Nathan Kahl

National Institutes of Health

NSF CAREER award allows researcher to look under AI’s hood

Nathan Kahl — Mon, 20 Jul 2026 16:30:48 +0000

NSF CAREER award allows researcher to look under AI’s hood Nathan Kahl Mon, 07/20/2026 - 12:30

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Ziyu Yao

Artificial intelligence (AI) systems are now part of everyday life, writing software, tidying up your shopping list, and answering questions. While we know they sit on top of large language models (LLMs), we still understand surprisingly little about what happens inside these systems or what is helping them generate responses.

Ziyu Yao. Photo provided

91�� Department of Computer Science Assistant Professor Ziyu Yao received a prestigious National Science Foundation (NSF) CAREER award for $674,100 over five years to peel the layers back on AI. Specifically, Yao will develop new methods for understanding the internal mechanisms of LLMs. Her research aims to make these AI systems more transparent and useful.

"We use language model–powered products every day, but we have very limited understanding of how they actually work," Yao said. Most efforts to evaluate AI systems focus on their behavior. Researchers give a model a prompt and judge the quality of its response. Yao's work instead focuses on mechanistic interpretability, an emerging field that examines what happens inside a language model as it processes information.

"If we send something to the model, we want to see how it extracts features or signals from my input and gradually performs computations before producing an answer," she said.

As language models have grown dramatically in size, that task is increasingly difficult. Today's leading models contain potentially hundreds of billions of parameters. And Yao said, "The interpretability research community has not caught up. Many traditional approaches cannot be efficiently applied to today's language models."

The project focuses specifically on AI-powered code generation, in which users describe a programming task in natural language and an AI system generates software code. Code generation is an increasingly important application of LLMs, with uses including software engineering and scientific research.

Yao's team will investigate how LLMs apply programming syntax, recall semantic knowledge, and perform deliberate reasoning while writing code. The researchers will also study how training data and learning methods influence those internal mechanisms and explore how interpretability insights can be used to improve AI coding systems.

Current interpretability studies often require researchers to manually design experiments, collecting internal model signals and analyzing the results. Yao plans to develop a framework that automates much of that process using AI itself, allowing researchers to study LLMs more efficiently.

At the same time, she wants interpretability research to produce practical benefits rather than simply satisfying scientific curiosity.

"We want to build the bridge between what we discover about language models and how that knowledge can improve them," Yao said. "Can interpretability actually help us build better AI systems?"

Although commercial AI systems such as ChatGPT are proprietary, Yao and colleagues primarily study open-weight language models that are downloadable. These models are becoming increasingly competitive with commercial systems while supporting the transparency needed for scientific research.

An expectation with the award and a hallmark of the CAREER program is a plan for broader impact. Yao hopes to expand AI literacy through outreach programs, create new curricula and open-source educational resources, provide undergraduate research opportunities, and foster collaborations between researchers in artificial intelligence and software engineering.

"I think we should encourage the younger generation to embrace AI," Yao said. "But at the same time, they should be skeptical, think critically, and be careful about what they are reading from AI."

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George Mason researcher develops an affordable helping hand for stroke recovery

Nathan Kahl — Tue, 14 Jul 2026 19:25:34 +0000

George Mason researcher develops an affordable helping hand for stroke recovery Nathan Kahl Tue, 07/14/2026 - 15:25

For millions of stroke survivors, something as simple as picking up a glass of water or holding a sandwich is a daily challenge. Quentin Sanders wants to make those moments easier through wearable robotic technology designed to restore hand function.

The 91�� assistant professor is developing a new generation of hand exoskeletons that may help people regain independence after stroke. His lab is testing wearable devices that amplify a user's remaining muscle activity, allowing them to open and close their hand more effectively while remaining affordable enough for broader use.

Sanders' prior work focused on lower-limb prosthetics. Photo by Ron Aria/Office of University Branding

The latest prototype resembles a lightweight glove that fits over the hand and upper arm. As users attempt to move their hand, the device detects their remaining muscle activity and assists the motion.

"We instruct them to try to open their hand," Sanders explained. "When they try to open their hand, we sense whatever residual activity they have, and then the exoskeleton amplifies that."

One version of the device seeks to use wearable ultrasound technology developed in collaboration with George Mason researcher Siddhartha Sikdar, a professor in the Department of Bioengineering and director of the Center for Medtech Innovation. The system will use wearable ultrasound sensors to monitor how muscles deform as they contract, translating those signals into movement. Another simpler version uses a button embedded in the glove that users press to control the device.

While brain-controlled devices remain the long-term aspiration for many in the field, Sanders is focused on a more practical solution that builds on the muscle activity people retain after a stroke. "I would say brain-controlled systems is kind of the holy grail. In my lab, we're going one level lower to see if we can use muscle activity to control it."

The need for better rehabilitation tools is growing. Sanders noted that roughly 800,000 people experience a stroke each year in the United States, while millions more live with its long-term effects. Advances in medicine mean more people survive strokes, but many struggle to access the lengthy rehabilitation needed to regain function.

"We're at this point where you're living a long life, but you have this need for rehab," Sanders said. "You need either consistent opportunities for movement or some type of device that can help improve your quality of life."

Commercial hand exoskeletons remain scarce in the United States, and many existing systems cost tens of thousands of dollars. Making the technology more affordable and accessible has become one of the driving goals of his research.

"You've got this large population of people who need these devices who aren't getting them," Sanders said. "I'm hoping we can make this kind of low-cost, accessible version that people can use to improve some aspect of their life."

Sanders is also working to broaden who can benefit from rehabilitation technologies. Many research studies enroll only patients who meet narrow eligibility criteria, leaving others without solutions tailored to their needs.

"I think that's the other breakthrough people are working on that my lab is working on," he said. "How can we make these devices work for a larger population?"

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Quentin Sanders

Siddhartha Sikdar

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Electrical and Computer Engineering

GCI-Health

George Mason researcher is using CAREER award to build brain-inspired AI that is more adaptive

Nathan Kahl — Tue, 07 Jul 2026 18:49:00 +0000

George Mason researcher is using CAREER award to build brain-inspired AI that is more adaptive Nathan Kahl Tue, 07/07/2026 - 14:49

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Maryam Parsa

Though artificial intelligence (AI) is making extraordinary advances, today's AI systems still have significant limitations. They often require enormous computing power and energy (not to mention well-documented water usage), struggle to adapt when conditions change, and can make decisions in ways that are difficult for humans to interpret, validate, or trust.

Maryam Parsa. Photo provided

Maryam Parsa, assistant professor in 91��'s Department of Electrical and Computer Engineering, believes those shortcomings point toward a compelling source of inspiration: the human brain.

Parsa received a five-year, $655,000 National Science Foundation Early Career Development (CAREER) Award for developing a new framework for brain-inspired AI. The project seeks to make AI systems more efficient, adaptable, resilient, and transparent by borrowing principles from biological nervous systems.

"AI was originally inspired by the brain," Parsa said. "Concepts such as neurons, synapses, and neural networks drew inspiration from how brains process information. But over time, AI has moved farther away from those biological principles.”

Parsa believes researchers now have an opportunity to reconnect AI and neuroscience. “Neuroscience advanced tremendously over the past few decades, with only a small fraction of those discoveries finding their way into AI systems.”

As computing power grew, researchers found AI systems could make major gains by scaling neural networks and training them on massive datasets. The approach, fueled by increasingly powerful computers, is leading to significant breakthroughs, particularly with the rise of large language models (LLMs).

"They are producing remarkable devices," she said. "The progress we see in LLMs and modern AI grew out of relatively simple neural-network ideas. Over time, researchers added more layers, more data, more computational power, and more sophisticated training methods, leading to powerful systems.”

The goal is not to replace today's successful AI systems with direct copies of the brain. Instead, her research focuses on areas where current AI falls short. "We know some of the key challenges AI still faces," she said. "It can be difficult to understand how these systems reach their decisions, and they do not always transfer well when conditions, data, or environments change. That is where biological intelligence can offer useful design principles.”

When today’s AI systems encounter unfamiliar environments or changing conditions, they often need to be retrained or fine-tuned. Biological intelligence, however, is remarkably adaptable and energy efficient, offering clues to how future AI systems could learn and respond more flexibly.

Her CAREER project builds a unified framework around three characteristics of biological intelligence. The first pillar, dimensionality, examines how the brain creates meaningful representations of the world rather than simply processing raw information. The second, heterogeneity, explores the brain's remarkable diversity. Rather than relying on identical neurons performing tasks, biological brains contain many neuron types with distinct behaviors. "The brain is not copy, paste, copy, paste, copy, paste,” she said.

The third pillar, nonlinearity, draws inspiration from the retina, which performs extensive processing before visual information reaches the brain. "It's not like the raw pixels go directly to our central brain," Parsa said. "The retina digests and processes the information first."

Parsa said the broader idea is combining these principles into a more biologically inspired approach to AI. “If I know how to process my input, create a better representation of my environment, and leverage this massive heterogeneity, then I can move AI a little closer to the way biological intelligence works,” she said.

Beyond advancing AI research, the project includes an educational effort to help students see how ideas from the brain can shape the future of computing and artificial intelligence. Parsa plans to bring these concepts into graduate courses, hands-on hackathons, and STEM outreach activities for K-12 students, creating new pathways for students to explore the intersection of neuroscience, engineering, and AI.

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large language models

Electrical and Computer Engineering Faculty

C-TASC

A CAREER in robot learning

Nathan Kahl — Mon, 06 Jul 2026 15:15:43 +0000

A CAREER in robot learning Nathan Kahl Mon, 07/06/2026 - 11:15

What if a robot could learn from another robot's experience instead of starting from scratch every time it encountered a new task? That question is at the center of new research by 91�� assistant professor Xuan Wang, who received a five-year, $640,000 Faculty Early Career Development (CAREER) award from the National Science Foundation (NSF).

Xuan Wang. Photo provided

The CAREER award, NSF's most prestigious honor for early-career faculty, will support Wang's efforts to develop new methods that allow robots to transfer knowledge more efficiently, reliably, and safely across different tasks, environments, and even different types of robots.

Today's learning-based robots often require enormous amounts of data and repeated trial-and-error to master new tasks. Wang's research aims to dramatically reduce that burden by helping robots determine what information from previous experiences is actually useful and how to apply it effectively.

"Robot learning is very time-consuming and computationally expensive," said Wang. "The idea is to leverage knowledge that another robot has already gained instead of learning everything from the beginning."

The project combines Wang's background in control theory, optimization, and multi-agent systems with modern machine learning techniques. Rather than treating knowledge transfer as a collection of trial-and-error, Wang will develop a mathematical framework allowing robots to determine when transferred knowledge will improve performance and when it should be ignored.

"We want robots to reason about when transfer learning is beneficial and when it is not," he said. "If there are many other robots to learn from, how can a robot quickly identify which one will provide the most useful knowledge?"

Consider a wheeled robot exploring a building and learning where rescue targets are located but being unable to access them because it can’t go up stairs. When a legged robot enters the same building, it can “learn” from the first and apply its own abilities to climb steps and find the target. Wang's research identifies what information transfers successfully despite differences in hardware.

A legged rescue robot could move through a building after learning from another robot where a rescue target is located. Photo provided.

Potential applications include teams of aerial and ground robots working together during disaster response, sharing information to search damaged buildings more efficiently. Agricultural robots could reuse knowledge gathered by other machines instead of repeatedly mapping fields from scratch. Security robots could improve patrol routes by learning from previous deployments.

Regarding his research progression, Wang said, "I saw an opportunity to combine my background in optimization and networked systems with modern robot learning. That became the foundation for this CAREER project."

The award also supports education and outreach activities. Wang will continue expanding RoboArt, a program that introduces elementary school students to robotics by inviting them to draw their own robot designs. Undergraduate researchers then use artificial intelligence tools to convert drawings into 3D-printable models before giving the finished creations to the students.

The project also will support graduate student training, undergraduate research experiences, and new educational materials that connect theoretical concepts with hands-on robotic systems.

"This project is about making robot learning more efficient, more reliable, and more explainable," Wang said. "If robots can learn from one another in a principled way, they'll be able to solve new problems much more quickly and safely."

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Xuan Wang

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Department of Cyber Security Engineering faculty

Your watch knows what you did last summer

Nathan Kahl — Mon, 15 Jun 2026 17:36:08 +0000

Your watch knows what you did last summer Nathan Kahl Mon, 06/15/2026 - 13:36

A smartwatch sitting quietly on someone’s wrist may seem unremarkable. But in a criminal investigation, that device could become a witness.

Umit Karabiyik. Photo provided

91�� Cyber Security Engineering Associate Professor Umit Karabiyik helps law enforcement agencies across the country understand how data from everyday connected devices can provide critical evidence in criminal cases. Through a U.S. Department of Justice (DOJ)–funded project, Karabiyik and researchers trained thousands of criminal justice professionals and provided technical assistance on cases involving everything from smartwatches and fitness trackers to smart home devices and medical technologies.

“We try to understand this Internet of Things (IoT) ecosystem and extract data from these technologies for evidentiary purposes in courts, criminal investigations, and criminal litigation,” said Karabiyik.

The project, funded by the Bureau of Justice Assistance, was first awarded in 2021 while Karabiyik was at Purdue University. Originally planned as a short-term effort, the grant was extended multiple times, allowing the team to continue responding to a growing challenge: helping investigators keep pace with the expansion of connected technologies in everyday life.

IoT devices include smartwatches, fitness trackers, smart appliances, home security systems, smart glasses, medical monitoring devices, and countless other products that collect and exchange data. “Law enforcement agencies need to understand what these technologies are, what the security issues are, what the privacy issues are, and most importantly, how to investigate them,” Karabiyik said.

Geolocation data points generated by an iPhone alongside data from nearby WiFi access points. Despite iPhone location services being disabled, information from surrounding devices is still collected. Image provided.

That work often goes beyond research papers and conference presentations. Since the project began, Karabiyik’s team has trained more than 7,000 criminal justice professionals through conferences, webinars, and specialized workshops. They have also become a resource for investigators facing technical questions in active cases.

“We get phone calls from law enforcement agencies that say, ‘I’ve got this case, and I got stuck. What do I do?’” Karabiyik said.

One line of research focused on how devices within Apple’s ecosystem communicate with one another. Karabiyik’s team discovered that Apple devices can collect identifying information about nearby Apple products even without direct interaction from users. The findings created new possibilities for investigators attempting to establish whether individuals or devices had been in proximity to one another.

After presenting the research, Karabiyik received inquiries from investigators working homicide and cold cases who wanted to know whether data from nearby Apple devices could help narrow the list of potential suspects. “These kinds of calls keep coming,” he said.

As part of the project, Karabiyik and colleagues serve as a criminal IoT helpdesk, of sorts.

In one case, Karabiyik served as an expert witness in a murder trial where investigators examined data from a smartwatch worn by the defendant, who maintained she had been asleep during the time prosecutors alleged the crime occurred.

“We looked at the technology and proved that she was indeed sleeping,” Karabiyik said. “There were no steps taken, and her stress level was perfectly fine until she found the victim dead.” The defendant was ultimately found not guilty.

While much of the project focuses on digital forensics, Karabiyik’s research also examines the privacy and security risks associated with connected devices. His team has built databases of vulnerabilities and studied how data collected by consumer technologies is stored, shared, and potentially exposed.

He noted that his research influences his own personal use. “I’m very picky about what technologies I use,” he said. He avoids devices with microphones and cameras whenever possible, does not use voice assistants such as Alexa or Google Home, and is cautious about products that collect large amounts of personal data.

At the same time, he sees enormous value in connected technologies when they are used responsibly. “Unless you are a bad person, these technologies can be super helpful,” he said. “If something happens to me, I know these devices are going to tell law enforcement a lot of things.”

As connected devices continue to multiply, Karabiyik expects demand for digital forensic expertise to grow as well. The current project concludes later this year, but he is already pursuing new DOJ funding opportunities aimed at helping forensic examiners, law enforcement agencies, and criminal justice professionals navigate an increasingly connected world.

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Cybersecurity Research

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Department of Justice

information sciences and technology

NSF CAREER award will support teen autonomy in age of AI

Nathan Kahl — Tue, 09 Jun 2026 15:45:38 +0000

NSF CAREER award will support teen autonomy in age of AI Nathan Kahl Tue, 06/09/2026 - 11:45

Adolescence has always been a time of figuring out who you are. Increasingly, that process unfolds alongside social media feeds, recommendation algorithms, and AI chatbots. 91��’s Nora McDonald received a prestigious National Science Foundation (NSF) CAREER award for $748,000 to better understand how adolescents experience and are shaped by these artificial intelligence (AI) and other personalized digital environments, and how they can develop the skills and capacities necessary to maintain their sense of self, autonomy, and agency within them.

McDonald, an assistant professor in the Department of Information Sciences and Technology, has spent several years studying a growing reality of adolescent life, and one increasingly shaped by personalized AI technologies. Her research has found that social media recommendation systems and AI chatbots can serve as spaces where young people reflect on themselves and seek feedback about who they are.

McDonald's work will give teens tools to properly engage with digital technologies. iStock photo.

“Much of the public conversation has been about how algorithms influence what kids see and how we go down rabbit holes,” McDonald said. “I’m interested in a different question: How do algorithms shape self-understanding and how do teens make sense of themselves in environments that are increasingly personalized and shaped by algorithms and AI?”

McDonald’s previous research suggests that even while teenagers often view AI chatbots and recommendation systems as practical tools, those systems can become important spaces for self-exploration and self-presentation.

“Over time, teens might come to expect these systems to reflect aspects of who they are,” McDonald said. “They use them not just for help with homework or entertainment, but also to explore and understand themselves.”

Those findings raise broader questions about adolescent development. If recommendation systems continually influence what young people see, what feedback they receive, and what content captures their attention, how will that affect how they see themselves?

Nora McDonalds. Photo provided

McDonalds's CAREER project will investigate that question through what she calls a resilience framework that examines how adolescents recognize, interpret, and respond to algorithmic influence and how it relates to the coherence of their sense of self.

The research combines interviews, diary studies, surveys, and participatory design activities to better understand how adolescents interpret and respond to algorithmic systems. McDonald will recruit teenagers from across the country, beginning with qualitative studies and eventually expanding to a national survey designed to generate broader statistical insights.

One major outcome of the project will be the development of practical resources and activities designed with teenagers to help them navigate personalized digital environments.

The effort differs from traditional digital literacy initiatives that focus primarily on explaining how algorithms work. “It helps them reflect on algorithmic influence and develop strategies that are really rooted in managing attention and maintaining perspective on who they are outside of digital experiences,” McDonald said.

The CAREER award also supports substantial educational activities. McDonald plans to develop a new undergraduate course focused on algorithmic self-defense from a human-computer interaction perspective. The project will also support a youth summer institute for high school students helping young people to critically engage with AI personalized digital technologies that increasingly shape their everyday experiences.

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George Mason PhD student recognized for blockchain privacy research

Nathan Kahl — Tue, 02 Jun 2026 10:51:19 +0000

George Mason PhD student recognized for blockchain privacy research Nathan Kahl Tue, 06/02/2026 - 06:51

91�� computer science doctoral student Aayush Yadav co-authored a paper that received a Distinguished Paper Award from the IEEE Symposium on Security and Privacy, one of the industry’s premier conferences in the security field. The conference typically accepts around 13 percent of submissions.

Aayush Yadav. Photo provided

The paper addresses a growing challenge in blockchain technology—how to protect sensitive transaction information before it becomes public, focusing on improving privacy protections within blockchain systems while maintaining the transparency and decentralized structure that define the technology.

His research grew out of an internship last summer with Category Labs, a company developing blockchain tools and infrastructure. The work addresses a vulnerability tied to how blockchain transactions are processed before they are officially added to the chain. Traditionally, blockchain transactions are publicly visible before they are finalized. That visibility allows entities responsible for processing transactions to potentially manipulate their order for financial gain.

“These entities can act maliciously in some way,” Yadav said. “Because this information is public, these actors know the direction of the order of the transactions before the rest of the public. And they can take advantage of that.” Consider that if an actor sees that someone is about to make a major purchase or sale, they can quickly place their own trade first, causing the market price to change. The original user then receives a worse price than expected, while the actor profits from the price movement, effectively creating a hidden cost for the user.

The team’s research focuses on encrypting transactions while they are waiting to be processed, helping prevent that kind of exploitation. The goal is to encrypt or hide transactions as they are sent, revealing them later.

The paper introduces new approaches to what researchers call “mempool privacy,” referring to the protection of transaction data before it becomes part of the public blockchain ledger. The work also explores “threshold encryption,” which requires multiple parties to work together to decrypt transaction data, helping eliminate single points of trust or failure.

Yadav, who is completing his PhD in computer science this year, said his interest in theoretical computer science and algorithms started early in his academic career. “I enjoyed the process of solving problems that were algorithmic in nature,” he said. “Thinking about these abstract things, but then also being able to solve them concretely using an algorithm, is very satisfying.”

He chose George Mason in part because of the university’s large computer science faculty and breadth of expertise. After arriving at George Mason in 2021, Yadav took his first cryptography course with Foteini Baldimtsi, an experience that shaped the direction of his research. “It had the right mix of theory and practice. The problems are very theoretical in some sense, and the way you solve them is theoretical, but at the same time they are grounded in real applications.”

“I am very proud of Aayush,” said Baldimtsi. “Throughout his PhD, he has worked on some of the most important challenges in modern cryptography, including signature protocols and privacy-preserving authentication for digital identities, while also focusing on post-quantum security. The award is a well-deserved recognition of his talent, dedication, and impact on the field.”

Later this year he will start a tenured-track faculty position at his alma mater, Rutgers University–Camden.

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Foteini Baldimtsi

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Chaos theory finds its voice

Nathan Kahl — Tue, 19 May 2026 15:42:55 +0000

Chaos theory finds its voice Nathan Kahl Tue, 05/19/2026 - 11:42

Talk may be cheap, but it is still rich with data, and George Mason's Anomadarshi Barua is using artificial intelligence (AI) and a new approach to chaos theory to rebuild missing pieces of speech with incredible accuracy.

"When we talk, our voices have a frequency component from zero kilohertz (kHz) to around 8 kHz," said Barua, an assistant professor in the Department of Cyber Security Engineering. "Capturing all those frequencies normally requires devices to collect and process large amounts of data, which consumes power, storage, and computing resources."

He will present a paper he coauthored on this topic at the Association for Computational Linguistics (ACL) annual meeting later this summer. ACL is one of the world's leading conferences in natural language processing and speech technologies, and the paper was accepted in the top 15 percent of accepted submissions at a conference with a 19 percent acceptance rate overall.

Smaller devices and sensors operating with limited battery life or bandwidth have difficulty capturing an entire signal, and so record a smaller slice, attempting to reconstruct the missing frequencies later. Barua says this is known as bandwidth extension or bandwidth reconstruction.

Imagine listening to a song through a wall and trying to mentally fill in the muffled higher notes. Barua's research teaches AI systems to do something similar, but with far greater mathematical precision. The team's major breakthrough came from incorporating a concept not often associated with speech: chaos theory.

"Our speech is actually a chaotic signal," Barua said. "'Chaos' does not mean completely random."

Instead, he describes speech as "deterministically random," meaning sounds and phonemes are strongly connected to one another in predictable ways. "You can actually determine what could be the next phoneme if you have the sufficient information from the previous phoneme," he said.

To capture those hidden relationships, the researchers developed what Barua calls a "chaotic discriminator" inside a machine-learning framework known as a generative adversarial network, or GAN. In simple terms, one part of the AI system generates reconstructed speech while another checks whether the recreated speech preserves the natural chaotic patterns found in human voices.

The approach significantly improved reconstruction quality compared to previous methods. "We are getting more improved results compared to the previous baseline," Barua said, "because of the incorporation of the chaotic properties."

By integrating chaos-informed modeling, the team dramatically reduced the size of the AI system needed for reconstruction. "We actually reduce the size of the discriminator by 14 times," Barua said. Smaller models require less memory and computing power, making them more practical for real-world devices.

For Barua, whose earlier work focused primarily on cybersecurity before expanding into speech and natural language processing, the ACL conference's acceptance marks an important milestone. "This is my first paper in natural language processing," he said.

While the current paper focuses on speech, Barua sees much broader possibilities ahead. The same reconstruction techniques could eventually be applied to electrical signals, sonar, lidar, and other sensing technologies. "This concept is not only limited to speech," he said. "We are trying to open up a larger branch in different signal modality."

And that message comes through loud and clear.

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Anomadarshi Barua

Explore Cybersecurity at George Mason

Powering Artificial Intelligence

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Disrupting disaster response: Systems engineering students get top spot in Patriot Pitch

Nathan Kahl — Thu, 07 May 2026 16:44:16 +0000

Disrupting disaster response: Systems engineering students get top spot in Patriot Pitch Nathan Kahl Thu, 05/07/2026 - 12:44

What began as a routine college course quickly turned into an innovation with real‑world impact for a team of systems engineering students, winning first place in the George Mason Patriot Pitch Social Impact Track along with a People’s Choice Award. The team also captured first place in the U.S. Military Academy System Engineering Design Competition.

“One minute we were playing video games in our dorm room. The next we were in the system engineering capstone class designing ways to deliver humanitarian aid to citizens in distress,” said Tim Shipman, a senior system engineering major. “Systems engineering is mind‑blowing.”

The student team set out to address a growing global challenge. According to team lead Hocine Filali, the demand for humanitarian aid is increasing. “Over the last 10 years, an average of 380 natural disasters per year required humanitarian aid delivery,” he said.

One major bottleneck in disaster response is transporting aid from cargo ships—often anchored up to five miles offshore—to temporary distribution warehouses on land. Traditionally, aid is transferred using helicopters, ferry boats, or trucks traveling across floating causeways.

To evaluate these options, the team developed a stochastic simulation model comparing three delivery methods for transporting 10,000 aid pallets over a 90‑day period. “Ferry boats are the least expensive option, but they’re also very slow,” said team member Alan Garcia. “Floating causeways are costly and take too long to install. Helicopters can deliver aid quickly, but they cost about twice as much as ferries.”

Beyond technical modeling, the students conducted interviews with a wide range of stakeholders, including representatives from nongovernmental organizations, aid workers, and military personnel involved in humanitarian missions.

“The ecosystem is incredibly complex,” said team member Nesma Khalafalla. “Economics, regulations, and politics often create perverse incentives. One thing became clear very quickly: if aid could move from ship to shore as fast as helicopters but at a lower cost, it would fundamentally improve the system.”

Exploring alternatives, the team examined the use of remotely piloted, heavy‑lift electric drones, an emerging technology capable of matching helicopter speeds at significantly lower operating costs. However, the drones presented a critical limitation. “The problem is battery recharge time,” explained team member Amr Hamza. “It slows the entire operation.”

A depiction of the Automated Floating Power Line System. Image provided.

The breakthrough came when the team discovered a catenary power system—an overhead power system similar to those used by city trams. In this approach, drones draw continuous power from an overhead electric line through what’s known as a pantograph, eliminating the need for battery recharging.

Designing that power infrastructure posed the next challenge. “We needed a power line that could stretch five miles from ship-to-shore,” said Shipman. “Our solution was a series of floating utility poles equipped with propellers that allow them to motor into position and hold steady against ocean currents. Detailed simulation models showed the concept is feasible.” The result was the Automated Floating Power Line System (AFPLS), a novel approach to ship‑to‑shore humanitarian aid delivery.

“The comprehensive nature of this design really showcases the power of system engineering,” said Guiping Hu, chair of the Systems Engineering and Operations Research Department. “We are extremely proud of our students and their ability to tackle complex, real‑world problems.”

“I highly recommend this course to any undergraduates who really want to learn how to develop innovative solutions to the world’s toughest problems,” said Filali.

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