Mulugeta Weldezgina Asres has followed the PhD Programme at the Faculty of Engineering and Science, specialisation in ICT, Artifical Intelligence.
- Trial lecture starts at 10.15
- Public defence starts at 12.15
Title of trial lecture: “Ethical considerations related to AI-based automated decisions in research and in society”
Disputation chair: Morgan Konnestad, Department of ICT, UiA
Assessment committee:
- First opponent: Dr. Mustafa Mustafa, Waymo Research, California
- Second opponent: Associate Professor Therese Sjursen, Høgskolen på Vestlandet (HVL) – had to withdraw during final phase of assessment.
- Acting second opponent: Researcher James Richard Catmore, UiO.
- Chair of assessment committee: Professor Lei Jiao, UiA
Supervisors in the doctoral work:
- Main supervisor: Christian Omlin, Department of ICT, UiA
Summary of thesis
Machine Learning (ML) tools have gained immense popularity due to the proliferation of sensor data for monitoring, prognostic, and diagnostic applications in various industrial domains. The growing system complexity and monitoring data volumes of the Large Hadron Collider (LHC) at CERN accentuates the need for automation through advanced ML tools. Detection, identification, and resolution of anomalies are essential to generate more physics collision data of the highest quality. Developing ML tools for complex systems often involves expensive data curation and modeling efforts; it requires adequate, cleaned, and annotated data sets, and addresses the challenges of heterogeneity and curse-of-dimensionality of large data sets.
The Compact Muon Solenoid (CMS) experiment - one of the large general-purpose
colliders at the LHC - has dedicated substantial monitoring efforts for detector systems
and particle data quality; the control and safety systems (DCS/DSS) actively monitor
safety-critical problems, and the data quality monitoring (DQM) system mitigates data
loss by identifying and diagnosing physics data problems. The existing monitoring
systems need to incorporate a wide range of monitoring variables and adapt to the
evolving conditions of the detectors. This dissertation focuses on the development of
unsupervised anomaly detection (AD), anomaly prediction (AP), and root-cause analysis (RCA) on multivariate time series data sets. We have developed deep learning models for frontend electronics of the Hadron Calorimeter (HCAL) of the CMS detector using diagnostic sensors and high-dimensional particle acquisition channel-monitoring data sets. We have employed subsystem-granularity modeling using a divide-and-conquer approach to monitor the complex HCAL systems with thousands of sensors. Our monitoring tools have detected and identified previously unknown and hard-to-monitor anomalies, and extended the monitoring, diagnostics, and prognostics automation of the HCAL. The developed tools are deployed at CERN and currently providing essential real-time and offline anomaly monitoring and diagnostics on the frontend electronics of the HCAL and the online DQM system.
Our scientific contribution in tackling the challenges for complex system monitoring
includes: 1) enhancing multivariate sensor AD, 2) a promising AP approach, 3) context-aware high-dimensional spatio-temporal AD, 4) transfer learning on multi-network deep
learning models, 5) lightweight interconnection and divergence discovery for multi-systems with multivariate sensors, and 6) enhancing computational efficiency of
anomalies causality discovery on binary anomaly data.
What to do as an online audience member
The disputation is open to the public. To follow the trial lecture and the public defence online, register on Zoom.
We ask online audience members to join no earlier than 10 minutes in advance. After these times, you can leave and rejoin the meeting at any time.
Opponent ex auditorio:
Deadline for the public to pose questions is during the break between the two opponents. Questions ex auditorio can be submitted to Emma Horneman.
Kontaktperson
- kristine.reinfjord@uia.no
- Phone
- +47 37 23 30 14