Paolo Notaro

PhD Candidate - Technische Universität München Informatik 10

Lehrstuhl für Rechnerarchitektur & Parallele Systeme (Prof. Schulz)

Email: paolo.notaro[at]

Location: Boltzmannstr. 3 85748, Garching b. München

Room: -

External Links: LinkedIn, GitHub, Google Scholar


  • [2023] Paolo Notaro, Soroush Haeri, Qiao Yu, Jorge Cardoso, and Michael Gerndt, "An Optical Transceiver Reliability Study based on SFP Monitoring and OS-level Metric Data", accepted at the IEEE/ACM International Conference on Cloud, Grid, and Internet Computing (CCGRID '23 - AR 20%, Best Paper Award nominee), Bangalore, India. (link)
  • [2023] Qiao Yu, Wengui Zhang, Soroush Haeri, Paolo Notaro, Jorge Cardoso, Odej Kao, "HiMFP: Hierarchical Intelligent Memory Failure Prediction for Cloud Service Reliability", accepted at the IEEE/IFIP International Conference on Dependable Systems and Network (IEEE IFIP DSN 2023 - AR 20%), Porto, Portugal. (link)
  • [2022] Paolo Notaro, Soroush Haeri, Jorge Cardoso, and Michael Gerndt, "LogRule: Efficient Structured Log Mining for Root Cause Analysis", IEEE Transactions on Network and Service Management (TNSM - IF: 4.76).  (link)
  • [2021] Paolo Notaro, Jorge Cardoso and Michael Gerndt, "A Survey of AIOps Methods for Failure Management", ACM Transactions on Intelligent Systems and Technology (TIST - IF: 2.86). (link)
  • [2020] Paolo Notaro, Jorge Cardoso and Michael Gerndt, "A Systematic Mapping Study in AIOps", Workshop on Artificial Intelligence for IT Operations (AIOPS) 2020, in the 18th International Conference on Service Oriented Computing, ICSOC 2020, Dubai (link)
  • [2019] Paolo Notaro, Magdalini Paschali, Carsten Hopke, David Wittmann and Nassir Navab, "Radar Emitter Classification with Attribute-specific Recurrent Neural Networks", submitted to the 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (link).

Research Interests

  • Artificial Intelligence for IT Operations (AIOps)
  • Online Failure Prediction
  • Root Cause Analysis
  • Time Series Classification
  • Deep Learning
  • Anomaly Detection


  • Seminar "Cloud Computing", WiSe 21/22, WiSe 22/23
  • Seminar "Cloud Computing: Artificial Intelligence for IT Operations (AIOps)", SoSe 21, SoSe 22

Supervised Master Theses:

  • "Cloud System Root Cause Analysis for Sequential Logs using Deep Learning", Ganesh Chandrasekaran, 2021