Alan Zaoxing Liu on Building Reliable Systems for the AI Era

Alan Zaoxing Liu, an assistant professor of computer science at the University of Maryland, studies how computing systems manage and move data at scale. His work focuses on designing data systems that support large cloud platforms and artificial intelligence workloads, with an emphasis on reliability, performance and trustworthiness.

Liu received his Ph.D. in computer science from Johns Hopkins University in 2019 and completed postdoctoral research at Carnegie Mellon University’s CyLab. At UMD, he leads the FROOT lab, where students build faster, more reliable computing infrastructure.

In a recent Q&A, Liu discussed the experiences that led him to computer science, his current research and advice for students interested in the field.

Was there a defining moment that shaped your career path into computer science?

My interest in computer science started in high school, when I worked on a project with other students to build a long-range wireless antenna. At the time, Wi-Fi connections had limited range, and we wanted to see if we could extend connectivity between buildings on campus.

We used concepts from physics and computing to design and build the antenna ourselves. Once it was working, we were able to connect computers between two buildings and communicate over a network we had created. That experience showed me how computing systems and networking could solve real-world problems. It gave me early exposure to networking and sparked my interest in understanding how these systems work.

Can you tell me a little about your research focus?

My research focuses on building the systems and data pipelines that support cloud computing and artificial intelligence. AI systems rely on large amounts of data, and that data needs to be collected, transferred and processed efficiently across distributed infrastructure.

We design systems that collect and ingest data faster, move data efficiently between machines, and synchronize it across computing environments. The systems we build also collect and analyze event data to explain why certain behaviors or failures occur. This enables AI systems to better understand complex system dynamics and improve decision-making. These infrastructure challenges are important because they affect how efficiently AI systems and cloud applications operate.

I became interested in this area during college, when I began working on research projects in computer systems and networks. That experience helped me see how improving infrastructure can affect many applications, including cloud computing and distributed AI systems.

Can you tell me a little about your lab?

I lead the FROOT lab, which focuses on building computing systems that are faster, more reliable and trustworthy. The lab includes Ph.D. students, master’s students and undergraduate researchers who work on different aspects of systems and infrastructure.

Students in the lab work on projects related to cloud computing, networking, and systems for AI/ML. We try to align projects with each student’s interests while also contributing to broader infrastructure research goals. The lab provides students with opportunities to learn how large-scale systems operate and how infrastructure supports modern computing applications.

What are you currently working on, and what interests you most about these projects?

One area we are working on involves building data analytics infrastructure that can monitor, analyze, and explain activity across large computing environments. These systems collect data from applications, virtual machines, networks and storage, helping engineers understand system performance and identify issues. The key challenge lies in handling massive volumes of high-dimensional data while remaining responsive and cost-effective. 

Another area focuses on using artificial intelligence techniques to manage computing infrastructure more effectively. By analyzing system telemetry and operational data, these methods can automatically detect anomalies, optimize resource allocation, and improve overall efficiency in networks and cloud environments.

What interests me most about these projects is their connection to real-world computing systems. Infrastructure improvements can support many applications, including cloud services and AI systems, and help make those systems more efficient and reliable.

What are some challenges you’ve encountered in your research, and how have you approached them?

One challenge is keeping up with rapid changes in hardware technology. My research focuses on software systems, but those systems run on hardware that is constantly evolving. To design effective systems, we need to understand how new hardware works and how software can leverage those capabilities.

This requires continuously learning about new hardware developments and adapting system designs accordingly. It is an ongoing process that involves understanding both software and hardware and how they interact.

How does your work connect with or contribute to the broader computer science community and society?

The systems we develop provide infrastructure that supports many applications, including cloud computing, artificial intelligence and large-scale data analysis. Improving infrastructure reliability and efficiency can benefit many types of applications that rely on distributed computing systems.

Our goal is to design infrastructure that is reliable and trustworthy to support applications across domains, including scientific research and data-driven technologies. These improvements can help ensure that computing systems operate efficiently and reliably.

What inspired you to join UMD’s Department of Computer Science, and what have you enjoyed most so far?

One of the main reasons I joined UMD was the people. The department has strong faculty and students working across many areas of computer science, creating opportunities for collaboration and research.

I’ve especially enjoyed working with students. They bring curiosity and different perspectives, and mentoring them as they develop their research skills is an important part of my work.

If you could give one piece of advice to students interested in your area of research, what would it be?

My advice is to explore broadly and try different areas before deciding on a specific research focus. Computer science research often draws on ideas from multiple fields, and early exploration can help students identify areas that align with their interests and strengths.

Curiosity and persistence are important. Understanding both theoretical concepts and practical systems can help students build strong foundations in systems research and computing infrastructure.

—Story by Samuel Malede Zewdu, CS Communications