Andrei Fedorov and his team have found a way to take some of the guesswork out of mass spectrometry imaging (MSI), potentially giving scientists better tools to explore the molecules that influence our health.
A small sample of human tissue can hold a remarkable amount of information.
Researchers can use it to search for molecular clues associated with cancer, investigate the chemical landscape of the brain, or determine where a drug travels after it enters the body. Mass spectrometry imaging, or MSI, helps answer those questions by mapping where different molecules are located within tissue.
But there's a catch. Despite MSI's sophisticated capabilities, finding the right conditions to get useful results can still require a considerable amount of trial and error. Georgia Tech professor Andrei Fedorov and Ph.D. student John Sentmanat are working to change that.
“Mass spectrometry imaging is probably the most powerful tool to enable new discoveries in cell biology and protein biochemistry. It helps provide a direct insight not only to how the body works but also the underlying cause of disease,” said Fedorov, Associate Chair for Graduate Studies and Rae S. and Frank H. Neely Chair in the George W. Woodruff School of Mechanical Engineering. “But the techniques for using it were evolving in a very empirical manner guided by experience and intuition. It’s like shooting arrows in the dark.”
In response to this barrier, the team has developed what Fedorov likens to a recipe, a framework designed to help researchers understand how different ingredients and conditions work together to produce the molecular information they want. Published in Analytical Chemistry, the approach could make MSI more predictable and give scientists a better starting point for developing new ways to study the molecular workings of the human body.
“This paper provides a very simple recipe that everyone can read and use. You don’t need to be a chef, or in this case, be able to write complicated equations,” Fedorov said. “It even allows you to come up with a new recipe using our model as the base.”
Though the framework was designed to be accessible to researchers across the field, Fedorov and Sentmanat didn’t set out to solve a problem for the scientific community — they were trying to solve one in their own lab.
In 2020, Fedorov, who is also a faculty member in the Parker H. Petit Institute for Bioengineering and Bioscience, was awarded $1.2 million from the National Institutes of Health (NIH) for the development of a new imaging instrument that could change the study of tumor growth, disease progression, and cell function.
The technology, called BeamMap, has continued to develop with additional support from the National Science Foundation Engineering Research Center for Cell Manufacturing Technologies (CMaT), led in part by Georgia Tech. By bringing together multiple imaging techniques, including MSI, BeamMap allows researchers to study both the physical surface of a biological sample and its molecular makeup.
While developing the instrument, Fedorov and Sentmanat encountered the same challenge that has made MSI difficult to optimize: Under some conditions, they successfully extracted the molecules they wanted to study. Under others, they damaged a sample without getting the information they needed. Rather than continue relying on trial and error, the team decided they needed a theory that could point them toward the right conditions.
“As we continued developing the instrument, we realized we needed to develop a theory to identify the optimal operating conditions,” Sentmanat said. “What began as an effort to fine-tune our own technology led to a broader theory that can be applied to a wide range of other approaches.”
To build the recipe, the team returned to the basic physics that determines when molecules leave a sample and become available for detection. They distilled those complex interactions into a simpler framework, then compared its predictions with experimental data from previously published studies. When the framework held up across different MSI approaches, the researchers realized the recipe could be useful well beyond their own lab.
“It's all measurable data that any scientist in this field would be able to use,” Fedorov said. “It’s like having a magic tool that allows you, all of a sudden, not to be blind.”
Fedorov sees the new framework as one step toward making MSI easier to understand and use. By giving researchers a better starting point for choosing how to operate an MSI system, the recipe could help them spend less time searching for the right conditions and more time using the technology to answer biological questions.
That could become increasingly important as medicine moves toward understanding disease and treatment at the molecular level.
“More and more, physicians base their diagnoses on much more specific molecular information,” Fedorov said. “The drugs of the future will not be this little pill that you will be given; they will be your own immune cells. Your diagnostic tools have to match that future.”
For now, the framework is intended primarily as a tool for researchers. But by helping scientists make better use of technologies like MSI, Fedorov hopes the work can contribute to discoveries that ultimately improve how diseases are understood, detected, and treated.
Like any recipe, the framework leaves room for adjustment. The team deliberately simplified some of the complex physics behind MSI to make the framework practical for other researchers, which means it is designed to point them toward promising conditions rather than predict exactly what will happen in every experiment.
“The theory provides a recipe that researchers can adapt by adding their own flavors,” Sentmanat said. “Rather than prescribing an exact solution, it offers a framework for identifying the right ingredients and refining the conditions to achieve the best results.”
Researchers can adapt that starting point to their own instruments, samples, and questions. What began as an effort to improve BeamMap could ultimately help others refine existing MSI technologies or develop new approaches of their own.
“It’s kind of empowering.”
Researchers can use a small sample of human tissue to search for molecular clues associated with cancer, investigate the chemical landscape of the brain, or determine where a drug travels after it enters the body. Mass spectrometry imaging, or MSI, helps answer those questions by mapping where different molecules are located within tissue. But there's a catch. Despite MSI's sophisticated capabilities, finding the right conditions to get useful results can still require a considerable amount of trial and error. Georgia Tech professor Andrei Fedorov and Ph.D. student John Sentmanat are working to change that.
]]>Parker H. Petit Institute for Bioengineering and Bioscience
]]>Francis Halzen, principal investigator of the NSF IceCube Neutrino Observatory and a professor at the University of Wisconsin-Madison, was honored by the Royal Swedish Academy of Sciences for his “decisive contributions to IceCube and the discovery of high-energy neutrinos of astrophysical origin.”
“Francis proposed building a neutrino telescope in Antarctic ice, and along with his collaborators, he proved that it is possible to detect neutrinos in this challenging environment,” says Ignacio Taboada, professor in the School of Physics and former spokesperson for the IceCube Collaboration. “IceCube was a technological tour de force, and its success is now recognized by this Nobel Prize. The award energizes the nascent field of neutrino astronomy.”
A. Nepomuk Otte, professor in the School of Physics, describes the impact of IceCube’s results as “breathtaking.”
“What we are witnessing is the opening of an entirely new window into the universe, providing us with a unique view of the most violent astrophysical objects and allowing us to study the elusive nature of neutrinos,” says Otte. “I am extremely excited about the prospects of pushing to even higher energies and deeper into the universe with the next generation of neutrino experiments planned at Georgia Tech and around the world.”
IceCube is a giant neutrino telescope located at the South Pole. Envisioned by Halzen in the late 1980s and completed in 2011, the instrument detects neutrinos, tiny high-energy particles that travel through the universe and rarely interact with matter. Scientists study neutrinos to better understand phenomena such as supermassive black holes and exploding stars.
Georgia Tech has been a member of the IceCube Collaboration since 2008. The collaboration includes more than 400 physicists from 58 institutions in 14 countries.
Georgia Tech researchers continue to shape neutrino astronomy by contributing to next-generation observatories. Taboada leads the design of the trigger system for the Pacific Ocean Neutrino Experiment (P-ONE) Observatory, and Otte is the principal investigator of the NSF-funded Trinity Neutrino Observatory in Utah.
]]>Despite decades of research, the chaotic nature of turbulence makes it difficult to predict and control. This challenge impedes more than smoother air travel. Solving the turbulence problem could lead to advances in areas ranging from sustainable energy to training a smarter workforce for the era of artificial intelligence (AI).
To advance understanding of turbulence, the National Science Foundation is establishing a new, $30 million Science and Technology Center (STC) at Michigan State University. Georgia Tech is among eight universities supporting the center.
Assistant Professor Qi Tang will join the STC for Transformative Explorations in Multi-Physics and Engineering of Scientific Turbulence (TEMPEST). TEMPEST, launching on Sept. 1, aims to build trustworthy, predictive models of real-world turbulence.
Tang will lead TEMPEST’s modeling and scientific machine learning (ML) efforts. NSF will fund Tang and Georgia Tech with over $1 million from the center’s five-year, $30 million award.
By understanding and predicting turbulence, TEMPEST can unlock new applications, with a focus on fusion energy and national security. The center will also generate long-term research projects with applications in air and space flight, manufacturing, chemistry, and broaden science education and AI fluency.
“Turbulence has resisted prediction for a century,” said Tang, a faculty member in the School of Computational Science and Engineering (CSE).
“STC TEMPEST aims to develop a unified, predictive science that advances fusion energy, improves hypersonic technologies, and deepens our understanding of how stars created the elements that make up our world and ourselves. For everyday people, the center points toward abundant clean energy and faster, more efficient flight."
Turbulence is the motion of a fluid characterized by chaotic changes in pressure and speed. Turbulent flows can occur across all scales, from interactions between subatomic particles to astrophysical scales, including supernovas, black holes, and cosmic rays.
While turbulent flows are common and occur naturally, scientists still do not fully understand them. Small changes in a turbulent flow can produce dramatically different outcomes. Combined with the countless interactions across multiple scales of time and space, this makes turbulence extraordinarily difficult to predict.
“Rather than studying individual pieces of this enormously complex problem in isolation, we are bringing together theory, experimentation, computation and artificial intelligence to develop a deeper understanding of turbulence across scales,” said Michael Murillo, an MSU professor and the director of TEMPEST.
“Our goal is not simply to understand turbulence better, but to make it predictable and controllable in ways that will enable new technologies and scientific discoveries.”
The NSF TEMPEST award supports students and researchers at Michigan State University, Auburn University, Baylor University, Georgia Tech, San José State University, Texas A&M University-Corpus Christi, University of Rochester, and Yale University to combine theory, computation, AI techniques, and experimentation to build trustworthy predictive models of real-world turbulence for high-consequence applications.
Additional partners include Los Alamos National Laboratory, Sandia National Laboratories, Lawrence Livermore National Laboratory, Pacific Fusion, and General Atomics.
Together, TEMPEST researchers will build more accurate and reliable physics-grounded models. The center will test new ideas against real-world observations, then use those results to refine the models.
Tang contributes expertise in ML, scientific computing, and plasma physics to STC TEMPEST.
Earlier this year, Tang received an Early Career Research award from the Department of Energy’s Office of Science. He is using the award to build ML and data science tools that help scientists analyze massive datasets from fusion experiments and simulations.
This interdisciplinary approach is intended to move science from understanding why turbulence behaves as it does to predicting how it will behave. Through prediction and simulation, scientists could eventually engineer solutions to control turbulence in real-world scenarios.
The center will make its data and software broadly available and engage the public through museum exhibitions, immersive media, and educational programs that are expected to reach more than 10,000 K-12 students annually. TEMPEST will also help train an AI-fluent scientific workforce prepared to tackle complex problems across disciplines.
“One reason I am excited to work in TEMPEST is because it aligns perfectly with our School of CSE mission. As a discipline, CSE complements theory and experimentation as a mode of scientific discovery,” Tang said.
“We build computational models to simulate scientific and engineering concepts, like turbulence in this case, so that we can test theories that are too difficult, expensive, or risky for physical experiments.”
]]>Georgia Tech is hosting the 2026 MFEM (Modular Finite Element Methods) Community Workshop, Sept. 22-25. MFEM supports research in computational physics, earth systems modeling, engineering, energy, and other fields.
The four-day workshop is being held at the Georgia Tech Global Learning Center and will focus on improving software for scientific computing and discovery. Participants can also attend online. Registration for the workshop is open through Sept. 11.
“We're very excited and grateful for the opportunity to have this workshop at Georgia Tech,” said Tzanio Kolev, a computational mathematician at Lawrence Livermore National Laboratory (LLNL).
“Georgia Tech has a great reputation in our field, and hosting at universities is a great way to connect with students. We see students as our future colleagues who will improve MFEM for generations to come.”
MFEM is an open-source software library for solving equations in computational models. Scientists and engineers use it to build and test virtual designs on some of the world’s most powerful supercomputers before creating real-world prototypes.
In fact, MFEM powered a tsunami early-warning model that ran on El Capitan, the world’s second-fastest supercomputer. The framework completed a simulation in a fraction of a second, 10 billion times faster than conventional algorithms. Kolev was part of the team that won the 2025 Gordon Bell Prize for the project.
MFEM’s impact extends beyond its original developers. The AWS Center for Quantum Computing uses the software as the foundation for a tool called Palace. This project runs 3D electromagnetic simulations to aid in the design of quantum computing hardware.
“These workshops have been very beneficial for both the broader user community and also for MFEM developers,” said Kolev.
“Every workshop, we are surprised by the ways people are building on our work, seeing what incredible, interesting, amazing things they do with it.”
Next month’s workshop at Georgia Tech will connect MFEM users and developers from national laboratories, academia, government, and industry.
The United Kingdom Atomic Energy Authority joins LLNL and Georgia Tech in sponsoring this year’s workshop. Nearly 20 scientists from Department of Energy (DOE) laboratories are attending in person. They represent LLNL, Los Alamos National Laboratory, Princeton Plasma Physics Laboratory, and the Naval Nuclear Laboratory.
The workshop’s first day features a free, hybrid tutorial. LLNL’s High Performance Computing Innovation Center will lead the tutorial, helping new users get started with MFEM and learn what the software can do. The tutorial requires separate registration for virtual participants.
The workshop will also host a simulation and visualization contest. Participants can submit images and videos of simulations using MFEM, which will be posted in a gallery. The workshop organizers will select an overall winner.
Further workshop activities include:
The MFEM workshop highlights the role open-source software plays in scientific discovery.
By making tools freely available, researchers can build on each other’s work rather than starting from scratch. This shared approach can accelerate innovation, improve software, and make advanced computing tools accessible to a wider community.
Georgia Tech hosts a Center for Scientific Software Engineering and an Open-Source Program Office. This year’s workshop connects these units with the MFEM community to help put the software into the hands of more researchers across science and engineering.
“The MFEM workshop aims to foster collaboration among all MFEM users and developers,” said Qi Tang, a workshop organizer and assistant professor in the School of Computational Science and Engineering.
“Hosting its annual community workshop brings researchers from DOE laboratories, universities, and industry to campus, giving Georgia Tech faculty and students direct access to experts from the field.”
]]>Georgia Tech is hosting the 2026 MFEM (Modular Finite Element Methods) Community Workshop, Sept. 22-25. MFEM supports research in computational physics, earth systems modeling, engineering, energy, and other fields.
The four-day workshop is being held at the Georgia Tech Global Learning Center and will focus on improving software for scientific computing and discovery. Participants can also attend online. Registration for the workshop is open through Sept. 11.
]]>Georgia Tech’s Itamar Kolvin is aiming to solve that problem using molecular motors powered by tiny chemical reactions, giving researchers a new way to guide how fibers assemble into larger structures in the lab. The work could open the door to advances in wound healing, artificial tissues, and organ repair.
Now, Kolvin, an assistant professor in the School of Physics, has been awarded a $747,000 CAREER grant from the National Science Foundation (NSF) to support this research.
The NSF Faculty Early Career Development Program is a five-year grant designed to help promising researchers establish a foundation for a lifetime of leadership in their field. Known as CAREER awards, the grants are NSF’s most prestigious funding for early-career faculty.
“The CAREER award is a crucial opportunity to push this research forward,” says Kolvin. “NSF plays a critical role in advancing science, and we wouldn’t be able to do our work without their support. I’m incredibly grateful for their commitment to advancing science.”
While the body naturally assembles these fibers, their small size makes protein filaments difficult to control in a lab setting. Without guidance, the materials grow randomly, creating weak and disorganized structures. But Kolvin has found that exposing the filaments to controlled fluid flows can help them grow in predictable ways.
That’s where Kolvin’s molecular motors come in. He attaches the motors to rod-shaped particles called microtubules, transforming the otherwise inert particles into microscopic motorboats. Powered by chemical reactions, the microtubules move through the fluid and generate tiny currents. Those currents guide the suspended protein filaments, directing how they align and assemble.
Like sticks in a stream, the current helps the protein filaments align with the flow around them. When millions of filaments interact in this way, they can form bundles, clusters, and large-scale networks.
“How do these rods align, when do they tangle, and when do they form networks?” Kolvin asks. “I’m interested in learning how we may be able to predict and ultimately control that behavior in order to direct the ways these structures can grow.”
Kolvin’s earlier work has already shown success with actin, the filaments responsible for building muscles. When suspended in fluid, the molecular motors helped actin fibers bundle together and form a membrane-like structure.
While that work demonstrated that molecular motors could influence assembly, Kolvin now aims to create a more dynamic, tunable system. Actin is limited, he explains, because of its simple rod-like shape, lack of rigidity, and because its bonds become permanent once the fibers bundle together.
He believes the more rigid, helix-shaped flagella in this new work will offer a wider range of opportunities. “The shape and rigidity of flagella expand the variety of patterns they can form,” he explains. “These new structures could have different applications.”
Through molecular engineering, the Kolvin Lab creates flagella that are temperature-sensitive, meaning that they can make and unmake bonds at different temperatures. This property might be key in creating a system that could be assembled and disassembled on demand.
“This could make it possible to tune materials in real-time by warming or cooling the system," Kolvin explains.
By combining molecular motors with temperature-sensitive protein fibers, Kolvin aims to create systems that can be predictably shaped, assembled, and disassembled on demand, opening the door to new possibilities for tissue engineering and regenerative medicine.
The work could also help answer long-standing problems in physics about active matter and collective behavior, revealing how flow and shape influence the way millions of microscopic building blocks align, pack, and assemble into complex materials.
“This CAREER award provides an exciting opportunity to pursue new ideas and tackle difficult, far-reaching questions,” Kolvin says. “I’m looking forward to seeing where this research leads in the years ahead.”
]]>Launched in 2025, INNS serves as a hub for neuroscience and neurotechnology research, connecting researchers across disciplines to advance discovery, innovation, and societal impact.
“A core part of INNS's mission is helping people find one another, connect around shared interests, and build something bigger than they could accomplish alone,” says Executive Director Christopher Rozell. “Simon has spent his career bringing together people, perspectives, and disciplines to tackle complex challenges. I can't think of a better person to help lead our interdisciplinary research efforts and support the next generation of collaborative neuroscience research.”
A longtime leader in Georgia Tech's neuroscience community, Sponberg helped shape the institute's early foundations through the Neuro Next Initiative, which evolved into INNS. As associate director, he will help guide research strategy, foster new collaborations, and support interdisciplinary work across the neuroscience ecosystem.
“Neuro-related research touches so many fields that we inevitably have a problem of finding all the potential right people and opportunities to tap into,” he said. “A core responsibility of this role is helping catalyze the ideas of new teams of talented researchers, educators, and trainees from inception to realization.”
Sponberg is the Glen Robinson Professor in Complex Systems with appointments in the School of Physics and the School of Biological Sciences, where he directs the Agile Systems Lab. His work also connects him to the Institute for Bioengineering and Bioscience (IBB) and the Institute for Robotics and Intelligent Machines (IRIM), underscoring the cross-disciplinary approach that defines both his research and leadership.
Since joining Georgia Tech in 2014, he has built a highly collaborative research program spanning neuroscience, biology, physics, engineering, and robotics while helping foster interdisciplinary connections across campus. He currently leads a Multidisciplinary University Research Initiative (MURI) that brings together neuroscientists and engineers from five universities to understand how brains achieve fast, flexible perception and decision-making in complex sensory environments. He also co-leads the Integrative Movement Sciences Institute, an NSF Biological Integration Institute focused on understanding movement and muscle function across scales.
His contributions have been recognized through numerous honors, including a Young Investigator Award from the International Society for Neuroethology, a Klingenstein-Simons Fellowship in the Neurosciences, and the Leddy Family Dean’s Faculty Excellence Award.
As associate director, Sponberg will focus on helping researchers identify new opportunities for connection and collaboration. In addition to overseeing seed funding programs, he hopes to create pathways for researchers to build teams, access support resources, pursue ambitious interdisciplinary research ideas, and team with Georgia Tech’s undergraduate and graduate neuroscience degree programs.
“I look forward to being a resource for our community when people are looking for help in building teams or finding opportunities to support their ideas,” he said. “Bring your best ideas and please reach out if you want to talk about ideas in the interdisciplinary neuro space that you want to see realized.”
Researchers at Georgia Tech play a key role in the international collaboration. The Georgia Tech-LIGO research group includes School of Physics Professor Laura Cadonati, Assistant Professor Surabhi Sachdev, Research Scientist Margaret Millhouse, Postdoctoral Scholar Prathamesh Joshi, eight graduate students, and multiple undergraduates.
The LVK network detects gravitational waves when a massive cosmic event — like the collision of two black holes — creates invisible ripples in the fabric of space-time. Waves ripple out at the speed of light, and millions of years after the events that first created them, they reach the LVK detectors.
But detecting gravitational waves does not simply mean capturing a signal — clues first need to be untangled from background noise.
“Identifying gravitational-wave signals requires carefully separating real astrophysical events from random fluctuations in the data,” says School of Physics graduate student Urja Shah, whose work to quickly identify phenomena supports rapid follow-up by the broader astronomical community.
To support the identification of phenomena, School of Physics graduate student Megan Arogeti conducts consistency tests between waveforms, checking results to find unexpected or unusual features. “Tests like this give us confidence in our models as we continue to observe gravitational waves with increasing sensitivity,” she explains. “They support new observations and help identify exciting new physics.”
“These efforts help ensure that gravitational-wave signals are robustly identified and accurately characterized, turning each detection into a precise measurement,” adds Shah. “In turn, these measurements deepen our understanding of some of the most massive and dense objects in the universe and the fundamental laws governing the cosmos.”
When a sensor detects a gravitational wave, it produces a distinctive response, says School of Physics graduate student Shobhit Ranjan. “Those signals encode a wealth of information we can analyze to learn about their sources — their masses, spins, distance, and location.” But in order to detect these chirps, the detectors must be carefully calibrated, and if calibration is not optimal, the signals can be compromised.
Now, a new tool is helping the LVK collaboration recalibrate less optimal signals. The technique is already showing promise: In an article recently accepted in Physical Review Letters, LVK researchers successfully applied it to two interesting signals. The first signal served as a testing opportunity for the method. The team used astrocalibration to recover the data and check it against secondary independent calibration data that was available. They then put the technique to use, recovering information from a second event where no secondary calibration data were available.
“Like autotune in the music industry, the new research shows that theoretical models can be used as guides, similar to how sheet music can help a studio shift off-key music to its correct tone,” Ranjan explains. “These theoretical models suggest the shape of the signal, and together with data from other detectors, we can adjust the data and read it correctly.”
“The fact that these detectors can now not only sense cosmic events, but leverage them to improve the data being collected marks a new era in gravitational wave science,” he adds.
The LVK Collaboration also published their fifth catalog of gravitational wave events this spring. The findings include an updated estimate of how fast the universe is expanding, evidence for the existence of second-generation black holes, the most precise sky localization ever achieved for a gravitational wave source, and the first measurement of three vibrational modes of a black hole.
“Our group helped enable 140 detections out of the 161 reported in this catalog,” says Joshi, who contributed to one of the flagship searches and designed a specialized search focused on detecting especially heavy black hole mergers.
Joshi also worked on determining precise locations of where the gravitational waves originated from in the universe — research that he says will allow astronomers around the world to perform long-term follow-up observations of interesting events.
One record-setting detection showed two black holes that had violently collided more than 3 billion light-years from Earth. Researchers were able to pinpoint its location in the sky more precisely than any other gravitational wave event observed before.
Improvements in the LVK network’s ability to localize events along with the large number of detections allowed for a better estimate of the Hubble constant, which measures the rate at which the universe is expanding. The new measurement is over 25% more precise than previous estimates.
The new catalog also includes the “clearest” gravitational wave signal ever detected. The clarity of the signal led to the most accurate test of general relativity ever performed and confirmation of Stephen Hawking’s black hole area theorem.
“This catalog provides not just the largest number of black hole detections, it marks a new era of rapid progress,” Sachdev says. “This is just the beginning of what these observations will allow us to uncover.”
]]>Logically this makes sense and has been backed up by past security research. However, researchers at Georgia Tech have found hidden paths in cyber-physical systems that attackers can use to disrupt or even destroy them.
To find these hidden paths before an attacker does, the researchers built a testing tool called ICSFlux. This new tool leans on the physics used by the industrial process and maps out the system to find new threats that were once thought impossible.
ICSFlux was deployed across 11 different programmable logic controllers in six industrial sectors, including chemical manufacturing, water treatment, power grids, aircraft, desalination, and waste processing. The process uncovered twenty genuine safety violations.
In one case drawn from a chemical-plant simulation, an attack path uncovered by the tool drove a reactor past its safe pressure limit and into a simulated explosion. By using nothing but valid operator commands, the team took the reactor from a completely normal and stable state to critical territory.
Because the method relies only on the physics of a process and not on the details of any one controller, the same tool worked across all six sectors without being rebuilt, and it reduced the search space by roughly 50%.
Burak Sahin, a Ph.D. student at Georgia Tech and the study's lead author, found that by sending a series of perfectly normal, fully authorized commands, intruders can slowly nudge a physical process toward a dangerous state.
“These systems are usually judged safe as long as nobody hacks into them,'' Sahin said. “What we found is that an attacker who can send everyday commands, the same ones a normal operator sends, can patiently steer the process toward a failure. No single command looks wrong, which is exactly why the usual defenses miss it.''
Most existing tools assume an attacker can rewire the controller or change the software inside it. In the real world, those controllers are locked down and cannot be touched. ICSFlux takes the opposite and more realistic view. It treats the controller as a sealed box that cannot be opened and works only with the commands an operator is normally allowed to send.
Rather than measuring how much of a controller's software it has exercised, the usual yardstick for this kind of testing, ICSFlux measures how close the physical system is getting to an unsafe limit and steers its testing in that direction.
“Two different sensor readings can run through the exact same code and still send a reactor in completely different directions,'' Sahin said. “Looking only at the software tells you nothing about whether the physical system is safe. We had to follow the physics, not the code.''
One of the study's most important takeaways emerged when the researchers tightened the safety margins to see whether caution alone would help. Even when every command stayed within approved limits, the way the controller reacted to a steady stream of small adjustments could still cause pressure to overshoot and the reactor to fail. In other words, staying inside the rules was not always enough.
All of the team's experiments were carried out on secured, controlled test beds. The work was conducted with Georgia Tech's Cyber-Physical Systems Security Lab, whose research spans the security of cyber-physical systems from industrial programmable logic controllers to marine, automotive, and drone platforms. Georgia Tech's Cyber Forensics Innovation Laboratory, a team of researchers who work together to further the investigation of advanced cyber crimes and the analysis and prevention of next-generation malware attacks, also contributed to the paper.
The labs are a collaboration between the School of Cybersecurity and Privacy and the School of Electrical and Computer Engineering.
Fuzzing the Physical Space: Physics-Aware Testing of Black-Box Industrial Control Systems' was accepted to the 2026 IEEE Symposium on Security and Privacy. In addition to Sahin, the team includes Ph.D. students David Oygenblik, Mingxuan Yao, and Yizhi Huang as well as Associate Professors Brendan Saltaformaggio, and Saman Zonouz.
]]>Logically this makes sense and has been backed up by past security research. However, researchers at Georgia Tech have found hidden paths in cyber-physical systems that attackers can use to disrupt or even destroy them.
To find these hidden paths before an attacker does, the researchers built a testing tool called ICSFlux. This new tool leans on the physics used by the industrial process and maps out the system to find new threats that were once thought impossible.
]]>Communications Officer II at the School of Cybersecurity and Privacy
]]>This concept of “lab tool as lab assistant” is the premise of a recent paper in npj | Computational Materials titled “Thinking Microscopes: Agentic AI and the Future of Electron Microscopy,” by Vida Jamali, assistant professor the School of Chemical and Biomolecular Engineering; Amirali Aghazadeh, assistant professor in the School of Electrical and Computer Engineering; and Josh Kacher, associate professor in the School of Materials Science and Engineering.
In the paper, the team introduces the concept of “thinking electron microscopes,” in which agentic AI systems are directly integrated with the instrument. This allows microscopes to move beyond their conventional role as characterization tools and toward functioning as co-scientists for human users.
Drawing on advances in specialized large language models, or LLMs, that demonstrate their ability to collaborate, reason over data, and integrate prior knowledge, the team envisions specialized LLM-based agents assigned to specific roles and areas of knowledge expertise. By explicitly incorporating domain knowledge into specialized agents and distributing information across multiple agents with focused expertise, the approach enables parallel evaluation of competing hypotheses, clearer separation of roles — such as planning, simulation, and critique — and more transparent and robust reasoning.
Within the experimental pipeline, these agents can analyze materials’ properties, physical data, chemical processes, and other relevant parameters. They could also collaborate with an agent that specializes in experimental design, refining iterative closed-loop experimentation, and real-time scientific discovery.
Although the research focuses on AI collaboration, the team notes that human researchers must retain accountability for the accuracy and integrity of both the experimental process and the results reported. This oversight begins with advocating for greater open access to research materials in all formats, building community-driven data repositories, and adopting standardization in how experimental parameters and metadata are reported. Equally important, researchers should be willing to report data from failed experiments as well as successful outcomes. Finally, organizations should work together to standardize secure APIs that enable shared, remote access to infrastructure across distances.
We see this as a step toward scientific instruments that do more than acquire data; systems that can reason over experiments, adapt measurements, and participate in the scientific discovery process alongside researchers. - Vida Jamali, assistant professor the School of Chemical and Biomolecular Engineering
The team is already developing these systems by connecting cloud-based, agentic infrastructures to microscopes at the Institute for Matter and Systems at Georgia Tech. With the addition of agentic AI, the goal is to accelerate discovery and engineering of new nanoscale materials for energy and quantum applications, as well as advance capabilities in cryo-electron microscopy and structural biology. These tools can optimize data collection, link real-time microscope observations with structural models of proteins, and dynamically adjust and prioritize experiments. The team sees this work as the first step toward the next generation of “thinking” electron microscopes, as well as an advancement in scientific discovery across domains.
- Christa M. Ernst
This research is supported by the Institute for Data Engineering and Science and the Institute for Matter and Systems
Original Publication
Jamali, V., Aghazadeh, A. & Kacher, J. Thinking microscopes: agentic AI and the future of electron microscopy. npj Computational Materials 12, 149 (2026). https://doi.org/10.1038/s41524-026-02077-y