CHEPFLCivil Engineering Institute (IIC, School of Architecture, Civil and Environmental Engineering ENAC)
Olga Fink
Associate Professor
Leads the Intelligent Maintenance and Operations Systems lab, developing machine learning for condition monitoring, fault diagnosis and predictive maintenance of infrastructure.
Olga Fink's lab at EPFL works on accelerators, chips & architecture and distributed, cloud & edge systems. 18 papers since 2023, 3 at top venues, mostly Data Mining and Knowledge Discovery and ICLR. 10 PhD students. 3 alumni. Applications close .
At a glance
- papers, 2023–2026
- 5/ year
- at T1 venues
- 17%
- PhD students
- 10
- MS students
- 0
- alumni listed
- 3
Papers by yearfrom the lab homepage, 2023–2026
- 18 papers2026 · T1 3 · collecting
- 0 papers2025 · T1 0
- 0 papers2024 · T1 0
- 0 papers2023 · T1 0
T1 venuesOtherVenues: Data Mining and Knowledge Discovery, ICLR, IJCV, ISIT, Nature Communications
Research focuspaper titles that use each topic's words
- Distributed, cloud & edge systems3 papers
- Accelerators, chips & architecture0 papers
- civil0 papers
- fault0 papers
- engineering institute0 papers
Paper list
From the lab homepage.
202618 papers
- Machine Learning: Engineering2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing (opens in a new tab)
- Nature Communications (T1)A physics-informed graph neural network conserving linear and angular momentum for dynamical systems (opens in a new tab)
- IJCV (T1)Adapting Vision-Language Models Without Labels: A Comprehensive Survey (opens in a new tab)
- Reliability Engineering & System SafetyDisentangling slow and fast temporal dynamics in degradation inference with hierarchical differential models (opens in a new tab)
- Reliability Engineering and System SafetyFrom physics to machine learning and back: Part I – Learning with inductive biases in prognostics and health management (PHM) (opens in a new tab)
- Reliability Engineering & System SafetyFrom Physics to Machine Learning and Back: Part II – Learning and Observational Bias in Prognostics and Health Management (PHM) (opens in a new tab)
- Results in EngineeringGraph Neural Network-based Quantification of the Reserve Capacity of Steel Beams (opens in a new tab)
- Machine Learning: EngineeringHeterogeneous Graph Neural Networks for Short-term State Forecasting in Power Systems across Domains and Time Scales: A Hydroelectric Power Plant Case Study (opens in a new tab)
- ENERGY AND BUILDINGSIntegrating Physics-based and Data-driven Approaches for Probabilistic Building Energy Modeling (opens in a new tab)
- Energy ReportsIntegrating the expected future in load forecasts with contextually enhanced transformer models (opens in a new tab)
- ICLR (T1)Loc2: Interpretable Cross-View Localization via Depth-Lifted Local Feature Matching
- Nuclear FusionOverview of the MAST Upgrade physics programme: testing novel concepts at low aspect ratio to inform future devices (opens in a new tab)
Where alumni went3 listed
Apply to EPFLFall 2027
EDIC Computer and Communication Sciences PhD
- Round
- Round 1
- Opens
- —
- GRE
- optional
- English
- TOEFL 100 / IELTS 7.0
- Fee
- none
Confirmed on the program page.
Similar labsnearest by research summary
- SGNUS
Jin Song Dong
- Probabilistic & causal inference
- USUIUC
Charles L. Isbell, Jr.
- Reinforcement learning
- USUCSD
Babak Salimi
- Probabilistic & causal inference
- CHEPFL
Martin Schrimpf
- Neuroscience & brain-computer interfaces
- CNSJTU
Baoliang Lu
- Computational neuroscience & cognition
- CNSJTU
Chaoyi Wu
- Medical imaging & healthcare AI
Your notes
Save this lab to keep notes.
Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.
EPFLcloses