large language models

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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Explore Cybersecurity at George Mason

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AI’s blind spots, one PhD student’s clear vision

Ryley McGinnis — Mon, 11 Aug 2025 18:36:42 +0000

AI’s blind spots, one PhD student’s clear vision Ryley McGinnis Mon, 08/11/2025 - 14:36

When artificial intelligence makes a mistake, the consequences can be annoying—or they can be life-altering. For one 91�� computer science PhD student, the stakes are clear: biased algorithms in health care aren’t just a technical flaw, they’re a human risk. And with internship experience teaching him crucial skills beyond the classroom, Fardin Ahsan Sakib is ready to make a difference in health care.

Sakib’s research in natural language processing (NLP) addresses concerns that many may have when working with emerging large language models (LLMs), but when it is applied to health decisions, there could be deeper negative risks. Tools like ChatGPT pull from vast amounts of information, but they have sometimes been known to “hallucinate,” or make up information.

“Sometimes these systems are taking a shortcut to get you an answer,” said Sakib. “But regardless of where it comes from, it can provide you incorrect information.” And in the health care space, these LLMs could aid doctors, but not when they could be driving improper care.

In electronic health records (EHRs), patients’ diagnoses, medical history, and demographic data are stored and organized. This information includes social determinants of health, like employment status, family situations, or housing conditions, hidden in records.

Fardin Ahsan Sakib is seeking to bring accurate and reliable large language models' capabilities to healthcare.

“These factors can affect up to 80% of health outcomes,” Sakib said. “So it's very important that we can extract them correctly from clinical notes and that the model is not introducing any bias.”

Sakib poses a scenario: A doctor is using an NLP tool to quickly assess a new patient’s health. The LLM is pulling from the patient’s records, and the physician asks the system, “Is this patient a smoker?” The system quickly sifts through the data, and says that yes, this patient is a smoker. The physician then proceeds to make care recommendations based on this information. Maybe they order a lung cancer screening, or they speak with the patient about smoking cessation resources.

But what if that answer was incorrect? The LLM decided to take a shortcut, because, based off the information it has, it discovered that most people with similar demographic data to the patient are indeed smokers, but this one isn’t.

“There are two things at play: bias and hallucination,” said Sakib. “First, algorithms can only use the information they have, and if that information is missing an entire racial or ethnic group, it can be biased. Second, these models can use this biased information to take shortcuts, leading to them delivering false information.”

Sakib isn't simply identifying these problems; he's developing solutions. His recent research on detecting and mitigating shortcut learning in health record processing was accepted for presentation at the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), one of the premier NLP conferences.

That desire to build reliable and trustworthy NLP tools has driven Sakib’s academic work and his professional pursuits.

At Brillient Corporation, where he interned last summer, Sakib worked on creating a retrieval augmented generation system that connected an LLM to the Food and Drug Administration’s (FDA) knowledge base to make accessing and retrieving information faster and grounded in facts. He and his colleagues submitted a patent for this effort.

This summer, he's deep into another high-impact internship at Amazon. “Amazon wants to automate support so that when you ask for help, a large language model can try to solve the problem before handing it off to a human,” he said.

Despite the different settings, he sees clear continuity between his internships and academic work. “The industry experience helps me in my research, and my research experience helps in industry. It goes both ways,” he said. “In academia, collaboration is usually focused within a lab or research group. In industry, I’ve worked with engineers, product managers, and domain experts all at once, spanning from health regulators to AWS cloud architects. That diversity of perspectives changes how you solve problems, and I’ve brought that mindset back into my research collaborations.”

It’s that dual perspective—academic precision paired with industry scale—that he plans to take with him into industry after graduation.

“George Mason has prepared me for a lot. From day one, all of the professors have helped me grow as a researcher, a person, and as a team member,” said Sakib.

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Long Nguyen and Kimmy Duong School of Computing

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Chaos theory finds its voice

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