Systems (Operating Systems, Networking, Storage, Databases)

Research in computer systems at The University of Arizona spans system design and development in providing computational needs of the emerging big data challenges from astronomy to healthcare. To provide future systems with needed capabilities and higher performance we are making advances in distributed systems (Cao, Zhang), energy efficiency (Zhang), networking (Zhang), intelligent data systems (Cao) and new hardwares (Cao).

Our research emphasizes developing innovative software systems. We're investigating and integrating both hardware and software mechanisms for overall improvement in system performance and energy efficiency, evolving from today's host-centric network architecture IP to a data-centric network architecture (NDN), designing new generation of cloud data systems, building intelligent data systems to support machine learning tasks such as anomaly detection, and extending the boundary of conventional data management systems to unstructured data. We are also applying novel solutions for medicine and civil engineering to provide improved communication and data reliability by advanced operating system and networking technologies.

Our collaborations involve colleagues in the College of Medicine, the College of Engineering, and the National Optical Astronomy Observatory (NOAO) located on campus.

2025

"Agree to Disagree: Robust Anomaly Detection with Noisy Labels" SIGMOD 2025
"Pluto: Sample Selection for Robust Anomaly Detection on Polluted Log Data" SIGMOD 2025

2024

"Combining Small Language Models and Large Language Models for Zero-Shot NL2SQL" VLDB 2024
"LakeBench: A Benchmark for Discovering Joinable and Unionable Tables in Data Lakes" VLDB 2024
"Outlier Summarization via Human Interpretable Rules" VLDB 2024
"MetaStore: Deep Learning Meta-Data Analytics at Scale" VLDB 2024
"RITA: Group Attention is All You Need for Timeseries Analytics" SIGMOD 2024
"MisDetect: Iterative Mislabel Detection using Early Loss" VLDB 2024

2023

"Extract-Transform-Load for Video Streams" VLDB2023
"Few-shot Text-to-SQL Translation using Structure and Content Prompt Learning" SIGMOD2023
"AutoOD: Automatic Outlier Detection" SIGMOD 2023

"MetaStore: Deep Learning Meta-Data Analytics at Scale" was nominated as a best paper candidate by VLDB 2024.

Amazon Research Award (Fall 2023): "SEED: Simple, Efficient, and Effective Data Management via Large Language Models"

NSF award: NSF proposal "An Automated High-Content Imaging Platform for Caenorhabditis elegans"

Systems Faculty

Lei Cao

Assistant Professor
Office: GS 712
Interests: Cloud Data System; Systems for ML; ML for Data Systems
(Ph.D., Worcester Polytechnic Institute)

caolei@arizona.edu

Beichuan Zhang

Associate Department Head and Professor
Office: GS 723
Interests: Computer networks, Internet routing architecture and protocols, Internet topology, multicast.
(Ph.D. University of California at Los Angeles, 2003)

520-621-4817
bzhang@cs.arizona.edu