifesaving measures.

The phenomenon is called “emergent deterioration.” 

"Kids will look fine, and then all of a sudden they'll fall off a cliff and rapidly deteriorate," said Dr. Mark Mai, a pediatric intensivist at Children’s Healthcare of Atlanta, who cares for critically ill children in the ICU. "When those kids go to the ICU and need emergency interventions, they have a much higher rate of death than kids we bring up to the ICU in a controlled fashion."

To help clinicians identify those high-risk patients sooner, researchers at Georgia Tech are partnering with Children's clinicians and technology teams to create an artificial intelligence-powered warning system. Known as Algorithm for Counteracting the Clinical Onset of Rapid Deterioration (ACCORD), the three-year project is one of the first funded projects focused on a software solution by the Children’s Healthcare of Atlanta Pediatric Technology Center at Georgia Tech (PTC). Project teams are working simultaneously on model development, app design, and user testing. And, unlike many healthcare AI projects that remain confined to research papers, ACCORD is being designed from the beginning for use in a real hospital. The ultimate goal is to help clinicians intervene before a child reaches a life-threatening emergency. 

Teaching AI to Spot Trouble Earlier

Today, clinicians rely on an early warning score that combines measurements such as heart rate, blood pressure, and breathing rate into a single risk assessment. While helpful, the approach simplifies a complex clinical picture.

"The current system is a pretty basic rubric that sums up all the data points and then decides whether they are high-risk or low-risk, but from the data science and machine learning perspective it's not optimal," said Kai Wang, an assistant professor in the School of Computational Science and Engineering and project co-principal investigator with Mai.

Rather than evaluating patients through a fixed scoring system, ACCORD's AI model analyzes dozens of data streams, including vital signs, laboratory results, and clinical observations. The goal is to uncover subtle patterns that may signal deterioration hours before the physical warning signs become obvious to clinicians. 

"We want to build a more sophisticated algorithm that can look into vitals, blood pressure, heart rate, and other measures to provide a more granular forecast of risk," Wang said.

Building that capability into an algorithm requires making sense of one of healthcare's biggest challenges: messy, incomplete, and constantly changing data. To develop the model, researchers are training and testing it using information pulled from Children's electronic health records. Some information, such as vital signs, is collected continuously. Other information may be recorded less frequently or at irregular intervals, such as nursing observations.

"That is both the most challenging and the most exciting part of this project," said Himadri Pandey, a Ph.D. student in H. Milton Stewart School of Industrial and Systems Engineering working on ACCORD. "We have to consider the realities of a healthcare setting. Data may be incomplete or delayed, staffing and resources may change, and any recommendation must be understandable and usable by clinicians."

"The goal is not simply to build a model that works mathematically," she added. "It's to build something that could genuinely support decision-making in a hospital."

Building Technology Nurses Will Use

For ACCORD to improve patient outcomes, the technology must fit naturally into clinicians' daily workflow. Most nurses care for multiple patients simultaneously and cannot continuously monitor a single child. Because episodes of emergent deterioration are relatively rare in a full day of care, opportunities to gain experience recognizing the earliest warning signs can be limited. Acknowledging that reality, project leaders involved frontline nurses from the beginning.

"We went straight to the nurses and said, 'This is what we're trying to do. You tell us what would make your job easier, how you need the flow, what it needs to look like, and we will build according to that,” said Christina Roberts, program manager for PTC and the Health Informatics Core at Children’s. "We brought them into the process very early and gave them a say in the development, so they know exactly what to expect when the tool comes to the floor."

The team plans to integrate ACCORD directly into the Children’s electronic health record system, where clinicians already access patient information. The application will combine vital signs, laboratory results, and other clinical data into a single dashboard that can automatically flag patients at elevated risk of deterioration.

Researchers and clinicians continue to meet regularly as the platform evolves.

"How do we distinguish what the algorithm finds important and what is just always there?" said Dr. Claire Stokes, a pediatric hematologist and oncologist with the Aflac Cancer and Blood Disorders Center at Children’s, who is involved in the project. "Because a clinician probably always wants to see the vital signs, even if they're not alert levels, for example."

Those conversations help ensure that the system provides useful information without overwhelming clinicians with additional data or alerts. The team hopes to begin testing the system in clinical settings by 2027 before broader deployment. For the researchers working on ACCORD, however, the project's success will ultimately be measured by the patients it helps.

"I know that one research project cannot solve every problem in healthcare," said Pandey, who has experienced the hospitalization of a close family member. "But being able to contribute, even in a small way, allows me to replace some of that earlier helplessness with purpose."

If successful, ACCORD could give clinicians something they rarely have when a child begins to decline — time. 

As AI systems become more agile with expanded access to tools, computer networks, and other real-world systems, finding better ways of identifying and preventing risky AI behavior becomes even more urgent. Still, high-profile incidents of AI behaving in unintended or dangerous ways are rare, which makes them difficult to study and understand. 

“If we can understand and detect what changes inside an AI model right before it begins behaving in unsafe or misaligned ways, we may be able to inform more effective safeguards,” said Eyas Ayesh, a second-year Ph.D. student in Georgia Tech’s School of Psychology and graduate fellow at the Center for Computation and Cognition.

Ayesh and his advisor, Assistant Professor Anna Ivanova, are developing a probe designed to identify internal activity patterns associated with harmful behaviors in large language models. Their work is supported by a $300,000 award from Coefficient Giving.

Misbehaving Models

Training a probe requires examples of both desirable and undesirable behavior researchers can teach it to distinguish between them. Because deceptive or unsafe responses can be rare and highly dependent on the model, prompt, and context, researchers may not have enough examples to determine what causes the behavior or how to recognize it the next time.

But an unexpected finding in AI research gave Ayesh an idea. In studies of a phenomenon known as emergent misalignment, researchers found that fine-tuning models on a narrow harmful task could lead them to produce a much wider range of problematic responses to unrelated prompts. For example, researchers found that fine-tuning a model to write insecure code without warning users could lead to other concerning behaviors unrelated to coding.

Ayesh realized he could use that phenomenon to solve the problem of scarce data. Starting with open-source AI models, Ayesh fine-tuned them to display targeted undesirable behaviors more frequently. Then, he analyzed different versions of the deliberately misaligned models using a probe that monitors the internal activity of the model.

The probe works somewhat like a pattern detector, looking at activity inside the model as it generates responses and searching for patterns associated with particular behaviors. Researchers can then train a classifier to distinguish patterns associated with desirable behavior from those associated with harmful behavior.

“Just as different parts of the brain turn on and off when people do different tasks, so do the different parts of an AI model,” Ayesh said. “When a model produces a specific type of response — such as deceptive statements or harmful advice — we may observe recurring patterns in its internal activity. We refer to these patterns as representations of a behavior.”

Inside the Black Box

Ayesh is now testing whether the internal patterns he identified remain consistent across differently tuned AI models. Early results suggest that signals identified in deliberately misbehaving models can also be detected in their original versions.

The next step is determining whether those signals can help researchers identify or flag concerning behavior in models that have not been deliberately fine-tuned to misbehave. 

“I hope our research in AI safety can help develop some guardrails and some ways to both identify and prevent misaligned behavior in AI systems, given how much of an impact they're already having on the world,” he said. 

Ayesh is a member of Georgia Tech’s AI Safety Initiative, which brings together researchers across disciplines to study and improve the safety of increasingly capable AI systems.

Ivanova said the value of their research lies partly in approaching AI safety from a fundamental science perspective.

“As scientists, we have been trained to think about the underlying mechanisms and causes of the behaviors we observe,” Ivanova said. “For us, it's important to develop a fundamental understanding of how these models work, or we will risk missing the bigger picture.”

However, a new paper by a Georgia Tech-led research team suggests that the requirements can disproportionately burden family farms and other small food producers due to a lack of technological infrastructure, resources, or compliance expertise.

The rule is mandated by Section 204(d) of the Food Safety Modernization Act, which Congress passed and President Barack Obama signed into law in 2011. Intended to help the FDA quickly identify the source of contamination, the rule requires producers or distributors with more than $25,000 in annual produce sales to comply or face significant fines. 

Earlier this year, the FDA extended its compliance deadline to July 2028. One reason for the extension may be the volume of feedback from stakeholders affected by the rule. The FDA received more than 1,200 comments during an open forum, with feedback from stakeholders ranging from major distributors to small farmers. 

“Many small farmers commented on how they may not have the devices needed to input this data,” said Soonho Kwon, a Ph.D. student in Georgia Tech’s School of Interactive Computing (SIC) and lead author of the paper. “Does this mean they have to purchase it themselves? Do they need to become educated on how to use computing systems?”

Kwon is a member of the Equity and Sustainability in Computing Lab, which is led by SIC professor and chair Shaowen Bardzell.

Many small farms also said they can’t afford to hire someone to track the data.

One small farmer said the amount of data tracking required by the rule is overwhelming. Some of the required data include:

• location and name of the field where food was harvested
• date of harvest
• packing dates
• shipping dates

“Please do not force us to do technology stuff,” the commenter said. “It’s hard enough to keep up with the local demand as it is. We run a pretty low digital technology effort. It’s basically harvest fresh organically grown veggies and sell them to local people.”

Kwon said the intention of the Food Traceability Rule is admirable.

“Preventing foodborne illness is critical, and data- and technology-driven governance can be a powerful tool toward that goal,” he said. “But it’s equally important to think critically about who is supposed to be the default user of these systems, and who gets missed in the process. It’s undermining the people who are unable to comply.”

Other factors didn’t appear to be considered by the FDA, including the lack of technological infrastructure in rural America, where many foods are grown.

“Many parts of rural America have limited or unreliable internet access,” Kwon said. “If the FDA wants them to send this data, they must comply within 24 hours. Does that mean they have to stop their daily routine and drive to a city that has internet to send it? The rule is operating on the assumption that everyone is dealing with the same physical environment.”

Culture and religion also seem overlooked. Some commenters raised concerns that Amish farmers who avoid certain technologies for religious reasons would be pressured into digital record keeping.

Many major distributors and organizations expressed support for the Food Traceability Rule in the FDA’s forum. Catherine Wieczorek, a Ph.D. student in SIC and co-author on the paper, said these organizations don’t have the same tension points as smaller producers.

“Larger food producers have the operational capacity to do the type of work the rule requires,” Wieczorek said. “We find there’s a power imbalance when it comes to smaller food producers like a family farm. Change at that scale is difficult for smaller producers.”

Bardzell, who advises Kwon and Wieczorek, said the situation provides computing researchers an opportunity to create interventions that will benefit those with fewer resources.

“In a data age when we’re adding AI into our work processes and labor practices, we need to think about how this need creates unequal burdens on different stakeholders in our food system,” Bardzell said. “It’s a significant challenge for computing going forward.”

Kwon will present the paper on behalf of his collaborators next week at the ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW) in Salt Lake City, Utah. Their research is supported by the National Science Foundation.

Georgia Tech researchers have developed a tool that can recover deleted conversations from AI assistants running on Android phones. The tool, called ORISA, can also rebuild the recovered information into a working AI session.

The findings show that deleting an AI conversation on a phone does not necessarily mean it removes every trace.

The research could give digital investigators a new way to recover information from phones. It also raises privacy concerns for people who use AI assistants that run locally on their devices.

Local AI assistants run directly on a phone rather than sending conversations to a cloud service. While the local assistant is running, information from conversations can remain in the phone’s working memory even after it is deleted.

ORISA searches this memory for pieces of deleted conversations.

In one experiment, researchers created a fictional AI assistant called “Project Note Keeper” using the ChatterUI Android app. They gave it 100 fictional facts about a project, then deleted the conversation.

ORISA recovered information from the deleted conversation and used it to create a new working session. When researchers asked the restored assistant to list the project notes it had been given, it returned 26 facts. The original session could access only 14 facts because the rest had fallen outside its active memory.

The experiment showed that researchers could recover information the AI had already stopped actively using.

“It can help establish what information someone shared with an AI assistant and what assistance they received,” said Ph.D. student Ken Xu, lead author of the study.

The researchers tested ORISA with 10 Android apps and three AI models. Across 60 test configurations, the tool recovered conversation information that remained in the phone’s memory.

The researchers say the information becomes harder to recover as the phone’s memory is overwritten by other activity.

The research was presented at the 2026 IEEE Symposium on Security and Privacy. The paper, titled Recovering and Rehosting Mobile Local LLM Conversations and Contexts via Memory Forensics, was authored by Ph.D. students Xu, David Oygenblik, Runze Zhang, and Mingxuan Yao; Research Scientist Muhammad Ibrahim; and Associate Professor Brendan Saltaformaggio. The work was done in Georgia Tech's Cyber Forensics Innovation Laboratory, a collaboration between the School of Cybersecurity and Privacy and the School of Electrical and Computer Engineering.