Yafan Huang Explores Efficient, Reliable Computing Systems

As scientific facilities and artificial intelligence systems process growing amounts of data, researchers must contend not only with computing speed but also with the cost of moving and storing information and the possibility of hardware errors affecting results.

Yafan Huang, who joined the University of Maryland’s Department of Computer Science as an assistant professor in fall 2026, studies those challenges through high-performance computing, or HPC.

“I am very excited to join the Department of Computer Science,” Huang said. “UMD has a strong tradition in high-performance computing, including longstanding connections with the U.S. Department of Energy’s national laboratories, which is particularly appealing given my research background.”

Huang said the department’s work across computer systems and artificial intelligence also creates opportunities to connect areas that increasingly rely on the same computing infrastructure.

“I am also excited about the department’s strengths in both systems and AI, as I believe bringing these communities together will create valuable opportunities for research on efficient and reliable AI infrastructure,” he said.

That intersection is central to Huang’s research. He describes his broader research direction as DEFT, short for Data-Efficient and Fault-Tolerant computing.

“My research focuses on HPC, with the goal of making computing systems more Data-Efficient and Fault-Tolerant, an overarching direction I call ‘DEFT,’” Huang said.

The data-efficiency side of his work examines ways to reduce the resources required to move, store and access information. Huang develops high-performance data compression techniques and methods for computing systems that combine different types of hardware.

His current projects include data-reduction methods for such systems and approaches to managing information produced by large scientific facilities. One focus is the Advanced Photon Source at Argonne National Laboratory, where researchers use powerful X-rays to study materials and biological systems.

Huang has worked closely with Argonne researchers since 2021. His collaborations have also included scientists in light source science and seismic imaging, connecting his systems research to fields that generate large amounts of experimental and computational data.

“By reducing these overheads, my research aims to help scientists process more data within existing computing resources, enabling faster analysis and more efficient use of large-scale computing facilities,” Huang said.

The fault-tolerance side of DEFT addresses a different systems problem. Hardware can occasionally introduce errors during computation, potentially affecting a result even when an application continues to run. Huang studies software-based methods to detect and mitigate those errors without changing the underlying hardware.

His current work examines how such errors propagate through scientific applications and large language model workloads. He said improving reliability becomes increasingly important as researchers depend on complex computational systems to interpret scientific data and run AI models.

Huang earned his Ph.D. in computer science from the University of Iowa in 2026. He received the 2025 ACM-IEEE CS George Michael Memorial HPC Fellowship and was named a 2026 MLCommons ML and Systems Rising Star. 

At UMD, Huang co-directs the Parallel Software and Systems Group (PSSG) with Professor Abhinav Bhatele. He plans to build on DEFT by bringing together HPC, computer systems and AI. His future research includes reducing memory use and data movement during AI inference, exploring compressed data representations during computation and studying how hardware errors affect modern AI workloads.

“I look forward to collaborating with colleagues and students across systems, AI, and computational science to develop computing techniques that can support both emerging AI applications and large-scale scientific discovery,” Huang said.

—Story by Samuel Malede Zewdu, CS Communications