conf.researchr.org / Minghua Ma
Registered user since Mon 8 Aug 2022
Name:Minghua Ma
Bio:
Minghua Ma is a Senior Researcher at Microsoft M365 Research. His work focuses on AIOps: building AI systems that autonomously detect, diagnose, and resolve failures in large-scale cloud infrastructure, improving the reliability of services used by millions. He received his Ph.D. from Tsinghua University in 2021, advised by Prof. Dan Pei in the Netman Group.
Country:United States
Affiliation:Microsoft
Personal website: https://minghua-ma.github.io/
Research interests:AIOps
Contributions
2027
2026
ASE
ESEC/FSE
- Author of An Agentic Framework for Triaging Incidents in Production Cloud Infrastructure within the Industry Papers-track
- Committee Member in Program Committee within the Industry Papers-track
- Author of TSGen: Automated Troubleshooting Guide Generation within the Industry Papers-track
- Author of Aloha: Localizing Batch Failures in Large-scale Cloud Systems via Contrast Analysis and Human-in-the-Loop Agent within the Industry Papers-track
EASE
2025
ASE
ESEC/FSE
- Author of OpsEval: A Comprehensive Benchmark Suite for Evaluating Large Language Models’ Capability in IT Operations Domain within the Industry Papers-track
- Committee Member in Program Committee within the Industry Papers-track
- Committee Member in Program Committee within the Research Papers-track
2024
ASE
- Author of ART: A Unified Unsupervised Framework for Incident Management in Microservice Systems within the Research Papers-track
- Author of End-to-End AutoML for Unsupervised Log Anomaly Detection within the Research Papers-track
- Author of Giving Every Modality a Voice in Microservice Failure Diagnosis via Multimodal Adaptive Optimization within the Research Papers-track
ESEC/FSE
- Author of Automated Root Causing of Cloud Incidents using In-Context Learning with GPT-4 within the Industry Papers-track
- Committee Member in Program Committee within the Industry Papers-track
- Author of MonitorAssistant: Simplifying Cloud Service Monitoring via Large Language Models within the Industry Papers-track
2023
ESEC/FSE
- Author of [Remote] Detection Is Better Than Cure: A Cloud Incidents Perspective within the Industry Papers-track
- Author of TraceDiag: Adaptive, Interpretable, and Efficient Root Cause Analysis on Large-Scale Microservice Systems within the Industry Papers-track
- Author of [Remote] Assess and Summarize: Improve Outage Understanding with Large Language Models within the Industry Papers-track
ICSE
- Author of TraceArk: Towards Actionable Performance Anomaly Alerting for Online Service Systems within the SEIP - Software Engineering in Practice-track
- Author of Aegis: Attribution of Control Plane Change Impact across Layers and Components for Cloud Systems within the SEIP - Software Engineering in Practice-track
- Author of CONAN: Diagnosing Batch Failures for Cloud Systems within the SEIP - Software Engineering in Practice-track