As artificial intelligence (AI) becomes more integrated into everyday life, researchers are working to better understand and reduce the new digital safety risks it creates. 

Veronica Rivera, an incoming assistant professor in Georgia Tech's School of Cybersecurity and Privacy (SCP), studies human-computer interaction, security, and privacy to create safer digital experiences. Her research earned her selection to the second cohort of the Computing Research Association (CRA) Trustworthy AI Research Fellowship for Early Career Scholars.

"I am excited and honored to be part of this year's CRA Trustworthy AI Research Fellowship cohort," said Rivera. 

"This program reflects the importance of cross-disciplinary research and collaboration in building AI systems that benefit and support society. I look forward to spending the next year building collaborations that support me and my students' research into protecting the digital safety of the diverse communities who use AI tools."

Rivera's research examines how technology shapes interpersonal relationships. Using empirical and design-based methods, she partners with communities affected by technology-facilitated abuse to understand digital safety risks and develop technologies that better protect users.

Through the fellowship, Rivera will study how AI is reshaping technology-facilitated abuse and develop data collection methods that enable researchers to assess these evolving risks over time.

"Trustworthy AI is a key research area in the School of Cybersecurity and Privacy and the College of Computing," said SCP Interim Chair Mustaque Ahamad. "The CRA Trustworthy AI Research Fellowship recognizes Veronica's innovative research and will help her build a research program that advances an area of strategic importance to our school and the broader AI community."

Supported by Microsoft, the CRA Trustworthy AI Research Fellowship recognizes early-career computing researchers who combine technical expertise with interdisciplinary approaches to address the ethical, societal, and human-centered challenges of artificial intelligence. The fellowship provides interdisciplinary training, mentorship, and collaboration opportunities that help researchers integrate ethical, societal, and human-centered perspectives into AI research and development.

Over the next year, Rivera and the other fellows will participate in a series of professional development activities, including a four-day Field School in Cambridge, Massachusetts. The program also includes quarterly virtual meetings, mentoring opportunities, and collaboration with scholars participating in related AI and data ethics programs.

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A School of Computing Instruction (SCI) faculty member recently shared his expertise in computing education with to an international audience.

SCI Instructor Pedro Guillermo Feijóo-García was selected by Fulbright Colombia to moderate the closing panel of the Fulbright Reimagined Summit 2026: AI Horizons: Learning Pathways for Tomorrow.

Held last month at Universidad del Norte in Barranquilla, Colombia, the summit convened scholars, educators, policymakers, and industry leaders to examine how artificial intelligence (AI) is transforming education, work, governance, and society.

Fulbright Colombia invited Feijóo-García to moderate the closing discussion, From Adopters to Architects: Professions of Tomorrow, in recognition of his research on human-AI interaction and the hidden curriculum in engineering and computer science education.

"As a Fulbright alumnus, being invited to contribute to such an important event was an honor and a privilege," Feijóo-García said. "Beyond the personal recognition, I viewed this opportunity as a meaningful platform to facilitate conversations around the future of education, technology, and workforce development."

From Computing Education to AI Futures

At Georgia Tech, Feijóo-García directs the People-Agents Research for Computing Education (PARCE) Laboratory. His research examines the intersection of computing education, STEM learning, and AI, focusing on student preparedness, professional development, and the hidden curriculum- which he explains as the implicit skills and expectations that shape students' academic and career success. 

Feijóo-García says his perspective is shaped by years of work in computing education. During his master's studies in systems and computing engineering at Universidad de los Andes in Colombia, he explored ways to expand access to computing education through interactive programming environments. He went on to earn his Ph.D. in human-centered computing from the University of Florida, where he focused his research on computing education and human-centered AI.

"AI is transforming how we learn, work, communicate, make decisions, and interact with one another," Feijóo-García said. "As educators, we need to prepare students not only with technical competencies, but also with critical thinking, ethical reasoning, collaboration, and the ability to adapt."

Preparing Students for the Professions of Tomorrow

Rather than viewing AI as a challenge for higher education, Feijóo-García sees it as an opportunity for universities to rethink how they prepare students for an evolving workforce. He believes educators should help students develop the judgment to know when AI can enhance their work and when independent reasoning and human expertise are essential.

"The conversation should not revolve around whether AI belongs in education, but rather how we integrate it responsibly and intentionally," Feijóo-García said.

He also hopes students begin to think of AI not only as artificial intelligence, but as augmented intelligence. "The goal should not be to replace human thinking, but to enhance it," he said.

Feijóo-García emphasized that AI should augment human capabilities rather than replace human judgment. Preparing students for an AI-driven workforce requires helping them understand when AI can support their work and when human reasoning, creativity, and expertise are essential.

Strengthening U.S.–Colombia Collaboration

The summit reinforced the value of collaboration among scholars, educators, policymakers, and industry leaders in advancing research and education. Earlier this year, Feijóo-García participated in another Fulbright Colombia event focused on strengthening scientific collaboration between Colombia and the United States.

The Fulbright Program, which operates in more than 160 countries worldwide, is the United States government’s flagship international academic exchange program to support peaceful relations between the people of the U.S. and the people of other countries. Fulbright Colombia, the binational commission for educational exchange between the U.S. and Colombia, has supported academic and scientific collaboration between the two countries since 1957.

Feijóo-García’s academic journey reflects the impact of these collaborations. The Fulbright Program supported his doctoral studies in the United States, and he continues to build connections between Colombia and the U.S. through his work at Georgia Tech.

"Scientific diplomacy is built on people," Feijóo-García said. "Events like the Fulbright Reimagined Summit provide the environment where trust is established, ideas are exchanged, and collaborations begin."

Top 3 Takeaways

• AI is accelerating cyberattacks.
• The same technology can strengthen defenses.
• The biggest risk is falling behind.

Artificial intelligence is changing cybersecurity on both sides of the battlefield. The technology helping organizations improve efficiency also enables cybercriminals to identify vulnerabilities, develop exploits, and launch attacks at unprecedented speed.

Researchers in the School of Cybersecurity and Privacy (SCP) say the greatest concern is not that AI is creating entirely new forms of cyberattacks. Instead, it is accelerating activities that attackers already perform, making existing threats quicker, cheaper, and harder to stop.

"AI is dramatically speeding up cyberattacks," said Brendan Saltaformaggio, associate professor in the SCP and the School of Electrical and Computer Engineering (ECE). "AI can identify vulnerabilities far faster than humans and often in places humans wouldn't think to look."

Hackers Are Moving Faster

Most cyberattacks include several stages: finding vulnerabilities, developing ways to exploit them, gaining access to systems, and pursuing a goal such as stealing information or disrupting operations. According to Frank Li, associate professor in the SCP and ECE, AI is having its biggest impact on the early phases of that process.

"AI can help attackers explore potential decisions and implement attacks faster than in the past, whether it's identifying software bugs or constructing social engineering hooks," Li said.

The pace of attacks has already changed dramatically. Peter Swire, J.Z. Liang Chair in the SCP and professor of law and ethics in the Scheller College of Business, says attackers are moving from vulnerability discovery to exploitation much faster than in the past.

"The average time until an exploit is detected even a couple of years ago was measured in months," Swire said. "Now it is measured in hours."

That compressed timeline is forcing organizations to rethink how quickly they identify, prioritize, and patch vulnerabilities.

The Risk Reaches Everyone

Healthcare systems, critical infrastructure providers, government agencies, and small businesses remain attractive targets because they often manage sensitive information or essential services while relying on legacy technology. But experts emphasize that no organization is immune.

"Unfortunately, everyone is at risk," Saltaformaggio said. "AI is not creating new victims; AI is making attackers faster and more effective at targeting the same organizations they have always pursued."

Organizations with outdated systems or limited cybersecurity resources face particular challenges because AI allows attackers to identify weaknesses in less time and at lower cost than ever before.

Fighting AI With AI

Cybersecurity defenders also have access to the same technology.

Experts say AI can strengthen defenses before, during, and after a cyberattack. Defenders can use AI to identify and patch vulnerabilities more quickly, reducing opportunities for attackers to gain access. AI can also detect subtle signs of intrusion by correlating activity across networks and recognizing patterns that would be difficult for humans to spot in real time.

Once an attack occurs, AI can support rapid investigation and response by analyzing how the compromise happened, identifying root causes, and helping deploy targeted protections.

"AI can help defenders in every step of stopping a cyberattack," Saltaformaggio said.

Researchers at Georgia Tech are already demonstrating AI's defensive potential. Saltaformaggio pointed to Georgia Tech's recent success in the Defense Advanced Research Projects Agency’s (DARPA) Artificial Intelligence Cyber Challenge, which highlighted how AI systems can autonomously discover and reason about software vulnerabilities at large scale. Swire also noted that a Georgia Tech team led by Professor Taesoo Kim won a $4 million DARPA prize for developing advanced AI-based cybersecurity defenses.

Speed Is the New Defense

Despite the promise of AI-powered security, significant challenges remain. Security teams need AI tools they can trust, ones that explain how they reached conclusions and provide evidence that analysts can verify. Attackers may also attempt to manipulate AI systems, creating new risks for organizations that rely heavily on automated tools.

Li says organizations cannot rely on traditional, human-paced security processes to keep up.

"AI's primary impact on cyberattacks is enhancing speed and scale," Li said. "Organizations need to adapt to more agile defenses and processes that account for this, in many cases relying on AI as well to help speed up defensive actions."

Organizations also need to rethink how they respond to vulnerabilities. Swire argues that traditional patch management is no longer fast enough in a world where exploits can emerge within hours.

"The entire process for patching systems will have to be re-engineered," he said.

The cybersecurity landscape has always been an arms race between attackers and defenders. AI hasn't changed that reality. It has simply accelerated it. The organizations that will be best positioned are those that adopt AI as quickly as the adversaries they are trying to stop.

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Ann Dunkin, P.E. (BSIE 1986, MSIE 1988), will serve as event chair for the Institute of Industrial and Systems Engineers’ (IISE) inaugural Emerging Technologies Forum on Aug. 26.

Dunkin is an IISE Fellow, the highest classification of IISE membership that recognizes outstanding leaders of the profession who have made significant, nationally recognized contributions to industrial and systems engineering. She has served as chief information officer at the U.S. Department of Energy and the U.S. Environmental Protection Agency, as well as chief strategy and innovation officer at Dell Technologies. She has also held numerous leadership positions at Hewlett Packard, largely in the areas of manufacturing, research and development and IT. She currently serves as a distinguished external fellow with Georgia Tech’s Strategic Energy Institute.  

Dunkin holds appointments as a distinguished professor of the practice in Georgia Tech’s School of Cybersecurity and Privacy and in the Jimmy and Rosalynn Carter School of Public Policy. She is also a distinguished external fellow at the Georgia Tech Research Institute (GTRI).

“The Emerging Technologies forum is a great opportunity for practicing ISEs to get exposure to a range of emerging technologies in a short period of time and identify those that they want to experiment with and implement in their organizations,” Dunkin said. 

The Aug. 26 event, which will be held virtually from 1-5 p.m. EDT, will explore technologies shaping the future of industrial and systems engineering, including artificial intelligence, digital twins, and robotics and autonomy. 

As event chair, Dunkin will be joined by experts from the healthcare, aerospace, logistics, manufacturing and technology industries, including representatives from Royal Caribbean, The Boeing Company, UPS and other organizations. 

The complimentary event is open to engineers and improvement professionals of all backgrounds. Registration is available through the IISE Emerging Technologies Forum website. 
 

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The Institute for Data Engineering and Science (IDEaS) has selected faculty to serve in two associate director roles that will center on developing internal and external community projects at the intersection of data sciences, machine learning, and AI. They will support the executive director, David Sherrill, in creating research and upskilling opportunities for the campus community in fundamental and applied machine learning and AI for research and by identifying promising new research collaborations and potential funding sources.

Ghassan AlRegib will serve as associate director for Research and Education, seeking large-scale funding opportunities, creating upskilling workshops, coordinating efforts with relevant student groups, and fostering thought leadership in areas relevant to IDEaS’ mission: data science, machine learning, artificial intelligence, and high-performance computing.

AlRegib is the John and Marilu McCarty Chair in the School of Electrical and Computer Engineering. His group, the Omni Lab for Intelligent Visual Engineering and Science (OLIVES), conducts machine learning research aimed at shifting learning systems from data-centric to human-centric, with an emphasis on robustness, uncertainty, human-AI interaction, and explainability. His team’s research is applied to vision, subsurface imaging, autonomous systems, healthcare intelligence, and education. 

Vijay  Ganesh has been appointed associate director for Research, after serving in this role on an interim basis since 2025. He will support the development of new strategic research initiatives, work closely with faculty, research staff, and industry partners to create and strengthen interdisciplinary teams, and help IDEaS seek large-scale funding opportunities.

Ganesh is a professor of computer science, and he served as co-director of the AI Institute at the University of Waterloo before moving to Georgia Tech in 2023. He is a co-founder and a steering committee member of the Centre for Mathematical AI at the Fields Institute and an AI Fellow at the Balsillie School of International Affairs. 

“I’m delighted that Vijay Ganesh has agreed to continue in the role of associate director of Research; his expertise in AI and formal verification methods has been central to our formulation of new research initiatives,” said Sherrill. “I’m also thrilled that Ghassan AlRegib is joining our leadership team. He is a longtime key IDEaS faculty member, and he has led multiple centers at Georgia Tech. His recent experience hosting a planning grant for his Accessible Healthcare Through AI-Augmented Decisions perfectly positions him to help lead IDEaS’ AI for Health initiative, and his experience in developing AI courses and educational tools makes him the right person to lead our AI upskilling workshops.”  

“I am thrilled to join IDEaS,” AlRegib said. “Data science, machine learning, and AI are no longer specialized disciplines; they are foundational tools on which nearly every research enterprise on campus depends. IDEaS' greatest value is not to be the place where AI happens, but to be the connective tissue that makes AI impactful and accessible for everyone else. This is the kind of work I care deeply about, helping connect faculty curiosity with collaborative teams, emerging methods with usable skills, and student interest with real AI capability. The best measure of our success will be what every other unit at Georgia Tech is able to accomplish because IDEaS exists.”

"I am delighted and honored to be reappointed as IDEaS' associate director for Research. AI is a paradigm-shifting technology that is transforming research across every discipline, and IDEaS is at the forefront of enabling that transformation at Georgia Tech,” Ganesh said. “My vision for this role centers on advancing the research on campus in both the foundations of AI and its applications to mathematics, science, and engineering. To that end, I look forward to shaping strategic research initiatives, partnering with faculty across campus to pursue large-scale funding opportunities, and organizing timely workshops and seminars that bring our community together. It is a privilege to continue this work.

-Christa M. Ernst | Research Communication Manager

Wei Zhu, assistant professor in the School of Mathematics, has been awarded a five-year, $500,000 CAREER Award from the National Science Foundation (NSF). The CAREER Award, NSF’s most prestigious honor for early-career faculty, helps promising researchers establish a foundation for a lifetime of leadership in their fields. Zhu’s award will support research and education initiatives focused on artificial intelligence (AI).

“I am very honored and excited to receive the NSF CAREER Award,” says Zhu. “This award will support research by my fantastic team of students and post-doctoral researchers and give me the opportunity to carry out education and outreach programs that expand our impact.”

Advancing AI Applications

Since joining Georgia Tech in 2024, Zhu’s research has focused on the mathematical foundations of machine learning and their applications in science and engineering.

With support from the CAREER Award, Zhu and his team will explore the two-way relationship between data and structure in machine learning. They aim to understand how known structures in scientific and engineering problems (e.g., symmetries or physical constraints) can help machine learning models learn more accurately and efficiently from limited data. They will also study how machine learning can uncover hidden structures directly from data, revealing patterns or principles that may not be known in advance.

The project focuses on settings where large amounts of high-quality data are difficult or expensive to obtain, as is often the case in science and engineering. Using mathematical analysis, Zhu and his team will examine how much data is required to accurately learn a model, how structural information can reduce this data requirement, and how much data is needed to reliably identify a model’s underlying structure.

According to Zhu, this approach could reduce the amount of data and time needed to build and test accurate models, leading to more reliable, interpretable, and efficient AI for scientific discovery.

“Such advances align with national AI priorities and help strengthen the mathematical foundations needed for future scientific and engineering applications of AI,” he says.

Expanding AI Literacy

Zhu believes it is important to help students understand AI, noting that they must learn how to interpret and evaluate its outputs rather than accept them uncritically. 

“AI has increasingly become incorporated into many fields of study,” he says. “Institutions must determine how to best integrate it into education while also teaching students how it works. The AI education and outreach components of my project aim to help prepare students for careers at the intersection of mathematics, computing, and science.”

Zhu’s CAREER Award will support educational initiatives at multiple levels, including new graduate and undergraduate courses on machine learning. It will also support a machine learning boot camp for high school students, organized by the School of Mathematics in collaboration with Emory University’s Department of Mathematics. The boot camp seeks to introduce students to foundational ideas in AI and machine learning, with an emphasis on the mathematical principles needed to understand and responsibly use these tools.

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The house in the distance, with a red, hip-shaped roof and white walls, tells Radu Casapu that this place is probably somewhere in Spain or Portugal. 

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

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

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

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

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

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

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

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

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

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

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

Show Your Work

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

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

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

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

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

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

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

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

Practice Partner

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

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

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

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

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

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

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Georgia Tech's H. Milton Stewart School of Industrial and Systems Engineering (ISyE) is launching a new undergraduate concentration in Artificial Intelligence and Operations Research (AI/OR) in Decision-Making this fall, formalizing a comprehensive AI and machine learning curricula. The concentration brings together machine learning, optimization, online decision-making, and responsible AI into a structured pathway for students who want to specialize at the intersection of artificial intelligence and operations research; fields that have always been deeply connected and are increasingly inseparable in industry practice.

The concentration was developed under the leadership of A. Russell Chandler III Chair and Professor Guanghui (George) Lan, who served as the named lead on the proposal to the Institute Undergraduate Curriculum Committee, working with a group of ISyE faculty across the AI, optimization, and statistics areas.

The curriculum draws on a portfolio of seven undergraduate courses taught by ISyE faculty, spanning the mathematical and algorithmic foundations of modern AI. Among them is Foundations of AI for Decision Systems, developed and taught by Assistant Professor Johannes Milz, which opens the black box of large language models. Students learn what is actually happening inside the AI systems they use every day from tokenization and attention mechanisms to the training processes that shape model behavior. They will graduate able to explain why outputs vary, why models hallucinate, and when an AI response should be trusted. As Milz has observed, students who enter thinking of AI outputs as either right or wrong leave the course recognizing that hallucination is not a random error but a predictable consequence of how these systems work, a more durable form of AI literacy than any tool-based introduction could provide.

Foundations of Modern Data Science, taught by Gerald D. McInvale Early Career Professor and Assistant Professor Ashwin Pananjady, takes a "looking under the hood" approach to data science — developing the probabilistic modeling, statistical inference, and optimization foundations that make modern data methods coherent. Students engage with everything from generative modeling and Bayesian inference to A/B testing and causal inference, learning to evaluate methods critically rather than apply them as black boxes.

The concentration also draws on Foundations and Applications of Machine Learning (ISYE 4600), developed by Coca-Cola Foundation Chair and Professor Yao Xie and now taught by her and several other faculty. The course introduces senior undergraduates to the core methods of modern machine learning – supervised and unsupervised learning, classification, regression, neural networks, feature selection, and ensemble methods – with an emphasis on mathematical foundations, algorithmic understanding, and practical implementation.

Two additional courses launch alongside the concentration this fall: Responsible AI, taught by Assistant Professor Juba Ziani, which grounds questions of fairness, accountability, and human-aware decision-making in the mathematical tools of machine learning and optimization; and Optimization Foundations for Machine Learning and AI, taught by Coca-Cola Foundation Chair and Professor Katya Scheinberg.

Together with existing courses on modern data science, machine learning, reinforcement learning, online learning, and advanced stochastic systems, the concentration prepares students to understand AI systems from the inside, evaluate their outputs critically, and deploy them responsibly in the complex operational settings where ISyE graduates work: supply chain, healthcare, manufacturing, finance services, and logistics.

The concentration's launch coincides with two new program-wide requirements for all BSIE students taking effect Fall 2026: a Systems Design requirement, ensuring every graduate has experience designing solutions at the systems level rather than optimizing components in isolation; and a Human Factors overlay, reflecting the growing centrality of human-AI interaction across every domain where ISyE graduates contribute.

"The methods that power modern AI from optimization, stochastic modeling, statistical inference to sequential decision-making are the methods ISyE has taught for decades," said Dima Nazzal, Associate Chair for Academic Administration. "What is new is the intentionality with which we are making that connection explicit, and the depth of preparation we are offering students who want to lead in AI-driven industries."

The concentration was developed under the leadership of Professor George Lan, working with a group of ISyE faculty including Ashwin Pananjady, Yao Xie, Katya Scheinberg, Johannes Milz, Joel Sokol, and Juba Ziani, whose collective expertise in optimization, machine learning, and statistical modeling shaped the curriculum.

As strange as it sounds, the key to understanding life’s origins might lie in artificial intelligence. At least, according to a new approached being pursued by researchers at Georgia Tech. 

School of Electrical and Computer Engineering (ECE) Assistant Professor Amirali Aghazadeh and Ph.D. student Daniel Saeedi have developed AstroAgents, an AI system that analyzes mass spectrometry data — detailed chemical compositions from meteorites and Earth soil samples — to generate novel hypotheses about the origins of life on the planet. 

What sets AstroAgents apart is its use of agentic AI. Unlike traditional AI systems that perform fixed tasks, this agentic system is designed to pursue a scientific goal. It draws from astrobiology literature, interprets complex data, and proposes original ideas that researchers can investigate further. 

Their paper, recently featured in the journal Nature, is opening new possibilities for how scientists explore questions that have remained unanswered for decades. 

In a special Q&A, Aghazadeh and Saeedi explain how AstroAgents analyzes space chemistry, what it’s revealing about the possible origins of life on Earth, and what they hope to explore next.

READ THE Q&A

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A drone powered by artificial intelligence crashes in a remote field, destroying its onboard computer and leaving investigators without the data needed to determine whether a cyberattack caused the failure.

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

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

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

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

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

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

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

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

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

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

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

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

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

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