Scientific careers are rarely linear, and Dr. Anaiya Reliford’s journey is proof that some of the most innovative research happens at the intersection of disciplines. As a Senior Research Scientist at the Research Institute for Tactical Autonomy (RITA), Dr. Reliford draws on a background spanning chemical engineering, atmospheric sciences, artificial intelligence, and autonomous systems to advance research in air and space autonomy. Her work supports RITA’s mission of developing trustworthy tactical autonomy while addressing some of the most complex challenges facing national security. We spoke with Dr. Reliford about her career path, the future of AI, and why persistence is one of the most important qualities for aspiring researchers.
Your career has taken you from chemical engineering and atmospheric sciences to artificial intelligence and tactical autonomy. What inspired that journey, and how have those experiences shaped the way you approach research today?
Growing up in California, I experienced the impacts of wildfires, droughts, and other environmental challenges firsthand. I also saw how those conditions affected different communities in different ways. Those experiences inspired me to pursue engineering and science so I could better understand those problems and use technology to help address them.
My bachelor’s and master’s degrees are in chemical engineering, and my Ph.D. is in atmospheric sciences, but today I work in tactical autonomy. One of the biggest lessons I’ve learned is that engineers develop transferable skills. The analytical thinking and problem-solving abilities you gain in one discipline can be applied across many others. Those experiences have given me a systems engineering perspective and a multidisciplinary lens for tackling complex technical challenges.
What research areas are you currently focused on at RITA?
My current research focuses on air and space autonomy. I’m interested in defining architectures that are critical to national security while leveraging advances in artificial intelligence and machine learning to improve autonomous capabilities.
How does your work contribute to RITA’s mission?
At RITA, we focus on trust in mission autonomy, collaboration between platforms, and human-machine teaming.
As a Senior Research Scientist focused on air and space autonomy, I work to ensure our autonomous systems are trained in ways that are both robust and ethical. That helps ensure the solutions we develop align with RITA’s core research pillars while maintaining integrity throughout the design process.
How would you explain the practical role of AI and machine learning in tactical autonomy?
AI and machine learning help us solve problems more efficiently, but they aren’t magic solutions. We still face significant resource constraints that increase the complexity of the challenges we’re trying to solve.
AI accelerates our ability to develop solutions, but there are still many technical hurdles that researchers must overcome before these systems reach their full potential.
You have extensive experience with small uncrewed aerial systems. Why are these platforms so valuable for research and mission applications?
Small uncrewed aerial systems, or drones, have become increasingly important across both national security and commercial applications. We see them making headlines because they’re changing how operations are conducted.
The ability to successfully navigate and operate in different environments provides a significant strategic advantage. As autonomous capabilities continue to advance, controlling the air domain will become increasingly important.
How does your background in atmospheric sciences influence your work in autonomy?
Environmental conditions have a tremendous impact on how autonomous systems perform, particularly in air and space domains.
If you want an autonomous system to operate reliably, you have to train and evaluate it across many different environments. If a system is only developed under ideal conditions, you can’t be confident it will perform when conditions become more challenging. Environmental factors are inevitable and should always be part of the design process.
Research often involves setbacks. What lessons have you learned throughout your career?
Research is an iterative process, and you’re going to fail more often than you succeed. That’s part of innovation.
I believe researchers should aim to fail fast and fail often because every setback helps you develop a stronger solution more quickly. If you’re never failing, you’re probably not pushing the boundaries enough to remain competitive.
What emerging technologies do you believe will have the greatest impact on autonomy in the future?
Technology is advancing so rapidly that it’s difficult to predict what the landscape will look like five or ten years from now.
I often tell people that we need to shorten our timelines because innovation is happening faster than we’ve ever seen before. Rather than trying to predict a decade into the future, we should focus on adapting to the rapid pace of change happening today.
If you could solve one challenge in autonomy tomorrow, what would it be?
Unlimited computational resources.
We have incredibly talented researchers and strong technical ideas, but we’re often limited by compute power. Greater computational resources would allow us to develop, train, and validate much more advanced autonomous systems while accelerating research across the field.
What advice would you give students or early-career researchers interested in AI, sensing, and autonomy?
Be persistent.
Engineering and science are challenging fields, especially as technology continues to evolve so quickly. There will be setbacks and difficult moments, but reaching a solution after working through those challenges is incredibly rewarding. That sense of accomplishment makes the journey worthwhile.
