At first glance, it looks almost like choreography: a cluster of drones weaving through the air, adjusting to one another in real time, never colliding, never losing sight of a shared goal. Each one makes its own decisions, yet all move together communicating, adapting, and learning as they go. This is the kind of system Shana Moothedath is working to make possible.
An assistant professor of electrical and computer engineering, Moothedath develops machine learning–enabled control algorithms, the invisible intelligence that allows autonomous systems to act safely and collaboratively in uncertain environments. Her work focuses on how multiple “agents” can coordinate their actions, even when conditions are constantly changing, and information is incomplete.
“We want these systems to make decisions in real time,” she explains. “They’re operating in dynamic environments, so they need to keep learning and adapting as they go.”
Moothedath’s path to this work began far from Iowa, where she discovered her passion for research almost by accident. As a master’s student, she had the opportunity to work with India’s space agency, ISRO, the country’s equivalent of NASA. There, she tackled a problem rooted in both physics and precision: how to optimize a rocket’s trajectory so that its components fall at exactly the right locations during descent.
The work was technically demanding, but what captivated her most was the process itself. Because of security restrictions, she couldn’t simply download articles or work remotely. Instead, she spent long days inside a tightly secured library, in the stacks, searching for documents and insights that might unlock the solution.
“It felt like an adventure,” she says. “You don’t know the answer, and you’re trying to find it—that’s what really excited me.”
The same sense of exploration still shapes how she approaches problems today. Her work doesn’t end at her desk. Moothedath often takes long walks along Ames trails to replay problems in her mind and shake loose new ideas.
“Sometimes I find the issues when I’m not looking directly at the problem,” she says.
That curiosity carried her through a Ph.D. at the Indian Institute of Technology Bombay and a postdoctoral appointment at the University of Washington. Along the way, her focus evolved from traditional control systems where engineers assume they know a system in advance to systems designed for the uncertainty of real-world environments.
Today, her research sits at the intersection of control theory and machine learning. She develops algorithms that enable systems not only to act but also to learn from data, while maintaining strict safety and reliability standards.
In a coordinated drone system, for example, each unit must keep a safe distance, communicate under limited bandwidth, and adapt if conditions shift or a teammate fails. Moothedath’s work addresses these challenges.
A key part of her research explores how systems can learn collaboratively without sharing sensitive data. In this approach, known as federated learning, each agent keeps its data private while contributing to a shared model, allowing the group to learn faster without compromising security. This can be a useful tool in collaborative efforts among different companies or countries.
“It’s like everyone learns together,” she says, “but no one has to reveal their private information.”
While much of her work is rooted in theory and computer simulation, Moothedath is now building toward implementation. She is developing a robotics testbed at Iowa State, where algorithms can move from models to physical systems, bridging the gap between abstract mathematics and practical impact.
In the classroom, she teaches courses in control theory, machine learning, and reinforcement learning, preparing students to work in an increasingly autonomous world. She also involves undergraduates in her research, giving them hands-on experience with these challenges at the forefront of engineering.
Her proudest accomplishment so far is the success of her first Ph.D. student, who joined her lab when she arrived at Iowa State in 2021 and has since launched an academic career.
“It’s very rewarding,” she says. “You grow together.”